MLS-C01 Modeling — All Questions With Answers

Question 1easymultiple choice

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A data scientist is training a binary classification model using Amazon SageMaker. The dataset is highly imbalanced (99% negative class, 1% positive class). The model currently achieves 99% accuracy but fails to detect most positive cases. Which metric should the data scientist primarily use to evaluate model performance?

Question 2hardmultiple choice

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A machine learning engineer is training a neural network on Amazon SageMaker using a custom Docker container. The training job fails with an error: 'CUDA out of memory.' The training instance is an ml.p3.2xlarge with 16 GB GPU memory. The model and data fit into memory when using batch size 32, but the engineer wants to maximize GPU utilization. Which approach should the engineer use to fix the out-of-memory error while maintaining efficient training?

Question 3hardmulti select

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A company is using Amazon SageMaker to tune hyperparameters for a gradient boosting model. The objective is to minimize root mean squared error (RMSE). The data scientist wants to explore the hyperparameter space efficiently. Which THREE hyperparameter tuning strategies should the data scientist consider? (Choose 3.)

Question 4mediummultiple choice

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A data scientist is training a binary classifier on an imbalanced dataset where the positive class represents 1% of the data. The model currently achieves 99% accuracy but a recall of only 10% on the positive class. Which metric combination should the data scientist prioritize to evaluate model improvements?

Question 5hardmultiple choice

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A data scientist is building a multi-class classification model with 10 classes. The dataset has 100,000 samples. After training a random forest with 100 trees, the model achieves 85% accuracy on the test set. However, the data scientist notices that for one rare class (1% of data), recall is only 5%. Which technique is MOST likely to improve recall for the rare class without significantly reducing overall accuracy?

Question 6mediummulti select

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Which TWO metrics are MOST appropriate for evaluating a regression model that predicts house prices, where the business is most sensitive to large errors?

Question 7hardmultiple choice

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A data scientist runs a SageMaker training job that fails with the above error. The S3 bucket and object exist, and the IAM role has s3:GetObject permission. What is the MOST likely cause?

Exhibit

Refer to the exhibit.

```
Training job status: Failed
Error: ClientError: Data download failed.
The downloaded file size (0 bytes) does not match expected size (1024 bytes).
Check that the S3 object exists and is readable.
```

Question 8easymultiple choice

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A data scientist is trying to run a SageMaker training job that writes output to an S3 bucket 'my-bucket'. The IAM policy is shown. The training job fails with an AccessDenied error when trying to write to S3. What is the reason?

Exhibit

Refer to the exhibit.

```
{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Action": [
        "sagemaker:CreateTrainingJob",
        "sagemaker:DescribeTrainingJob",
        "sagemaker:StopTrainingJob"
      ],
      "Resource": "*"
    },
    {
      "Effect": "Allow",
      "Action": [
        "s3:GetObject",
        "s3:PutObject"
      ],
      "Resource": "arn:aws:s3:::my-bucket/*"
    }
  ]
}
```

Question 9mediummultiple choice

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A data scientist is training a binary classifier to predict customer churn. The dataset has 10,000 samples, with 500 churners (positive class). The scientist trains a logistic regression model and obtains an F1-score of 0.6. To improve the F1-score, which approach is MOST likely to be effective?

Question 10hardmultiple choice

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A company is deploying a real-time fraud detection system using a gradient boosting model on AWS SageMaker. The model uses 200 features and is trained on 50 GB of data. The inference latency requirement is under 10 ms per request. During load testing, the endpoint shows average latency of 15 ms. Which change is MOST likely to reduce latency below 10 ms?

Question 11mediummultiple choice

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A company is building a recommendation system for an e-commerce platform. They have user-item interaction data (clicks, purchases) and want to use matrix factorization. They plan to use Amazon SageMaker to train the model. Which dataset format is MOST appropriate for the built-in Factorization Machines algorithm?

Question 12easymultiple choice

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A data scientist is training a binary classification model on a dataset with a severe class imbalance (95% negative, 5% positive). The model achieves 95% accuracy but only correctly identifies 10% of the positive class. Which metric should the data scientist use to evaluate model performance?

Question 13hardmultiple choice

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A research team is developing a deep learning model to classify medical images into 10 disease categories. They have a dataset of 50,000 labeled images, but the class distribution is highly imbalanced: the most common class has 20,000 images, while the rarest class has only 200 images. To address this, they apply data augmentation (random rotations, flips, and brightness adjustments) to the minority classes until each class has 20,000 images. They then train a convolutional neural network (CNN) from scratch using cross-entropy loss. The model achieves 95% overall accuracy but only 30% recall on the rarest class. Which change is MOST likely to improve recall on the rarest class without significantly reducing overall accuracy?

Question 14mediummultiple choice

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A financial services company is building a fraud detection model using a large dataset of credit card transactions. The dataset contains 10 million rows with 50 features, including transaction amount, merchant category, time of day, and customer historical features. The label is binary: fraudulent (1% of data) or legitimate. The company wants to deploy a real-time inference endpoint using Amazon SageMaker that can score transactions with sub-100ms latency. The current model is a gradient boosting model (XGBoost) trained on a sample of 1 million rows due to memory constraints. The model achieves 0.95 AUC on a held-out test set but the fraud recall (sensitivity) is only 0.4, which is unacceptable because the cost of missing a fraud is high. The data science team has access to a larger compute instance (ml.m5.24xlarge) for training. Which course of action is most likely to improve fraud recall while maintaining latency requirements?

Question 15mediumdrag order

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Drag and drop the steps to use Amazon SageMaker Debugger to debug a training job in the correct order.

Drag or tap steps into the slots.

Steps

Order

1Step 1

2Step 2

3Step 3

4Step 4

5Step 5

Question 16mediummatching

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Match each AWS AI service to its capability.

Drag a concept onto its matching description — or click a concept then click the description.

Concepts

Matches

Natural language processing

Language translation

Text-to-speech

Speech-to-text

Conversational chatbots

Question 17easymultiple choice

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A data scientist is training a binary classification model on a highly imbalanced dataset where the positive class represents only 1% of the data. Which metric should be used to evaluate model performance during training to ensure the model is learning to detect the positive class?

Question 18mediummultiple choice

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A company uses Amazon SageMaker to train a classification model. The training job fails with an error indicating that the algorithm requires a GPU but the instance type does not have one. The scientist used the built-in XGBoost algorithm. What should the scientist do to resolve the issue?

Question 19hardmultiple choice

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A company uses Amazon SageMaker to train a model using the built-in Linear Learner algorithm. The training data contains missing values in some features. What is the best practice for handling missing values with this algorithm?

Question 20mediummulti select

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Which TWO metrics are appropriate for evaluating a binary classification model when the cost of false negatives is high?

Question 21easymulti select

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Which TWO actions are best practices for tuning hyperparameters using Amazon SageMaker Automatic Model Tuning?

Question 22hardmultiple choice

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Refer to the exhibit. A data scientist runs the above AWS CLI command to create a SageMaker training job using the built-in Linear Learner algorithm. The training job fails with an error. What is the most likely cause?

Exhibit

Refer to the exhibit.

```
aws sagemaker create-training-job \
    --training-job-name my-training-job \
    --algorithm-specification TrainingImage=382416733822.dkr.ecr.us-east-1.amazonaws.com/linear-learner:1,TrainingInputMode=File \
    --role-arn arn:aws:iam::123456789012:role/SageMakerRole \
    --input-data-config [{"ChannelName":"train","DataSource":{"S3DataSource":{"S3DataType":"AugmentedManifestFile","S3Uri":"s3://bucket/train.manifest","S3DataDistributionType":"FullyReplicated"}},"ContentType":"application/x-recordio"}] \
    --output-data-config S3OutputPath=s3://bucket/output \
    --resource-config InstanceType=ml.m5.large,InstanceCount=1,VolumeSizeInGB=10 \
    --stopping-condition MaxRuntimeInSeconds=3600

Question 23mediummultiple choice

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Refer to the exhibit. A data scientist is configuring SageMaker Model Monitor for data quality checks. The configuration above is used. What is the purpose of the `ProbabilityThresholdAttribute` set to "0.5"?

Exhibit

Refer to the exhibit.

```
{
  "DataQualityCheckConfig": {
    "DatasetFormat": {
      "Csv": {
        "Header": true
      }
    },
    "KmsKeyId": "",
    "S3OutputPath": "s3://bucket/datachecks/",
    "LocalPath": "/opt/ml/processing/output"
  },
  "DataQualityJobInput": {
    "EndpointInput": {
      "EndpointName": "my-endpoint",
      "LocalPath": "/opt/ml/processing/input",
      "S3InputMode": "File",
      "S3DataDistributionType": "FullyReplicated",
      "InferenceAttribute": "predicted_label",
      "ProbabilityAttribute": "probability",
      "ProbabilityThresholdAttribute": "0.5",
      "StartTimeOffset": "-PT1H",
      "EndTimeOffset": "-PT0H"
    }
  }
}
```

Question 24hardmultiple choice

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A company is building a recommendation system using collaborative filtering on Amazon SageMaker. The dataset contains user-item interactions with a long-tail distribution: a few items have millions of interactions, while most items have very few. The model currently uses matrix factorization with ALS. The recall@20 metric is low for niche items. Which modification would most likely improve recall for long-tail items?

Question 25mediummultiple choice

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A company is using Amazon SageMaker to deploy a model that predicts customer churn. The model was trained using a linear learner algorithm. During inference, the endpoint returns predictions that are always 0.5 (the probability of churn). What is the most likely cause?

Question 26hardmultiple choice

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A data scientist is using Amazon SageMaker to train a deep learning model for image classification. The training job is using a single GPU instance and is taking too long. The scientist wants to reduce training time without sacrificing model accuracy. The dataset contains 100,000 images of size 256x256. Which change would most effectively reduce training time?

Question 27hardmulti select

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A company uses Amazon SageMaker to build a text classification model using a pre-trained BERT model. The dataset contains 10,000 labeled documents. The model is overfitting: training accuracy is 99%, validation accuracy is 85%. Which TWO of the following are most likely to help reduce overfitting? (Choose TWO.)

Question 28easymultiple choice

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A company is using Amazon SageMaker to train a deep learning model. The training job is taking a long time, and the data scientist wants to reduce training time without sacrificing accuracy. Which technique should they use?

Question 29mediummultiple choice

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A company is building a multiclass classification model using Amazon SageMaker. The dataset has 100 classes and is highly imbalanced. The model currently achieves high accuracy on the majority classes but poor performance on minority classes. Which technique should the data scientist use to improve minority class performance?

Question 30hardmultiple choice

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A machine learning engineer is training a model using Amazon SageMaker. The training data is stored in S3 and is 10 TB. The engineer wants to use Pipe input mode to stream data from S3. Which algorithms support Pipe mode? (Select all that apply)

Question 31mediummulti select

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A data scientist is training a model using Amazon SageMaker. The training job is running on GPU instances, but the GPU utilization is low. Which TWO actions could improve GPU utilization?

Question 32easymulti select

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A data scientist is performing feature engineering for a machine learning model. The dataset contains categorical features with high cardinality. Which THREE techniques are appropriate for encoding high-cardinality categorical features?

Question 33hardmultiple choice

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Refer to the exhibit. A training job failed with the error shown. What is the most likely cause?

Network Topology

Question 34mediummultiple choice

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Refer to the exhibit. A data scientist is trying to create a SageMaker training job but receives an access denied error. The IAM policy shown is attached to the user. What is the likely issue?

Exhibit

Refer to the exhibit.

```
{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Effect": "Allow",
            "Action": [
                "s3:GetObject",
                "s3:PutObject"
            ],
            "Resource": "arn:aws:s3:::my-bucket/*"
        },
        {
            "Effect": "Allow",
            "Action": [
                "sagemaker:CreateTrainingJob",
                "sagemaker:CreateModel",
                "sagemaker:CreateEndpointConfig",
                "sagemaker:CreateEndpoint"
            ],
            "Resource": "*"
        }
    ]
}
```

Question 35easymultiple choice

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Refer to the exhibit. A data scientist checks the status of a SageMaker endpoint and sees the output above. What does this indicate?

Network Topology

Question 36easymultiple choice

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A machine learning team is building a model to predict customer churn. They have a dataset with 10,000 samples and 50 features, including categorical variables with high cardinality (e.g., ZIP code). Which feature engineering technique is most appropriate to reduce dimensionality while preserving predictive information?

Question 37hardmultiple choice

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A machine learning engineer is using Amazon SageMaker to deploy a model for real-time inference. The model is a large ensemble that requires 4 GB of memory and has a latency requirement of 100 ms. Which instance type and deployment configuration should the engineer choose to optimize cost while meeting requirements?

Question 38hardmultiple choice

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A company uses Amazon SageMaker to train a model for fraud detection. The dataset has 1 million samples with 200 features. The data is highly imbalanced (0.1% fraud). The team wants to use a random forest model. Which technique should they use to handle the class imbalance during training?

Question 39hardmulti select

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A machine learning engineer is evaluating a classification model that predicts whether a transaction is fraudulent. The model outputs a probability score. The cost of a false negative (missed fraud) is 10 times higher than the cost of a false positive (false alarm). Which TWO evaluation metrics should the engineer use to tune the model? (Choose TWO.)

Question 40hardmulti select

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A company is deploying a machine learning model using Amazon SageMaker. The model requires GPUs for inference. Which THREE configurations can the company use to meet this requirement? (Choose THREE.)

Question 41easymultiple choice

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A data scientist wants to build a binary classifier to predict customer churn. The dataset has 10,000 records with 500 churners (5%). Which technique should the data scientist use to address class imbalance?

Question 42hardmultiple choice

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A data scientist is tuning a gradient boosting model using SageMaker automatic model tuning. The hyperparameter 'num_round' ranges from 50 to 500. The tuning job uses 'ObjectiveMetric' = 'validation:auc'. After 50 training jobs, the best objective value is 0.95. The data scientist suspects overfitting. What should the data scientist do?

Question 43easymultiple choice

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A company uses Amazon SageMaker to train a linear regression model. The training data includes a feature 'age' with values ranging from 0 to 100. The model's loss is not converging. What is the MOST likely cause?

Question 44mediummulti select

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A data scientist is building a binary classifier using logistic regression. The dataset has 10 features and 100,000 observations. The model achieves 99% accuracy on the test set, but the precision is 50% and recall is 90%. Which TWO actions should the data scientist take to improve model performance? (Choose 2.)

Question 45hardmulti select

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A company is deploying a machine learning model for real-time fraud detection. The model must have extremely low latency (<10 ms) and high throughput. Which THREE design choices meet these requirements? (Choose 3.)

Question 46easymulti select

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A data scientist is training a text classification model using Amazon SageMaker's built-in BlazingText algorithm. The dataset contains 1 million documents. Which TWO hyperparameters are most important to tune for improving model accuracy?

Question 47easymultiple choice

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A data scientist is training a binary classification model and wants to evaluate its performance using a metric that is robust to class imbalance. Which metric should be used?

Question 48easymultiple choice

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A data scientist is using Amazon SageMaker to train a model and wants to automatically stop the training job if the loss does not improve for a certain number of epochs. Which SageMaker feature can be used for this purpose?

Question 49hardmultiple choice

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A machine learning team is deploying a real-time inference endpoint for a recommendation model using Amazon SageMaker. The model takes a long time to load (several minutes) due to its size (5 GB). Which deployment strategy minimizes the cold start latency?

Question 50mediummultiple choice

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A data scientist is training a neural network for time series forecasting. The training loss decreases initially but then starts to increase after 20 epochs. Which action should the scientist take to address this?

Question 51mediummulti select

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Which TWO of the following are valid techniques for handling missing values in a dataset for machine learning?

Question 52mediummultiple choice

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A data scientist is trying to create a SageMaker training job but receives an access denied error. The IAM policy attached to the role is shown in the exhibit. What is the most likely cause of the error?

Exhibit

Refer to the exhibit.

```
{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Action": "sagemaker:CreateTrainingJob",
      "Resource": "*"
    },
    {
      "Effect": "Allow",
      "Action": "s3:GetObject",
      "Resource": "arn:aws:s3:::my-bucket/training/*"
    }
  ]
}
```

Question 53hardmultiple choice

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A data scientist ran an XGBoost training job in SageMaker and it failed with the error shown in the exhibit. Which hyperparameter change is most likely to resolve the numeric overflow?

Exhibit

Refer to the exhibit.

```
{
  "name": "my-training-job",
  "hyperParameters": {
    "max_depth": "10",
    "eta": "0.1",
    "subsample": "0.8",
    "colsample_bytree": "0.8",
    "num_round": "100"
  },
  "trainingJobStatus": "Failed",
  "failureReason": "AlgorithmError: /opt/program/src/sagemaker_xgboost_container/algorithm_mode/... 
    Message: The pipeline has been stopped. There was a numeric overflow in the tree."
}

Question 54mediummultiple choice

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A data science team is training a binary classification model using Amazon SageMaker. The dataset is highly imbalanced (95% negative class, 5% positive class). The team wants to maximize the F1 score. Which built-in SageMaker algorithm is most appropriate?

Question 55easymultiple choice

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A company is using Amazon SageMaker to deploy a model for real-time inference. The model receives requests with varying payload sizes. The company observes occasional latency spikes. Which feature can help mitigate this?

Question 56easymultiple choice

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A company is using Amazon SageMaker to deploy a model for real-time inference. The model is updated frequently. Which deployment strategy allows for zero-downtime updates and easy rollback?

Question 57mediummultiple choice

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A data scientist is using Amazon SageMaker to train a model. The training dataset is stored in S3 as CSV files. The scientist wants to use the SageMaker built-in Linear Learner algorithm. Which input mode should be used for optimal performance?

Question 58easymulti select

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Which TWO of the following are appropriate use cases for Amazon SageMaker built-in algorithms?

Question 59mediummultiple choice

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A data scientist is training a binary classification model on an imbalanced dataset where the positive class represents 1% of the data. The model needs to maximize recall while keeping precision above 0.7. Which sampling strategy should the data scientist use?

Question 60hardmultiple choice

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A data scientist is training a gradient boosting model on a large dataset (100 GB) stored in Amazon S3. The training job uses a SageMaker built-in XGBoost algorithm with a single ml.p3.2xlarge instance. The job fails with a memory error. Which solution should the data scientist adopt to resolve the memory issue?

Question 61easymultiple choice

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A data scientist wants to evaluate the performance of a multiclass classification model. The model outputs probabilities for 10 classes. Which metric is most appropriate for evaluating the model's ranking performance across all classes?

Question 62hardmultiple choice

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A company is building a recommendation system using Amazon SageMaker Factorization Machines. The dataset includes user IDs, item IDs, and implicit feedback (clicks). The data is sparse with millions of users and items. The model needs to capture interactions between users and items. Which hyperparameter tuning strategy should be used to improve model performance?

Question 63mediummultiple choice

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A data scientist is building a fraud detection model using a highly imbalanced dataset. The model uses a random forest classifier. The recall for the minority class is 0.6, and precision is 0.9. The business requires recall above 0.8. Which action should the data scientist take to improve recall?

Question 64mediummulti select

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A data scientist is training a gradient boosting model using SageMaker's built-in XGBoost algorithm. The dataset has missing values in several features. Which TWO actions should the data scientist take to handle missing values effectively? (Choose two.)

Question 65mediummultiple choice

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A company is building a binary classifier to detect fraudulent transactions. The dataset is highly imbalanced (99% legitimate, 1% fraudulent). Which metric is most appropriate for evaluating the model?

Question 66hardmultiple choice

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A machine learning team trains a deep learning model on SageMaker. The training job uses a single ml.p3.2xlarge instance and takes 12 hours. The team needs to reduce training time without changing the algorithm. Which approach is most effective?

Question 67easymultiple choice

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A healthcare company needs to predict patient readmission risk using clinical notes. Which AWS service can be used to preprocess the text data into numerical features for a machine learning model?

Question 68hardmultiple choice

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A data scientist uses SageMaker Autopilot to automatically build a binary classification model. The dataset has 50 features and 100,000 rows. After the experiment, Autopilot provides multiple candidate models. Which candidate should the data scientist select to minimize inference latency for real-time predictions?

Question 69mediummultiple choice

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A company uses SageMaker to train a time-series forecasting model using Amazon Forecast. The dataset contains historical sales data for 10,000 products over 2 years. Which data format is required for the target time series?

Question 70mediummultiple choice

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Refer to the exhibit. An IAM policy is attached to a SageMaker execution role. A data scientist tries to create a training job that reads training data from s3://my-bucket/confidential/data.csv. What will happen?

Exhibit

Refer to the exhibit.
```
{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Action": [
        "sagemaker:CreateTrainingJob",
        "sagemaker:DescribeTrainingJob"
      ],
      "Resource": "*"
    },
    {
      "Effect": "Allow",
      "Action": [
        "s3:GetObject"
      ],
      "Resource": "arn:aws:s3:::my-bucket/*"
    },
    {
      "Effect": "Deny",
      "Action": [
        "s3:GetObject"
      ],
      "Resource": "arn:aws:s3:::my-bucket/confidential/*"
    }
  ]
}
```

Question 71hardmultiple choice

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Refer to the exhibit. A SageMaker training job failed with the error shown. What is the most likely cause of this error?

Network Topology

Question 72mediummultiple choice

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Refer to the exhibit. A data scientist creates a SageMaker model using the configuration above. When deploying the model to an endpoint, the endpoint status remains 'Creating' for a long time and then fails. What is the most likely cause?

Exhibit

Refer to the exhibit.
```
{
  "ModelName": "my-model",
  "PrimaryContainer": {
    "Image": "382416733822.dkr.ecr.us-east-1.amazonaws.com/linear-learner:1",
    "ModelDataUrl": "s3://my-bucket/model.tar.gz",
    "Environment": {
      "SAGEMAKER_CONTAINER_LOG_LEVEL": "20",
      "SAGEMAKER_REGION": "us-east-1"
    }
  },
  "ExecutionRoleArn": "arn:aws:iam::123456789012:role/SageMakerRole"
}
```

Question 73mediummultiple choice

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A team is using Amazon SageMaker to train a deep learning model. The training job is taking too long, and they want to reduce training time without significant accuracy loss. They have already tried increasing the number of instances. Which technique should they consider next?

Question 74hardmultiple choice

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A machine learning engineer is deploying a model that predicts loan defaults. The model uses features like income, credit score, and debt-to-income ratio. After deployment, the model's performance degrades over time. Which concept best describes this phenomenon?

Question 75mediummultiple choice

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A company uses Amazon SageMaker to train a model for detecting fraudulent transactions. The dataset is highly imbalanced (99.9% legitimate, 0.1% fraudulent). Which approach is most effective to address this imbalance?

Question 76hardmultiple choice

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A data scientist is building a regression model to predict house prices. The dataset contains features like number of bedrooms, square footage, and location. After training, the model has high variance. Which technique should the data scientist use to reduce variance without significantly increasing bias?

Question 77easymultiple choice

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A company wants to build a model to detect fraudulent transactions. The dataset has a highly imbalanced class distribution. Which technique should be used during training to handle class imbalance?

Question 78mediummultiple choice

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A data scientist is working with a dataset containing categorical features with high cardinality. The scientist wants to use a tree-based model. Which encoding method should be used?

Question 79easymulti select

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Which TWO metrics are appropriate for evaluating a binary classification model when the cost of false negatives is high? (Choose 2)

Question 80hardmulti select

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Which TWO SageMaker features can be used to perform hyperparameter optimization? (Choose 2)

Question 81mediummultiple choice

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A team is using Amazon SageMaker to train a model and wants to automatically stop training when the model stops improving to save costs. Which SageMaker feature should they use?

Question 82mediummulti select

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A data scientist is training a gradient boosting model using SageMaker. The model is overfitting to the training data. Which TWO actions can help reduce overfitting? (Choose 2)

Question 83hardmultiple choice

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A company is building a sentiment analysis model using Amazon SageMaker BlazingText. The training data consists of 100,000 product reviews. The data scientist wants to use the Word2Vec algorithm to generate word embeddings. Which configuration is required to use the continuous bag-of-words (CBOW) architecture?

Question 84easymultiple choice

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A data scientist is using Amazon SageMaker to train a model. The training job is taking longer than expected. The scientist wants to reduce training time without changing the algorithm or the hardware. Which action is most likely to help?

Question 85mediummultiple choice

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A company is using Amazon SageMaker to deploy a model for real-time inference. The model has a latency requirement of less than 100 milliseconds. During testing, the latency is around 150 milliseconds. Which action can most likely reduce the latency to meet the requirement?

Question 86hardmulti select

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A data scientist is using Amazon SageMaker to train a random forest model for a binary classification task. The dataset has 50 features and 10,000 samples. The model achieves high training accuracy but poor test accuracy. Which TWO actions should the scientist take to improve generalization?

Question 87mediummulti select

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A company is using Amazon SageMaker to train an XGBoost model. The training data contains missing values. Which TWO methods can XGBoost handle missing values internally?

Question 88hardmultiple choice

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Refer to the exhibit. An IAM policy is attached to a SageMaker notebook instance role. A data scientist is trying to train a model using the SageMaker built-in XGBoost algorithm with training data in 'my-bucket/training-data/' and expects output in 'my-bucket/output/'. The training job fails with an access denied error. What is the most likely missing permission?

Exhibit

Refer to the exhibit.
```
{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Action": [
        "sagemaker:CreateTrainingJob",
        "sagemaker:CreateModel",
        "sagemaker:CreateEndpointConfig",
        "sagemaker:CreateEndpoint"
      ],
      "Resource": "*"
    },
    {
      "Effect": "Allow",
      "Action": "s3:GetObject",
      "Resource": "arn:aws:s3:::my-bucket/training-data/*"
    },
    {
      "Effect": "Allow",
      "Action": "s3:PutObject",
      "Resource": "arn:aws:s3:::my-bucket/output/*"
    }
  ]
}
```

Question 89mediummultiple choice

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Refer to the exhibit. A data scientist ran a SageMaker training job and reviewed the logs. The training completed quickly, but the model performance is very poor. What is the most likely cause?

Exhibit

Refer to the exhibit.
```
2019-11-01 10:23:45 Starting - Preparing the instances for training
2019-11-01 10:24:01 Downloading - Downloading input data
2019-11-01 10:24:15 Training - Training image download completed. Training in progress.
2019-11-01 10:25:30 Training - Training completed.
2019-11-01 10:25:31 Uploading - Uploading generated model artifacts
2019-11-01 10:25:35 Completed - Training job completed
```

Question 90mediummultiple choice

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Refer to the exhibit. A data scientist is using Amazon SageMaker Ground Truth to label a dataset. The output manifest file references S3 objects with metadata. The scientist notices that a training job using the labeled data yields poor accuracy. What is the most likely issue?

Network Topology

Question 91mediummultiple choice

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A machine learning team is using SageMaker to train a deep learning model. The training job is failing due to insufficient GPU memory. Which approach should the team take to resolve this issue without changing the model architecture?

Question 92easymultiple choice

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A company is building a model to classify customer reviews as positive or negative. The dataset has 10,000 positive and 100 negative reviews. Which metric is most appropriate for evaluating model performance?

Question 93mediummulti select

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Which TWO actions can help reduce overfitting in a neural network? (Choose 2.)

Question 94mediummulti select

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Which THREE evaluation metrics are appropriate for a multi-class classification problem? (Choose 3.)

Question 95hardmultiple choice

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A team is deploying a real-time inference endpoint using Amazon SageMaker. The model is a large ensemble of 10 deep learning models, each 500 MB. The inference latency requirement is under 200 ms. Currently, the endpoint using a single ml.p3.2xlarge instance takes 1.5 seconds per request. Which approach is MOST likely to meet the latency requirement?

Question 96easymultiple choice

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A machine learning engineer is using Amazon SageMaker to train a model. The training job fails with an out-of-memory error. The training data size is 10 GB and the instance is ml.m5.xlarge (16 GB memory). Which change is MOST likely to resolve the issue without increasing cost?

Question 97hardmultiple choice

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A company is deploying a machine learning model for real-time fraud detection. The model must have low latency (under 100 ms) and high throughput. The model is an ensemble of 5 gradient boosted trees (XGBoost), each 200 MB. Which deployment strategy is MOST suitable?

Question 98hardmultiple choice

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A financial services company uses Amazon SageMaker to train a model for credit risk prediction. The dataset contains 500 features and 1 million records. The target variable is binary with 20% default rate. The data scientist uses a gradient boosting algorithm (XGBoost) with default hyperparameters. After training, the model achieves 95% accuracy, but the precision for the default class is only 30%, and recall is 15%. The business requires at least 50% recall and 40% precision for the default class. The data scientist tries to adjust the decision threshold, but this does not simultaneously meet both targets. The scientist suspects that the model is not learning the default patterns well. The company also has a large dataset of unlabeled transactions that could be used. Which action should the data scientist take to improve the model?

Question 99mediummultiple choice

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A healthcare company is building a model to predict patient readmission within 30 days of discharge. The dataset includes 10,000 patient records with 200 features, including lab results, demographics, and historical admissions. The target variable is highly imbalanced: only 8% of patients are readmitted. The data scientist splits the data into 80% training and 20% test sets, ensuring the same proportion of readmissions in each. The scientist trains a logistic regression model and a random forest model. The logistic regression achieves 92% accuracy but recall of 10% for the readmitted class. The random forest achieves 90% accuracy but recall of 25%. The business requirement is to achieve at least 60% recall for readmissions while maintaining reasonable precision. The scientist also has access to a large collection of unlabeled patient records from other hospitals. Which strategy should the data scientist use to meet the business requirement?

Question 100easymultiple choice

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A data scientist is training a binary classification model on a dataset with 10,000 features. The model overfits severely. Which technique is MOST appropriate to reduce overfitting?

Question 101mediummultiple choice

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A team is training a deep learning model on Amazon SageMaker. The training job is slow because the data is stored in S3 as many small files. Which approach is MOST effective to improve training throughput?

Question 102hardmultiple choice

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A data scientist is using SageMaker to train a custom TensorFlow model. The training script reads data from S3 using TensorFlow's tf.data API. The training is bottlenecked by I/O. Which strategy would MOST effectively improve data throughput?

Question 103hardmulti select

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A machine learning engineer is using SageMaker's built-in XGBoost algorithm for a multi-class classification problem. The training job completes but the model accuracy is low. Which THREE hyperparameters should the engineer tune to improve performance?

Question 104hardmultiple choice

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A company uses Amazon SageMaker to train a deep learning model for image classification. The training dataset consists of 500,000 images, each 256x256 pixels, stored in S3. The team uses a single ml.p3.2xlarge instance for training. The training time is unacceptably long (over 48 hours). The team wants to reduce training time without sacrificing model accuracy. They have already optimized the data pipeline by using SageMaker Pipe mode and sharding the S3 dataset. The model is a ResNet-50 implemented in TensorFlow. The team is considering the following options: A) Switch to a ml.p3.16xlarge instance which has 8 GPUs and more memory. B) Implement distributed data parallelism using Horovod across multiple instances. C) Use SageMaker's built-in Hyperparameter Tuning to find optimal hyperparameters. D) Reduce the image resolution to 128x128 to speed up training. Which option will MOST effectively reduce training time while maintaining accuracy?

Question 105easymultiple choice

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A company has deployed a real-time inference endpoint using SageMaker for a fraud detection model. The model uses a Random Forest classifier. The endpoint receives predictions but the latency is too high. The metric shows p99 latency of 500ms, but the requirement is under 200ms. The team has already optimized the instance type to the maximum allowed by their budget. The data scientist suggests: A) Reducing the number of trees in the Random Forest model. B) Switching to a linear model like Logistic Regression. C) Enabling SageMaker's batch transform instead of real-time endpoint. D) Adding more instances to the endpoint behind a load balancer. Which option will MOST effectively reduce latency while maintaining acceptable accuracy?

Question 106easymultiple choice

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A company is building a binary classifier to predict customer churn. The dataset has 10,000 samples with 500 churners (5% positive class). After training a logistic regression model, the precision is 0.8 and recall is 0.2. Which metric should the data scientist focus on to improve the model's ability to identify churners while minimizing false positives?

Question 107mediummultiple choice

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A data scientist is training a deep learning model on Amazon SageMaker using a custom Docker container. The training job fails with an error 'OutOfMemoryError: CUDA out of memory'. The instance type is ml.p3.2xlarge (8 GB GPU memory). The model has 50 million parameters. What is the most likely cause and solution?

Question 108hardmultiple choice

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A team deployed a SageMaker endpoint for real-time inference using a PyTorch model. After monitoring, they notice that the latency is highly variable, with p99 latency 10x the p50 latency. The endpoint uses a single ml.c5.2xlarge instance with auto-scaling based on average CPU utilization. Which change is most likely to reduce latency variability?

Question 109mediummultiple choice

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A company is fine-tuning a BERT model on Amazon SageMaker for a text classification task. The training script uses PyTorch and Hugging Face Transformers. The training job completes successfully, but the final model accuracy is low. The dataset has 10,000 labeled samples. What is the most likely cause and solution?

Question 110easymultiple choice

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A machine learning team is using SageMaker to train a model with the built-in Linear Learner algorithm. The dataset has 1 million rows and 20 features. The training completes, but the model's mean squared error (MSE) is high. Which parameter adjustment is most likely to reduce MSE?

Question 111hardmultiple choice

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A company is using Amazon SageMaker to host a model for real-time inference. The model is a large ensemble of 10 XGBoost models, each 2 GB. The endpoint uses a single ml.c5.18xlarge instance. The inference latency is high (average 2 seconds). Which change would most effectively reduce latency?

Question 112mediummultiple choice

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A data scientist has this IAM policy attached to their role. When trying to create a SageMaker endpoint using the AWS CLI, they get an 'AccessDenied' error. What is the most likely reason?

Exhibit

Refer to the exhibit.

```
{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Action": "sagemaker:CreateTrainingJob",
      "Resource": "*"
    },
    {
      "Effect": "Allow",
      "Action": "sagemaker:CreateModel",
      "Resource": "*"
    },
    {
      "Effect": "Allow",
      "Action": "sagemaker:CreateEndpointConfig",
      "Resource": "*"
    },
    {
      "Effect": "Allow",
      "Action": "sagemaker:CreateEndpoint",
      "Resource": "*"
    }
  ]
}
```

Question 113hardmultiple choice

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A financial services company is building a fraud detection model using Amazon SageMaker. The dataset has 10 million transactions, with 0.1% fraudulent. They train an XGBoost model with default hyperparameters. The model achieves 99.9% accuracy on the test set, but only catches 10% of actual fraud cases. The company wants to maximize the number of fraud cases caught while keeping the false positive rate below 5%. The data scientist has already tried adjusting the class weights and threshold, but the recall is still low. What should the data scientist do next?

Question 114mediummultiple choice

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A company is training a deep learning model on Amazon SageMaker using a large dataset stored in S3. The training job is failing with an error indicating insufficient memory. The model architecture and hyperparameters are fixed. Which change is MOST likely to resolve the issue without modifying the model code?

Question 115hardmultiple choice

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A data scientist is using Amazon SageMaker to train a gradient boosting model on a dataset with categorical features. The dataset contains a column 'UserID' with over 1 million unique values. The training is taking very long and the model size is large. Which technique would MOST effectively reduce training time and model size while maintaining accuracy?

Question 116easymultiple choice

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A machine learning engineer is using Amazon SageMaker to deploy a model for real-time inference. The model must respond within 100 milliseconds. The initial deployment uses a single ml.m5.large instance, but latency is too high. Which change should the engineer make to reduce latency?

Question 117hardmulti select

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A data scientist is training a binary classification model using Amazon SageMaker's built-in XGBoost algorithm. The dataset is highly imbalanced (95% negative class, 5% positive class). The model achieves high accuracy but poor recall on the positive class. Which TWO actions should the data scientist take to improve recall without significantly sacrificing precision?

Question 118easymulti select

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A machine learning engineer is deploying a model using Amazon SageMaker. The model requires preprocessing steps (e.g., scaling, encoding) that were applied during training. Which TWO options can ensure the same preprocessing is applied at inference?

Question 119mediummultiple choice

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A data scientist is using Amazon SageMaker Autopilot to automatically build a binary classification model. The dataset has 50 features and 100,000 rows. After the experiment completes, the best candidate model achieves an F1 score of 0.85 on the validation set. However, when deployed to a real-time endpoint, the model's F1 score drops to 0.72 on production data. The data distributions between training and production are similar. What is the MOST likely cause of the performance drop?

Question 120easymultiple choice

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A company wants to use Amazon SageMaker to train a model, but the training data contains personally identifiable information (PII). The data scientist needs to ensure that the PII is not accessible during training. The data is stored in S3. What is the MOST secure approach?

Question 121mediummulti select

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A data scientist is training a binary classification model to predict customer churn. The dataset has 10,000 samples with 500 churners (5% positive class). Which TWO techniques should the scientist use to address the class imbalance? (Choose TWO.)

Question 122hardmulti select

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A machine learning engineer is tuning a Gradient Boosting model for a regression task. The dataset contains 50 features and 100,000 samples. The engineer wants to speed up training without sacrificing predictive performance significantly. Which THREE hyperparameters should the engineer consider adjusting? (Choose THREE.)

Question 123easymultiple choice

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A data scientist is training a linear regression model using Amazon SageMaker's built-in Linear Learner algorithm. The dataset has 500 features and 1 million rows. After training, the model's training RMSE is 2.5 and validation RMSE is 2.6, which is acceptable. However, the scientist notices that many feature coefficients are very small but non-zero, and the model takes a long time to train. The scientist wants to reduce training time while maintaining similar accuracy. Which action should the scientist take?

Question 124mediummultiple choice

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A company is building a fraud detection model using a random forest classifier. The dataset is highly imbalanced with 99% legitimate transactions and 1% fraudulent. The model currently achieves 99% accuracy on the test set, but the fraud recall is only 10%. The business requires at least 80% recall for fraud. The data scientist has tried oversampling the minority class and adjusting class weights, but recall remains below 40%. The dataset contains millions of transactions with hundreds of features. Which approach should the data scientist try next to improve fraud recall?

Question 125mediummultiple choice

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A machine learning engineer is deploying a sentiment analysis model using Amazon SageMaker. The model is a BERT-based transformer that takes up to 512 tokens. The engineer notices that inference latency is high (over 500 ms per request) on a single ml.c5.xlarge instance. The application requires latency under 100 ms. The model has already been optimized using half-precision (FP16). Which action should the engineer take to reduce latency?

Question 126hardmultiple choice

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A data scientist is training a binary classifier to detect network intrusions. The dataset has 1,000 features and 10 million samples, but only 0.1% are positive (intrusions). The scientist uses XGBoost with scale_pos_weight set to 100. The model achieves a recall of 0.90 and precision of 0.05 on the test set. The business requires precision of at least 0.50 while maintaining recall above 0.80. Which technique should the scientist apply?

Question 127easymultiple choice

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A machine learning engineer is training a regression model to predict house prices using Amazon SageMaker. The dataset contains 10,000 samples and 50 numerical features. After training a linear regression model, the engineer notices that the training loss is low, but the validation loss is high. The engineer suspects overfitting. The dataset is already normalized. Which action should the engineer take to reduce overfitting?

Question 128mediummultiple choice

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A team is deploying a real-time inference endpoint using Amazon SageMaker. The model is a large deep learning model that requires GPU for inference. The endpoint must handle variable traffic patterns with minimal latency. Which deployment strategy should the team use?

Question 129hardmultiple choice

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A machine learning engineer is training a deep learning model using the SageMaker built-in XGBoost algorithm. The training job is taking longer than expected. The engineer notices that the training data is stored in S3 in CSV format and is 500 GB in size. The instance type is ml.c4.8xlarge with 10 instances. Which change would most likely reduce training time?

Question 130mediummultiple choice

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A company is deploying a fraud detection model using Amazon SageMaker. The model is a linear learner trained on 100 GB of data. For inference, the model receives individual transactions and must return a prediction within 100 ms. Which endpoint configuration should the team use to meet the latency requirement?

Question 131hardmultiple choice

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A data scientist is using SageMaker to train a TensorFlow model. The training script uses tf.data.Dataset to load data from S3. Training is slow because of I/O bottleneck. Which change should the data scientist make to improve I/O performance?

Question 132mediummultiple choice

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A company uses Amazon SageMaker to deploy a model for real-time inference. The endpoint uses an ml.m5.large instance with automatic scaling based on CPU utilization. The team notices that during traffic spikes, the endpoint returns 5xx errors. What should the team do to improve the endpoint's availability?

Question 133easymulti select

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A data scientist is training a binary classifier using imbalanced data. Which TWO techniques can help improve model performance on the minority class? (Choose two.)

Question 134mediummulti select

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A company is using SageMaker to deploy a model for real-time inference. The model requires GPU for low latency. Which THREE configurations should the company consider for high availability and cost optimization? (Choose three.)

Question 135mediummultiple choice

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A company is deploying a machine learning model for real-time fraud detection. The model must respond within 100ms. Which SageMaker endpoint deployment strategy should be used?

Question 136hardmultiple choice

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A team is training a deep learning model on SageMaker using a custom Docker container. The training job fails with 'OutOfMemoryError'. The instance type is ml.p3.2xlarge with 61 GB memory. Which change should increase available memory?

Question 137mediummultiple choice

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A company wants to deploy a machine learning model that requires GPU acceleration for inference. The model is small and can fit on a single GPU. Which SageMaker endpoint configuration is MOST cost-effective?

Question 138hardmultiple choice

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A company is deploying a model for real-time inference with SageMaker. The endpoint receives spiky traffic, with occasional bursts of 10x normal load. Which scaling policy is MOST cost-effective while maintaining availability?

Question 139easymultiple choice

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A CloudFormation stack creation failed. The SageMaker endpoint resource shows CREATE_FAILED. What is the most likely issue?

Network Topology

Question 140easymultiple choice

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A data scientist is training a model and wants to monitor training progress. Which AWS service can be used to track metrics like loss and accuracy in real time?

Question 141mediummultiple choice

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A company is using SageMaker to train a model, but the training job fails with an out-of-memory error. Which action should the data scientist take to resolve this issue?

Question 142easymultiple choice

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A data scientist is building a time series forecasting model for monthly sales data. The scientist has observed that the sales data shows a clear upward trend and a seasonal pattern that repeats every 12 months. Which algorithm would be most appropriate for this task?

Question 143easymulti select

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Which TWO of the following are valid methods for handling missing values in a dataset before training a machine learning model?

Question 144mediummulti select

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Which THREE of the following are best practices for training deep learning models on Amazon SageMaker?

Question 145easymultiple choice

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A data scientist is training a text classification model using a bag-of-words approach. The dataset contains 1 million documents and 100,000 unique words. The resulting feature matrix is very sparse. Which technique should the data scientist use to reduce the dimensionality of the feature space?

Question 146hardmultiple choice

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Refer to the exhibit. A data scientist ran a SageMaker training job using a built-in XGBoost algorithm. The job failed with the error shown. Which step should the data scientist take to fix the issue?

Network Topology

Question 147hardmulti select

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A data scientist is building a binary classification model to predict loan default. The dataset is highly imbalanced (5% default, 95% non-default). Which TWO techniques should the data scientist use to address the class imbalance?

Question 148mediummulti select

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A data scientist is training a deep neural network on Amazon SageMaker. The training is taking a long time and the data scientist wants to speed it up. Which THREE actions can help reduce training time?

Question 149mediummultiple choice

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A data scientist is using principal component analysis (PCA) for dimensionality reduction before training a classifier. The classifier's performance on the test set is poor. What is the most likely cause?

Question 150mediummulti select

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A data scientist is training a model using SageMaker and wants to automatically stop training when the model stops improving. Which TWO options can be used?

Question 151hardmulti select

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A company is deploying a real-time inference endpoint with SageMaker. The model is a large neural network that requires GPU acceleration. Which TWO configurations must be set?

Question 152hardmultiple choice

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Refer to the exhibit. A data scientist is trying to create a SageMaker training job but receives an access denied error. The IAM policy shown is attached to their role. What is the most likely reason for the error?

Exhibit

Refer to the exhibit.

{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Action": "sagemaker:CreateTrainingJob",
      "Resource": "*",
      "Condition": {
        "StringEquals": {
          "sagemaker:TrainingJobStatus": "Failed"
        }
      }
    }
  ]
}

Question 153mediummultiple choice

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Refer to the exhibit. A data scientist runs the AWS CLI command to create a SageMaker training job. The job fails immediately with 'ValidationException: Invalid instance type'. What is the most likely issue?

Exhibit

Refer to the exhibit.

$ aws sagemaker create-training-job \
    --training-job-name my-job \
    --algorithm-specification TrainingImage=382416733822.dkr.ecr.us-west-2.amazonaws.com/xgboost:latest,TrainingInputMode=File \
    --role-arn arn:aws:iam::123456789012:role/SageMakerRole \
    --input-data-config "[{\"ChannelName\": \"train\", \"DataSource\": {\"S3DataSource\": {\"S3DataType\": \"S3Prefix\", \"S3Uri\": \"s3://my-bucket/train/\", \"S3DataDistributionType\": \"FullyReplicated\"}}}]" \
    --output-data-config "{\"S3OutputPath\": \"s3://my-bucket/output/\"}" \
    --resource-config "{\"InstanceType\": \"ml.m5.large\", \"InstanceCount\": 1}" \
    --stopping-condition "{\"MaxRuntimeInSeconds\": 86400}"

Question 154hardmultiple choice

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A machine learning team is deploying a model using Amazon SageMaker. The model receives requests with sparse high-dimensional features. The team wants to minimize inference latency. Which SageMaker endpoint configuration is MOST suitable?

Question 155mediummultiple choice

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A company uses Amazon SageMaker to train a deep learning model on a GPU instance. The training job is taking too long. Which action would MOST likely reduce training time?

Question 156mediummulti select

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Which TWO approaches are valid for handling missing categorical values in a dataset before training a machine learning model?

Question 157mediummultiple choice

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A company uses Amazon SageMaker to train an XGBoost model on a large dataset. Training takes a long time. Which action can reduce training time without significantly affecting model accuracy?

Question 158easymultiple choice

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Refer to the exhibit. A data scientist creates a SageMaker notebook instance using this Terraform configuration. The notebook fails to start. The logs indicate 'The IAM role does not have the necessary permissions'. Which addition to the IAM role policy is MOST likely needed?

Exhibit

Refer to the exhibit.

resource "aws_sagemaker_notebook_instance" "my_notebook" {
  name          = "my-notebook"
  role_arn      = aws_iam_role.notebook_role.arn
  instance_type = "ml.t2.medium"
  lifecycle_config_name = aws_sagemaker_notebook_instance_lifecycle_configuration.my_config.name
}

Question 159easymultiple choice

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A company is building a binary classification model to predict customer churn. The dataset has 10,000 samples with 500 churners (positive class). The data scientist trains a logistic regression model and obtains an accuracy of 95%. However, the model predicts all customers as non-churn. Which metric should the data scientist use to evaluate the model's performance?

Question 160mediummultiple choice

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A data scientist is using Amazon SageMaker to train a model on a dataset that contains both numerical and categorical features. The categorical features have high cardinality (e.g., postal codes, product IDs). Which feature engineering approach is most suitable for handling these high-cardinality categorical features in a tree-based model?

Question 161hardmultiple choice

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A data scientist is using Amazon SageMaker's built-in BlazingText algorithm for word2vec embeddings. The dataset is a corpus of 10 million documents. After training, the data scientist observes that the learned embeddings do not capture semantic similarity well (e.g., 'king' and 'queen' are not close). Which hyperparameter adjustment is most likely to improve the quality of embeddings?

Question 162hardmultiple choice

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A company uses Amazon SageMaker to train a model for fraud detection. The dataset has 1 million transactions, with 0.1% fraud. The data scientist trains a random forest model and achieves 99.9% accuracy but 0% recall on the fraud class. Which technique is most likely to improve recall without significantly reducing precision?

Question 163mediummultiple choice

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A data scientist ran an XGBoost training job on Amazon SageMaker using a CSV dataset. The training job failed with the error shown. What is the most likely cause of this failure?

Network Topology

Question 164hardmultiple choice

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A data scientist is setting up an IAM role for an Amazon SageMaker training job. The policy shown is attached to the role. The training job fails with an access denied error when trying to read the training data from s3://my-bucket/training/data.csv. What is the most likely reason?

Exhibit

Refer to the exhibit.
```
{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Effect": "Allow",
            "Action": [
                "sagemaker:CreateTrainingJob",
                "sagemaker:DescribeTrainingJob"
            ],
            "Resource": "*"
        },
        {
            "Effect": "Allow",
            "Action": [
                "s3:GetObject"
            ],
            "Resource": "arn:aws:s3:::my-bucket/training/*"
        },
        {
            "Effect": "Allow",
            "Action": [
                "s3:PutObject"
            ],
            "Resource": "arn:aws:s3:::my-bucket/output/*"
        }
    ]
}
```

Question 165hardmultiple choice

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A data scientist ran a hyperparameter tuning job for an XGBoost model. The tuning job completed, but the best validation RMSE is 2.34. The data scientist believes the model can perform better. Based on the exhibit, which change to the tuning strategy is most likely to improve the model's performance?

Network Topology

Question 166mediummultiple choice

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A company is using SageMaker to deploy a real-time inference endpoint for a natural language processing model. The model receives input text and returns predictions. The data scientist notices that the endpoint latency increases significantly under load. Which design change would MOST effectively reduce latency?

Question 167hardmultiple choice

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A data scientist is training a deep learning model for object detection. The training loss decreases rapidly in the first few epochs but then plateaus at a high value. The validation loss starts increasing after a few epochs. Which adjustment is MOST likely to improve generalization?

Question 168mediummultiple choice

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A data scientist is building a model to predict insurance claim amounts. The target variable is right-skewed with many small claims and a few very large claims. The scientist wants to minimize the impact of outliers. Which loss function or transformation is MOST appropriate?

Question 169hardmulti select

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A data scientist is using Amazon SageMaker to train a deep learning model. The training job is taking too long. Which THREE actions can reduce training time?

Question 170mediummulti select

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A company is building a sentiment analysis model for customer reviews. The dataset is balanced with 10,000 positive and 10,000 negative reviews. The model achieves 95% accuracy on the test set but fails to generalize to new reviews from a different product category. Which TWO techniques can improve generalization?

Question 171hardmultiple choice

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Refer to the exhibit. A data scientist wants to use SageMaker to train a model using data stored in 'my-bucket'. The training job fails with an access denied error. What is the MOST likely cause?

Exhibit

Refer to the exhibit.

```
{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Action": [
        "s3:GetObject",
        "s3:PutObject"
      ],
      "Resource": "arn:aws:s3:::my-bucket/*",
      "Condition": {
        "StringEquals": {
          "s3:x-amz-server-side-encryption": "AES256"
        }
      }
    },
    {
      "Effect": "Deny",
      "Action": "s3:PutObject",
      "Resource": "arn:aws:s3:::my-bucket/*",
      "Condition": {
        "StringNotEquals": {
          "s3:x-amz-server-side-encryption": "AES256"
        }
      }
    }
  ]
}
```

Question 172mediummultiple choice

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A machine learning team is deploying a real-time inference endpoint for a fraud detection model using Amazon SageMaker. The model requires low latency (<100 ms) and the team expects a steady stream of requests with occasional spikes. Which instance type and deployment strategy should they use to minimize cost while meeting latency requirements?

Question 173hardmultiple choice

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A data scientist is training a deep learning model on Amazon SageMaker using a PyTorch estimator. The training job runs on a single ml.p3.2xlarge instance but is taking too long. The scientist wants to reduce training time by using distributed data parallelism across multiple GPUs. Which change to the training script and SageMaker estimator is required?

Question 174hardmultiple choice

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A machine learning engineer is deploying a model to an Amazon SageMaker endpoint for real-time inference. The model is a large ensemble that requires 4 GB of memory. The engineer wants to minimize cost while ensuring the endpoint can handle up to 100 concurrent requests with a latency under 200 ms. Which instance configuration is most appropriate?

Question 175hardmulti select

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A data scientist is tuning a gradient boosting model using Amazon SageMaker Automatic Model Tuning (AMT). Which THREE hyperparameters should the scientist consider tuning to reduce overfitting? (Select THREE.)

Question 176easymultiple choice

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Refer to the exhibit. The log shows the end of a successful SageMaker training job. However, the ML engineer cannot find the model artifacts in the specified S3 bucket. What is the most likely cause?

Exhibit

Refer to the exhibit.

```
[Container] Training completed.
[Container] Uploading model artifacts to S3: s3://my-bucket/model.tar.gz
[Container] Training job completed
```

Question 177mediummultiple choice

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Refer to the exhibit. An IAM policy is attached to a SageMaker notebook instance. A data scientist is trying to invoke the endpoint 'my-endpoint' from the notebook but receives an AccessDenied error. What is the likely cause?

Exhibit

Refer to the exhibit.

```
{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Action": [
        "sagemaker:CreateEndpoint"
      ],
      "Resource": "*"
    },
    {
      "Effect": "Allow",
      "Action": [
        "sagemaker:InvokeEndpoint"
      ],
      "Resource": "arn:aws:sagemaker:us-east-1:123456789012:endpoint/my-endpoint"
    }
  ]
}
```

Question 178mediummultiple choice

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A company is deploying a real-time fraud detection model using Amazon SageMaker. The model must make predictions in under 100 milliseconds. The data scientist uses a pre-trained XGBoost model and deploys it to a SageMaker endpoint with an ml.c5.xlarge instance. After load testing, the average latency is 150 ms. Which action should the data scientist take to reduce latency?

Question 179hardmultiple choice

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A company wants to build a machine learning model to predict customer churn. The dataset includes customer demographics, usage patterns, and support interactions. The data is stored in Amazon S3. The data scientist needs to perform feature engineering, including creating aggregate features from support interactions and encoding categorical variables. Which AWS service is most suitable for building the feature engineering pipeline?

Question 180mediummultiple choice

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A data scientist is assigned an IAM policy as shown. The data scientist attempts to create a SageMaker endpoint to deploy a model, but the request fails. What is the most likely reason?

Exhibit

Refer to the exhibit.

{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Effect": "Allow",
            "Action": [
                "sagemaker:CreateTrainingJob",
                "sagemaker:DescribeTrainingJob",
                "s3:GetObject",
                "s3:PutObject"
            ],
            "Resource": "*"
        },
        {
            "Effect": "Deny",
            "Action": [
                "sagemaker:CreateEndpoint"
            ],
            "Resource": "*"
        }
    ]
}

Question 181easymultiple choice

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A data scientist is using Amazon SageMaker to train a linear regression model. The training data contains missing values. Which preprocessing step should be applied before training?

Question 182hardmultiple choice

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A data scientist is using Amazon SageMaker to train a large language model with PyTorch. The training job is taking too long. The dataset is stored in S3 and the training script uses the SageMaker PyTorch container. Which change is MOST likely to reduce training time?

Question 183easymultiple choice

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A data scientist is using Amazon SageMaker to train a model. The training job is using a large dataset stored in S3. Which data input mode provides the FASTEST data loading for training?

Question 184easymulti select

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A company is using Amazon SageMaker to train a model. Which TWO metrics should be used to evaluate a binary classification model?

Question 185mediummultiple choice

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A machine learning engineer is trying to deploy a model using a SageMaker endpoint but receives an access denied error. The IAM policy attached to the role is shown in the exhibit. What is the MOST likely cause of the error?

Exhibit

Refer to the exhibit.

{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Action": [
        "sagemaker:CreateTrainingJob",
        "sagemaker:CreateModel",
        "sagemaker:CreateEndpointConfig",
        "sagemaker:CreateEndpoint",
        "sagemaker:InvokeEndpoint"
      ],
      "Resource": "*"
    }
  ]
}

Question 186hardmultiple choice

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A data scientist runs a training job that fails. The CLI output is shown in the exhibit. What is the MOST likely cause of the failure?

Network Topology

Question 187easymultiple choice

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A machine learning team is using Amazon SageMaker to train a linear regression model. The team notices that the training loss decreases rapidly initially but then plateaus at a high value. What is the MOST likely cause?

Question 188hardmultiple choice

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A data scientist is training a convolutional neural network (CNN) for image classification using Amazon SageMaker. The training loss decreases steadily but validation loss starts increasing after a few epochs. Which action should the data scientist take to address this issue?

Question 189mediummultiple choice

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A data scientist is trying to launch a SageMaker training job using an IAM role with the above policy. The training job fails with an access denied error. What is the MOST likely reason?

Exhibit

Refer to the exhibit.

```
{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Action": [
        "sagemaker:CreateTrainingJob",
        "sagemaker:DescribeTrainingJob",
        "sagemaker:StopTrainingJob"
      ],
      "Resource": "*"
    },
    {
      "Effect": "Allow",
      "Action": [
        "s3:GetObject",
        "s3:PutObject"
      ],
      "Resource": "arn:aws:s3:::my-bucket/*"
    }
  ]
}
```

Question 190hardmultiple choice

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A data scientist runs a SageMaker training job and receives the above error. The S3 bucket 'my-bucket' contains a folder 'data' with a file 'data.csv'. What is the MOST likely cause of the error?

Network Topology

Question 191mediummultiple choice

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A data scientist is deploying a SageMaker model using CloudFormation. The stack creation fails with the above error. What is the MOST likely cause?

Network Topology

Question 192hardmultiple choice

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A company is deploying a real-time inference endpoint using SageMaker. The model is a large deep learning model (5 GB) with strict latency requirements (< 100 ms per request). The team expects bursty traffic with up to 1000 requests per second. Which configuration best meets the latency and throughput requirements?

Question 193mediummultiple choice

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A company uses SageMaker built-in BlazingText algorithm for text classification. The model performance is poor on the validation set. The data consists of short documents (average 50 words). Which hyperparameter tuning strategy is most likely to improve performance?

Question 194hardmultiple choice

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A machine learning team is using SageMaker to train a custom TensorFlow model on a dataset that fits in memory. The training job is taking too long. The team wants to reduce training time without changing the model architecture. Which approach is most effective?

Question 195mediummultiple choice

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A data scientist is using SageMaker to train a model that requires access to a private S3 bucket in another account. The scientist has set up the correct IAM roles and bucket policies. However, the training job fails with an access denied error. What is the most likely cause?

Question 196mediummultiple choice

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An IAM policy attached to a SageMaker execution role is shown. A training job executed with this role fails with an error that the role cannot access the S3 bucket. The training job uses input data from s3://my-bucket/train/data.csv and output to s3://my-bucket/output/. What is the most likely cause?

Exhibit

Refer to the exhibit.

{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Action": [
        "s3:GetObject",
        "s3:PutObject"
      ],
      "Resource": "arn:aws:s3:::my-bucket/train/*"
    },
    {
      "Effect": "Allow",
      "Action": [
        "sagemaker:CreateTrainingJob",
        "sagemaker:DescribeTrainingJob"
      ],
      "Resource": "*"
    }
  ]
}

Question 197easymultiple choice

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A data scientist needs to evaluate a binary classification model. The dataset is balanced. Which metric is most appropriate to compare model performance?

Question 198hardmulti select

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A company is deploying a machine learning model for fraud detection. The model outputs a probability score. The cost of false negatives is very high. Which TWO metrics should the company focus on optimizing?

Question 199easymulti select

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A data scientist is evaluating a classification model. The confusion matrix shows that the model has 50 true positives, 100 true negatives, 20 false positives, and 30 false negatives. Which TWO metrics can be calculated from this confusion matrix? (Choose two.)

Question 200hardmultiple choice

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A financial services company is developing a fraud detection model using a highly imbalanced dataset where fraudulent transactions are only 0.1% of the data. The data scientist has trained a gradient boosting model that achieves 99.9% accuracy but only detects 20% of actual fraud cases. The business requirement is to detect at least 80% of fraud while minimizing false positives. The data scientist has access to SageMaker and can use any built-in algorithm or custom script. Which approach should the data scientist take to meet the business requirement?

Question 201mediummultiple choice

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A data scientist is using Amazon SageMaker to train a linear regression model. The training data has 10 features and 100,000 observations. The model's training loss is decreasing, but the validation loss starts increasing after a few epochs. Which step should the data scientist take first to address this issue?

Question 202hardmultiple choice

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A data scientist is training a binary classifier on a dataset with 1 million rows and 500 features. The model uses XGBoost and achieves an AUC of 0.95 on the training set but only 0.72 on the test set. The scientist suspects overfitting. Which combination of hyperparameter adjustments is most likely to improve generalization?

Question 203mediummulti select

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A data scientist is building a text classification model using a bag-of-words approach with logistic regression. The dataset has 10,000 documents and 50,000 unique tokens. The model overfits. Which TWO techniques can help reduce overfitting?

Question 204hardmulti select

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A company is training a deep learning model for object detection using Amazon SageMaker. The training job is taking too long. Which TWO actions can reduce training time?

Question 205easymulti select

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A data scientist is using Amazon SageMaker to train a linear regression model. The training data contains missing values. Which TWO techniques are appropriate for handling missing values in the dataset?

Question 206mediummultiple choice

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A data scientist is working on a binary classification problem to predict loan default. The dataset has 200,000 samples and 50 features. The target variable is imbalanced: 5% default, 95% non-default. The scientist trains a logistic regression model and achieves 95% accuracy, but the recall for the default class is only 20%. The business requires that at least 70% of actual defaults be identified (recall >= 0.7). Which approach should the scientist take to improve recall without significantly sacrificing precision?

Question 207mediummultiple choice

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A machine learning team is deploying a model for real-time fraud detection. The model must make predictions with less than 100ms latency. The team uses SageMaker and the model is a large ensemble of decision trees. Which SageMaker hosting option is MOST suitable?

Question 208hardmultiple choice

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A company uses SageMaker to train a model that processes sensitive customer data. Due to compliance, the training data must be encrypted at rest and in transit, and the model artifacts must be stored in a secured S3 bucket with encryption. Which combination of actions is REQUIRED?

Question 209hardmultiple choice

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A data scientist is training a model using SageMaker's built-in XGBoost algorithm. The dataset has 500 features and 1 million rows. The training job is taking too long. The scientist wants to reduce training time without sacrificing accuracy. Which action is LIKELY to be most effective?

Question 210mediummulti select

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A data scientist is building a binary classifier to predict customer churn. The dataset is highly imbalanced (5% churn). Which TWO techniques can help improve the model's ability to detect churn?

Question 211hardmultiple choice

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A company runs a real-time recommendation system on SageMaker with a model that uses a deep neural network. The endpoint uses a single ml.p3.2xlarge instance. Recently, the number of users has grown, and the endpoint's latency has increased from 50ms to 200ms, exceeding the SLA of 100ms. The model inference code is optimized and cannot be improved further. The company wants to reduce latency while minimizing cost. The data scientist has the following options: A. Switch to a larger instance type with more GPU memory, such as ml.p3.8xlarge. B. Use SageMaker's Elastic Inference to attach an EI accelerator to the existing instance. C. Deploy the model on multiple smaller instances (e.g., ml.p3.2xlarge) behind a load balancer and distribute traffic. D. Convert the model to use TensorFlow Lite and deploy on a CPU-based instance. Which option is the MOST cost-effective and meets the latency requirement?

Question 212mediummultiple choice

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A data scientist is training a binary classification model on a dataset with 100 features and 10,000 rows. The model overfits significantly: training accuracy is 99%, but validation accuracy is 80%. The data scientist has tried L1 and L2 regularization without improvement. The dataset is clean and representative. Which approach is MOST likely to reduce overfitting? A. Increase the number of training epochs. B. Add more training data by generating synthetic samples using SMOTE. C. Reduce the number of features using PCA. D. Use a simpler model like logistic regression instead of a decision tree ensemble. The data scientist needs to maintain a validation accuracy above 85%, but the current model is too complex. The company has limited budget for data labeling. Which option is BEST?

Question 213easymultiple choice

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A company is using SageMaker to train a text classification model using a built-in BlazingText algorithm. The dataset has 500,000 documents, each labeled with one of 10 categories. The training time is taking longer than expected. The data scientist wants to speed up training without increasing cost. The training job is using a single ml.m4.xlarge instance. The code uses default hyperparameters. Which change is MOST likely to reduce training time? A. Use a larger instance type, such as ml.m4.4xlarge. B. Increase the learning rate significantly. C. Use SageMaker Managed Spot Training. D. Use the 'mode' hyperparameter set to 'batch_skipgram' instead of 'supervised'. The company has a fixed budget and wants to minimize cost while reducing training time. Which option should the data scientist choose?

Question 214easymultiple choice

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A data scientist is training a binary classification model on an imbalanced dataset (95% negative class, 5% positive class). The model achieves 95% accuracy but only predicts the negative class for all examples. Which metric should the scientist use to evaluate model performance more appropriately?

Question 215easymultiple choice

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A data scientist has this IAM policy attached to an IAM role used by SageMaker. When trying to create a training job, the scientist gets an access denied error. The training data is in 's3://my-bucket/training-data/'. What is the most likely cause?

Exhibit

Refer to the exhibit.

{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Action": [
        "sagemaker:CreateTrainingJob",
        "sagemaker:CreateModel",
        "sagemaker:CreateEndpointConfig",
        "sagemaker:CreateEndpoint"
      ],
      "Resource": "*"
    },
    {
      "Effect": "Allow",
      "Action": "s3:GetObject",
      "Resource": "arn:aws:s3:::my-bucket/training-data/*"
    }
  ]
}

Question 216mediummultiple choice

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A data scientist ran a SageMaker training job that failed with the error shown. The training script expects the data in '/opt/ml/input/data/training/train.csv'. What is the most likely issue?

Network Topology

Question 217hardmultiple choice

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A data scientist is setting up a SageMaker training job and has attached this IAM policy to the execution role. The training job fails with an access denied error when trying to write to the output path 's3://my-bucket/output/model.tar.gz'. What additional permission is needed?

Exhibit

Refer to the exhibit.

{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Action": [
        "s3:PutObject",
        "s3:GetObject"
      ],
      "Resource": "arn:aws:s3:::my-bucket/output/*"
    },
    {
      "Effect": "Allow",
      "Action": "iam:PassRole",
      "Resource": "arn:aws:iam::123456789012:role/SageMakerRole"
    }
  ]
}

Question 218hardmultiple choice

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A company is building a real-time fraud detection system using Amazon SageMaker. The model is a gradient boosting classifier trained on 500 GB of transactional data. The inference endpoint is deployed as a SageMaker real-time endpoint using an ml.c5.9xlarge instance. The model is serialized using the native format of the framework (XGBoost). The endpoint receives about 100 requests per second with an average payload size of 10 KB. The company observes that the endpoint's latency is around 200 ms, but they need under 100 ms. The data scientist profiles the endpoint and finds that the model inference time is 50 ms, but the remaining time is spent on data preprocessing and serialization/deserialization. The preprocessing involves converting JSON input to a NumPy array and then to a DMatrix. Which action is most likely to reduce latency to meet the requirement?

Question 219mediummultiple choice

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A data scientist is using Amazon SageMaker to train a natural language processing model using a custom Docker container. The training script reads data from an S3 bucket and writes checkpoints to an S3 bucket. The training job is failing with the error 'Unable to write to checkpoint path: s3://my-bucket/checkpoints/'. The IAM role associated with the training job has the following policy: {'Effect': 'Allow', 'Action': 's3:PutObject', 'Resource': 'arn:aws:s3:::my-bucket/checkpoints/*'}. The bucket 'my-bucket' exists and the prefix 'checkpoints/' is empty. What is the most likely cause of the failure?

Question 220easymultiple choice

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A data scientist is using Amazon SageMaker to train a linear regression model. The training data contains 100 features and 1 million rows. The scientist notices that the model is overfitting, with training R² of 0.99 and validation R² of 0.65. The scientist has already tried adding L2 regularization and reducing the number of features. Which additional technique should the scientist try to reduce overfitting?

Question 221hardmultiple choice

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A company uses Amazon SageMaker to host a model for real-time inference. The model is a large ensemble of 10 deep learning models, each 500 MB. The total model size is 5 GB, which exceeds the 5 GB limit for SageMaker real-time endpoints. The data scientist wants to reduce the model size without significantly impacting accuracy. The ensemble uses averaging of predictions from all models. The scientist has access to a validation set with 10,000 samples. Which technique should the scientist use to reduce the model size?

Question 222mediummulti select

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A machine learning engineer is training a deep learning model on Amazon SageMaker. The training job is taking a long time. Which THREE actions can reduce training time? (Choose 3.)

Question 223hardmulti select

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A data scientist is training a random forest model for regression. The model shows high variance on the validation set. Which TWO actions are most likely to reduce variance? (Choose 2.)

Question 224easymultiple choice

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A startup is building a recommendation system for an e-commerce platform using collaborative filtering. They have a dataset of user-item interactions (ratings) with 1 million users and 100,000 items. The data is sparse (99% missing ratings). They need to train a model on Amazon SageMaker that can handle large-scale sparse data efficiently. Which approach should they use?

Question 225mediummultiple choice

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A financial services company is developing a fraud detection model using gradient boosting. The dataset contains 10 million transactions with 0.1% fraudulent. The model is trained on a SageMaker ml.m5.2xlarge instance and takes 8 hours. The team needs to reduce training time without sacrificing model performance. They have permission to use up to 4 instances. What should they do?

Question 226hardmultiple choice

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A healthcare company is building a model to predict patient readmission within 30 days. They have structured electronic health records (EHR) data with 200 features. The data includes missing values, categorical variables with high cardinality (e.g., diagnosis codes), and a severe class imbalance (5% readmission). They need to deploy a model on SageMaker that is interpretable and achieves high recall for the positive class. Which combination of techniques should they use?

Question 227mediummultiple choice

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A company uses SageMaker to deploy a real-time inference endpoint for a fraud detection model. The model is an XGBoost model trained on 50 features. The endpoint receives 100 requests per second, but latency is higher than the required 200 ms. The team wants to reduce latency without retraining. What should they do?

Question 228mediummultiple choice

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A company is using SageMaker's built-in image classification algorithm to classify product images into 100 categories. The training takes 3 hours on a single p3.2xlarge instance. They need to reduce training time to under 1 hour. They have access to a cluster of 4 p3.2xlarge instances. Which approach should they take?

Question 229hardmultiple choice

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A bank is building a credit risk model using a large dataset with 500 features and 2 million samples. The dataset contains many categorical features with high cardinality (e.g., zip code, occupation). The model must be deployed on SageMaker and provide real-time predictions with low latency. They also need to explain individual predictions for regulatory compliance. Which approach is most appropriate?

Question 230easymultiple choice

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A company uses Amazon SageMaker to train a linear regression model on a dataset with 10 million rows and 50 features. The training job takes 8 hours to complete. A data scientist wants to reduce the training time to under 2 hours without changing the dataset size or the model algorithm. The SageMaker instance type currently used is ml.m5.2xlarge. Which action should the data scientist take to achieve the desired training time?

Question 231hardmulti select

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A machine learning team is deploying a real-time inference endpoint for a fraud detection model using Amazon SageMaker. The model is a LightGBM classifier trained on 1 GB of tabular data. The endpoint must respond within 100 ms for 99% of requests, with a throughput of 10 requests per second. During load testing, the team observes that the 99th percentile latency is 250 ms and the endpoint CPU utilization is consistently above 90%. The team has already selected an ml.c5.xlarge instance with auto scaling enabled. Which combination of actions should the team take to meet the latency requirement? (Choose 3.)

Question 232hardmultiple choice

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A data scientist is using Amazon SageMaker to train a neural network for a regression task. The training loss decreases but the validation loss starts to increase after several epochs. The data scientist wants to automatically stop training when validation loss no longer improves, without manual intervention. Which SageMaker feature should be used?

Question 233mediummulti select

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A data scientist is preparing a dataset for training a linear regression model using Amazon SageMaker. The dataset contains a categorical feature 'city' with 50 unique values. The data scientist wants to encode this feature appropriately for the model. Which two steps should be taken? (Choose two.)

Question 234mediummultiple choice

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A machine learning engineer is training a linear regression model on a dataset with 50 features using Amazon SageMaker's built-in Linear Learner algorithm. The training loss decreases initially but then starts to oscillate significantly and the model performs poorly on the validation set. The engineer suspects overfitting. Which of the following techniques is specifically designed within the Linear Learner algorithm to address this issue?

Question 235hardmultiple choice

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A machine learning engineer is using Amazon SageMaker Automatic Model Tuning to optimize a deep learning model. The tuning job has completed 50 training jobs, but the best objective metric has not improved in the last 15 jobs. The engineer wants to stop the tuning job early to save cost while preserving the best model found so far. Which action should the engineer take?

Question 236mediummultiple choice

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A machine learning engineer is using Amazon SageMaker to train a model that predicts customer churn. The dataset contains a categorical feature "region" with 50 unique values. The engineer wants to avoid imposing an artificial ordinal relationship and also keep the feature dimension manageable. Which encoding approach should be used?

Question 237mediummultiple choice

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A data scientist is training a gradient boosting model using the SageMaker built-in XGBoost algorithm on a tabular dataset with a highly skewed target variable. The model performs poorly on the minority class in validation. The scientist wants to improve performance without collecting new data. After enabling scale_pos_weight in the hyperparameters, which additional SageMaker built-in XGBoost hyperparameter should be tuned to directly control the trade-off between training loss and model complexity?

Question 238easymultiple choice

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A data scientist is training a model using Amazon SageMaker and wants to evaluate its performance on a held-out test set. The dataset is stored in Amazon S3 and is already split into training and test sets. Which SageMaker feature should be used to compute evaluation metrics after training?

Question 239hardmultiple choice

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A machine learning engineer is deploying a model to an Amazon SageMaker endpoint for real-time inference. The model is a deep neural network that requires GPU acceleration and must handle a high volume of requests with low latency. The engineer wants to optimize the endpoint for cost and performance. Which deployment strategy should the engineer use?

Question 240hardmultiple choice

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A financial institution is building a model to predict loan defaults using Amazon SageMaker. The dataset is highly imbalanced, with only 2% of loans defaulting. The team wants to evaluate the model's ability to identify defaulters while controlling the false positive rate. Which metric should the team use to optimize the model?

Question 241hardmultiple choice

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A machine learning engineer is training a deep learning model on Amazon SageMaker using the PyTorch framework. The training data is stored in an Amazon S3 bucket in TFRecord format. The engineer wants to maximize GPU utilization and minimize training time. The dataset is large (500 GB) and consists of many small files. Which data input mode and configuration should the engineer use to achieve the best performance?

Question 242mediummultiple choice

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A data scientist is using Amazon SageMaker to train a deep learning model for natural language processing. The training job is taking too long, and the data scientist wants to reduce the training time without sacrificing model accuracy. The model uses a large embedding layer and many LSTM layers. Which of the following changes is most likely to achieve this goal?

Question 243mediummultiple choice

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A machine learning engineer is training a linear regression model on a dataset with 500 features using Amazon SageMaker's built-in Linear Learner algorithm. The model's training loss decreases but validation loss starts to increase after 10 epochs. The engineer wants to automatically stop training when validation loss no longer improves, while still capturing the best model artifacts. Which SageMaker training configuration should the engineer use?

Question 244easymultiple choice

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A data scientist is training a linear regression model using Amazon SageMaker's built-in Linear Learner algorithm. The dataset contains 20 features, and the target variable is a continuous value. The data scientist wants to ensure the model does not overfit. Which hyperparameter should be tuned to control the strength of L2 regularization?

Question 245mediummulti select

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A machine learning engineer is using Amazon SageMaker to train a model with the built-in K-Means algorithm for customer segmentation. The engineer notices that the clusters formed are not meaningful and wants to improve the clustering quality. Which two actions should the engineer take to improve the results? (Choose two.)

Question 246easymultiple choice

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A data scientist is training a k-means clustering model using the SageMaker built-in KMeans algorithm on a dataset with 10 million records and 100 features. The training is taking a long time. Which action will most effectively reduce training time while still producing a reasonable clustering?

Question 247hardmultiple choice

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A data scientist is training a gradient boosting model using Amazon SageMaker's built-in XGBoost algorithm on a dataset with 10,000 rows and 200 features. The model performs well on training data but poorly on validation data. The scientist suspects overfitting. Which hyperparameter tuning strategy is most likely to reduce overfitting?

Question 248mediummulti select

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A data scientist is using Amazon SageMaker to train a model with the built-in K-Means algorithm for customer segmentation. The data scientist wants to ensure the algorithm converges to a good solution and produces meaningful clusters. Which two actions should the data scientist take to improve the quality of the clustering results? (Choose two.)

Question 249easymultiple choice

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A machine learning engineer is using Amazon SageMaker to train a model with the built-in K-Means algorithm. The engineer wants to determine the optimal number of clusters for a dataset of customer behaviors. Which method should the engineer use?

Question 250mediummultiple choice

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A data scientist is using Amazon SageMaker to train a linear learner model for a regression task. The dataset contains 20 features, some of which are highly correlated. After training, the model's performance on the validation set is poor, and the training loss is much lower than the validation loss. The data scientist suspects overfitting. Which action should the data scientist take to reduce overfitting while maintaining model interpretability?

Question 251hardmulti select

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A machine learning team is using Amazon SageMaker to train a model with the built-in k-means algorithm for customer segmentation. They have a dataset with 10,000 records and 20 features. After training, they notice that the clusters are not meaningful and the within-cluster sum of squares (WCSS) is high. They want to improve the clustering results. Which of the following steps should they take? (Choose two.)

Question 252mediummultiple choice

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A machine learning engineer is using Amazon SageMaker to train a deep learning model for image classification. The training job uses a large dataset stored in Amazon S3 and takes several hours. The engineer wants to reduce the training time without sacrificing model accuracy. The model uses a batch size of 32 and a learning rate of 0.001. Which change is MOST likely to reduce training time while maintaining accuracy?

Question 253mediummultiple choice

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A machine learning engineer is using Amazon SageMaker to train a model with the built-in K-Means algorithm on a dataset with 10 million records and 50 features. The training job is taking too long, and the engineer suspects that the algorithm is not converging efficiently. The engineer wants to reduce training time while maintaining reasonable cluster quality. Which action should the engineer take?

Question 254hardmultiple choice

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A machine learning engineer is using Amazon SageMaker to train a deep learning model with the built-in Object Detection algorithm. The training job is using a p3.8xlarge instance and a large dataset stored in Amazon S3. The engineer notices that the GPU utilization is consistently below 30%, and the training job is taking much longer than expected. Which action should the engineer take to improve GPU utilization and reduce training time?

Question 255mediummultiple choice

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A data scientist must train a model on a tabular dataset where the target is the count of insurance claims per policyholder, ranging from 0 to about 15, with many zeros. The team wants a SageMaker built-in algorithm whose objective matches count data. Which approach should the data scientist take?

Question 256hardmultiple choice

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A data scientist is using Amazon SageMaker to train a deep learning model with a custom training script. The script uses TensorFlow and requires a specific version of a Python library that is not included in the default SageMaker TensorFlow container. The scientist needs to ensure the library is available during training. What is the MOST efficient way to accomplish this?

Question 257mediummultiple choice

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A financial institution is building a model to predict loan default. The dataset is highly imbalanced, with only 2% of loans defaulting. The team wants to use Amazon SageMaker to train a binary classification model and ensure that the model performs well on the minority class. Which evaluation metric should they optimize during training?

Question 258hardmulti select

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A machine learning specialist is using Amazon SageMaker to train a deep learning model for image segmentation. The model is trained on a large dataset of high-resolution images. During training, the specialist notices that the GPU memory is exhausted, causing the training job to fail. The specialist wants to reduce GPU memory usage without significantly impacting model accuracy. Which two strategies should the specialist use? (Choose two.)

Question 259hardmulti select

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A data scientist is training a model using Amazon SageMaker's built-in XGBoost algorithm on a dataset with both numerical and categorical features. The model's performance is poor, and the data scientist suspects that the categorical features are not being handled properly. Which two actions should the data scientist take to improve the model's performance? (Choose two.)

Question 260hardmultiple choice

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A data scientist is using Amazon SageMaker to train a deep learning model for natural language processing. The training dataset is stored in Amazon S3 in Parquet format and is approximately 500 GB. The data scientist wants to minimize training time and cost while ensuring the model sees all data in each epoch. Which of the following data input modes and configurations should be used?

Question 261hardmultiple choice

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A machine learning engineer is using Amazon SageMaker to train a model on a dataset with 100 features. After training, the model's performance is good, but inference latency is high. The engineer wants to reduce the number of features without significantly sacrificing accuracy. Which technique should be used?

Question 262easymultiple choice

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A data scientist is using Amazon SageMaker to train a k-means clustering model on a dataset of customer behaviors. The data scientist wants to determine the optimal number of clusters (k) for the model. Which method should the data scientist use to evaluate different values of k and select the best one?

Question 263hardmultiple choice

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A team trains a SageMaker built-in Linear Learner model for a regression task predicting delivery time in minutes. After training, the model's validation root mean squared error is 480 minutes, far worse than a baseline mean predictor. The feature set includes raw timestamps, delivery distance in kilometers, package weight, and a high-cardinality carrier ID. Which change is MOST likely to improve the model?

Question 264mediummultiple choice

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A machine learning engineer is training a binary classifier on a dataset with 1,000,000 examples and 500 features. They use Amazon SageMaker's built-in Linear Learner algorithm in a training job with two ml.c5.4xlarge instances. After training, they notice that the model's validation accuracy is significantly lower than the training accuracy, and the training loss continues to decrease while validation loss starts to increase after 10 epochs. Which technique should they implement to address this issue?

Question 265mediummulti select

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A data scientist is using Amazon SageMaker to train a model for a regression task. The model's predictions are consistently off by a large margin, and the residuals show a pattern. The data scientist suspects that the model is underfitting. Which two actions are most likely to address underfitting? (Choose two.)

Question 266easymultiple choice

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A machine learning engineer has trained a SageMaker model and now needs to evaluate its performance on a held-out test set. The engineer wants to use the SageMaker Python SDK to obtain standard regression metrics such as mean squared error and root mean squared error without writing custom metric code. Which approach should the engineer take?

Question 267mediummulti select

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A machine learning engineer is training a model on Amazon SageMaker and wants to improve model generalization by reducing overfitting. The engineer is using a built-in XGBoost algorithm. Which two hyperparameters should the engineer adjust to reduce overfitting? (Choose two.)

Question 268hardmultiple choice

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A data scientist is using Amazon SageMaker Automatic Model Tuning to optimize a deep learning model. The tuning job has completed 50 training jobs, and the best objective metric is 0.82. The data scientist notices that the tuning job explored only a narrow range of the learning rate and batch size, and many trials used identical hyperparameter values. The tuning job configuration specifies max_jobs=50, max_parallel_jobs=10, and the hyperparameter ranges as continuous. Which change should the data scientist make to improve the exploration of the hyperparameter space?

Question 269mediummultiple choice

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A data scientist is using Amazon SageMaker to train a k-means clustering model on a dataset of customer behaviors. The dataset has 10 numerical features, and the data scientist wants to determine the optimal number of clusters (k). After training models with different values of k, the data scientist plots the within-cluster sum of squares (WCSS) against k and observes an "elbow" at k=4. However, when visualizing the clusters in two dimensions using PCA, the clusters appear overlapping and not well-separated. Which action should the data scientist take to improve cluster separation?

Question 270hardmultiple choice

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A data scientist is tuning a binary classification model using Amazon SageMaker automatic model tuning (AMT). The objective metric is 'validation:auc', and the tuning job has completed 50 training jobs. The data scientist notices that the best training job achieved an AUC of 0.85, but the average AUC across all jobs is 0.70, with high variance. The scientist wants to improve the tuning job's ability to find better hyperparameters without increasing the number of training jobs. Which strategy should the scientist use?

Question 271mediummultiple choice

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A data scientist is using Amazon SageMaker Automatic Model Tuning to optimize a regression model. The objective metric is validation RMSE, and the tuner is configured with the Bayesian optimization strategy. After several hours, the tuner has completed many training jobs but the best objective value has barely improved, and the scientist suspects the search is stuck in a poor region of the hyperparameter space. Which action is MOST likely to improve the tuning results?

Question 272mediummultiple choice

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A machine learning engineer is tuning a gradient boosting model on Amazon SageMaker. The training dataset is small (1,000 rows) and contains 50 features, many of which are correlated. The engineer notices that the model performs well on the training set but poorly on the validation set. Which SageMaker built-in algorithm and hyperparameter configuration is MOST appropriate to reduce overfitting while maintaining interpretability?

Question 273mediummultiple choice

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A machine learning engineer is using Amazon SageMaker to train an image classification model with the built-in Image Classification algorithm. The engineer wants to use transfer learning with a pre-trained model. Which hyperparameter must be set to use a pre-trained model from a different dataset?

Question 274hardmultiple choice

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A machine learning engineer is using Amazon SageMaker to train a deep learning model for image classification. The training dataset is stored in Amazon S3 and consists of 500,000 images, totaling 1 TB. The engineer notices that each epoch takes a very long time and wants to optimize the training speed. The model uses the built-in Image Classification algorithm with a ResNet-50 backbone. Which of the following changes is MOST likely to reduce the training time without significantly affecting model accuracy?

Question 275mediummulti select

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A machine learning engineer is using Amazon SageMaker to train a model with the built-in XGBoost algorithm. They want to reduce overfitting and improve model generalization. Which two hyperparameter adjustments should they consider? (Choose two.)

Question 276mediummultiple choice

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A machine learning engineer is training a linear regression model on a dataset where the target variable has a highly skewed distribution. The team wants to reduce the impact of extreme outliers on the model's coefficients without removing any data points. Which approach should the engineer use?

Question 277easymultiple choice

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A data scientist is using Amazon SageMaker to train a model on a dataset that contains a large number of categorical features with high cardinality. The data scientist wants to reduce the dimensionality of these features before training a linear model. Which SageMaker built-in algorithm is specifically designed for this purpose?

Question 278hardmultiple choice

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A machine learning team is using Amazon SageMaker to train a model with the built-in Linear Learner algorithm on a dataset with 100 features and 1 million samples. The team wants to perform binary classification and needs to handle missing values and categorical features. They also want to automatically tune hyperparameters to maximize the area under the ROC curve (AUC). Which combination of steps should they take to meet these requirements?

Question 279mediummultiple choice

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A machine learning team is using Amazon SageMaker to train a model for predicting customer churn. The training dataset is stored in Amazon S3 and contains 100,000 records with 20 features. The team wants to use the SageMaker Debugger to monitor training and detect issues such as vanishing gradients. They configure a training job with the XGBoost algorithm. Which of the following is TRUE regarding SageMaker Debugger's capabilities for this job?

Question 280hardmultiple choice

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A machine learning engineer is training a deep learning model for image classification using Amazon SageMaker. The model is a ResNet-50 with 25 million parameters, and the training data is stored in Amazon S3 as 500,000 JPEG images. The engineer wants to minimize training time and cost. They plan to use SageMaker's Pipe mode for data input. Which additional configuration should they implement to maximize I/O throughput?

Question 281hardmulti select

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A machine learning team is using Amazon SageMaker to train a deep learning model for image classification. They are using the SageMaker distributed data parallel library to speed up training across multiple GPU instances. They notice that the training throughput does not scale linearly with the number of instances, and they want to optimize the distributed training configuration. Which two actions should they take to improve scaling efficiency? (Choose two.)

Question 282hardmultiple choice

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A team is using Amazon SageMaker Automatic Model Tuning to search for a good XGBoost configuration. They define a hyperparameter as a ContinuousParameter with a range from 0.001 to 1.0, but after several hours the tuning job keeps sampling values clustered near the low end and the best training jobs never explore the upper region. The team wants the search to explore the full range more aggressively in the remaining time. Which change should they make to the tuning job configuration?

Question 283hardmultiple choice

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A team is building a binary classifier for fraudulent transactions using Amazon SageMaker. The dataset has 2 million rows with a 0.4% positive rate. The team wants to maximize recall on the minority class while keeping precision above 0.60, and they need a principled way to set the decision threshold rather than guessing. Which approach best meets these requirements?

Question 284hardmulti select

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A machine learning engineer is using Amazon SageMaker to train a model with the built-in XGBoost algorithm. The engineer wants to improve the model's generalization performance by tuning hyperparameters. Which two hyperparameters should the engineer focus on to control model complexity and prevent overfitting? (Choose two.)

Question 285hardmultiple choice

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A data scientist is training a gradient boosting model using Amazon SageMaker's built-in XGBoost algorithm on a dataset with 1 million rows and 200 features. The training job is taking longer than expected, and the data scientist wants to reduce training time without significantly sacrificing model accuracy. The dataset is stored in Amazon S3 in CSV format. Which action is MOST likely to reduce training time?

Question 286mediummultiple choice

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A data scientist is training a model using Amazon SageMaker's built-in k-means algorithm on a dataset with 10 million records and 100 features. The training job is taking a very long time. The scientist wants to reduce training time without significantly sacrificing model quality. Which action should the scientist take?

Question 287mediummulti select

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A fraud detection team trains a binary classifier in Amazon SageMaker on data where fraudulent transactions are only 0.5% of records. After training, the model shows very high accuracy but poor recall on the fraud class, and the team wants to improve detection of the minority class without collecting new data. Which two actions should they take? (Choose two.)

Question 288hardmultiple choice

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A machine learning engineer is using Amazon SageMaker to train a deep learning model for a natural language processing task. The model is trained on a large corpus and the engineer wants to reduce the vocabulary size by mapping rare words to a special token. Which SageMaker built-in algorithm or feature should the engineer use to achieve this?

Question 289easymultiple choice

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A data scientist is training a linear regression model using Amazon SageMaker's built-in Linear Learner algorithm. The dataset contains a categorical feature with high cardinality (over 10,000 unique values). The data scientist wants to avoid the curse of dimensionality and reduce training time. Which preprocessing step should be applied to the categorical feature before training?

Question 290mediummulti select

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A machine learning team is using Amazon SageMaker to train a binary classification model on a dataset with 100,000 records and 30 features. The team wants to improve model performance by using the built-in XGBoost algorithm. Which two hyperparameters should the team tune to reduce overfitting? (Choose two.)

Question 291mediummultiple choice

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A financial institution is building a fraud detection model using Amazon SageMaker. The dataset is highly imbalanced, with only 0.5% of transactions being fraudulent. The data scientist trains an XGBoost model using the built-in algorithm and evaluates it using accuracy, which is 99.5%. However, the model fails to identify any fraudulent transactions in the test set. The data scientist needs to improve the model's ability to detect fraud. Which approach should be taken?

Question 292hardmultiple choice

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A machine learning engineer is training a deep neural network for image classification using Amazon SageMaker. The training dataset contains 1 million images, and the engineer wants to use data augmentation to improve model generalization. The engineer decides to use the SageMaker TensorFlow estimator with a custom training script. The script currently reads the entire dataset into memory, but the training job fails with an out-of-memory error. The engineer needs to modify the data loading approach to handle the large dataset efficiently. Which solution is MOST appropriate?

Question 293hardmulti select

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A machine learning engineer is using Amazon SageMaker to train a model with the built-in Object2Vec algorithm to generate embeddings for a product recommendation system. The engineer wants to ensure that the embeddings capture meaningful semantic relationships. The training data consists of pairs of products that are frequently bought together. Which two hyperparameters should the engineer tune to improve the quality of the embeddings? (Choose two.)

Question 294hardmulti select

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A company is using Amazon SageMaker to train a deep learning model for image classification. The training job is taking too long, and the data scientist wants to speed up training without sacrificing model accuracy. The training uses a large dataset stored in Amazon S3 and a single GPU instance. Which two actions should the data scientist take to reduce training time? (Choose two.)

Question 295easymultiple choice

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A team is building a product recommendation system using matrix factorization in Amazon SageMaker. They notice that the model's training loss decreases steadily but validation loss starts increasing after 5 epochs. What is the most likely cause?

Question 296mediummultiple choice

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A company is using Amazon SageMaker to train a deep learning model on a large dataset. The training job is taking too long. The team wants to reduce training time without changing the model architecture. Which action should they take?

Question 297mediummultiple choice

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A data scientist is deploying a regression model in Amazon SageMaker that predicts housing prices. The model shows high bias (underfitting). Which action is most likely to reduce bias?

Question 298hardmulti select

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A data scientist is training a deep learning model using Amazon SageMaker. The training loss is decreasing, but the validation loss starts increasing after 10 epochs. The model is overfitting. Which TWO actions should the data scientist take to reduce overfitting? (Choose 2.)

Question 299hardmultiple choice

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An e-commerce company uses a linear regression model to predict customer lifetime value (LTV). The model shows high variance on the test set, with training RMSE much lower than test RMSE. Which of the following is the MOST effective approach to reduce overfitting?

Question 300easymultiple choice

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A company wants to use Amazon SageMaker to train a deep learning model using a custom TensorFlow script. The data is stored in an S3 bucket. Which SageMaker API operation should be used to launch the training job?

Question 301mediummultiple choice

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A company uses an XGBoost model to predict equipment failures. The model has high precision but low recall. The business impact of a false negative is very high (missing a failure). Which action would MOST effectively increase recall while keeping precision reasonably high?

Question 302hardmulti select

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Which THREE techniques can help reduce overfitting in a neural network trained on a small dataset?

Question 303easymultiple choice

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A machine learning team is training a deep learning model on Amazon SageMaker and notices that the training loss is decreasing but the validation loss is increasing. What is the most likely cause?

Question 304hardmultiple choice

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A data scientist is tuning a gradient boosting model using Amazon SageMaker's Automatic Model Tuning (hyperparameter optimization). The objective metric is validation:auc. After 50 training jobs, the best model still has a validation AUC of only 0.65. The scientist suspects overfitting because the training AUC is 0.99. Which hyperparameter configuration is MOST likely to reduce overfitting?

Question 305mediummulti select

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A data scientist is training a neural network for image classification. The training loss is not decreasing significantly, and the validation loss is high. Which TWO actions should the scientist take to address potential vanishing gradients?

Question 306hardmulti select

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A company is using Amazon SageMaker to train a large language model. The training job is taking too long. The data scientist wants to reduce training time without sacrificing model accuracy. Which THREE strategies are MOST appropriate?

Question 307mediummultiple choice

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A company is building a recommendation system for an e-commerce platform. They have user-item interaction data and want to use matrix factorization. However, the dataset is sparse (99% missing interactions). Which approach should the data scientist take to train the model effectively?

Question 308mediummultiple choice

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A financial services company is building a model to detect fraudulent credit card transactions. The dataset contains 1 million transactions, with only 0.1% labeled as fraud. The data scientist trains a logistic regression model on the raw dataset and obtains the following results on a held-out test set: accuracy = 99.8%, precision = 50%, recall = 60%, F1 = 0.545. The business requirement is to maximize recall while keeping precision above 80%. Which course of action should the data scientist take to improve the model?

Question 309easymultiple choice

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A data scientist is training a linear regression model to predict house prices. The dataset includes features such as square footage, number of bedrooms, and location. After training, the model achieves an R² of 0.85 on the training set but only 0.60 on the test set. Which of the following is the MOST likely cause of this discrepancy?

Question 310hardmulti select

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A machine learning team is building a multi-class image classifier using a pre-trained ResNet-50 model in Amazon SageMaker. The dataset has 10 classes but is highly imbalanced, with one class representing 80% of the samples. The team wants to improve model performance on the minority classes. Which TWO of the following approaches are most likely to help? (Select TWO.)

Question 311mediumdrag order

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Drag and drop the steps to deploy a model as a SageMaker endpoint for real-time inference in the correct order.

Drag or tap steps into the slots.

Steps

Order

1Step 1

2Step 2

3Step 3

4Step 4

5Step 5

Question 312mediummatching

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Match each hyperparameter tuning strategy to its description.

Drag a concept onto its matching description — or click a concept then click the description.

Concepts

Matches

Exhaustive search over specified hyperparameter values

Random sampling of hyperparameter combinations

Probabilistic model to guide search

Early stopping and resource allocation

SageMaker automatic tuning

Question 313mediummultiple choice

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A machine learning team is deploying a model that performs real-time inference on streaming data from Amazon Kinesis Data Streams. The model requires sub-100ms latency. Which deployment option should the team choose?

Question 314hardmultiple choice

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A data scientist is training a deep learning model on a large dataset using Amazon SageMaker. The training job is taking too long. The scientist notices that GPU utilization is low and data loading is the bottleneck. Which action should the scientist take to improve training performance?

Question 315easymultiple choice

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A data scientist is building a regression model to predict house prices. The dataset contains many features, some of which are highly correlated. The model is overfitting. Which regularization technique should the scientist use to penalize large coefficients and perform feature selection?

Question 316hardmultiple choice

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A data scientist is working on a multi-class classification problem with 10 classes. The model outputs probabilities and the scientist wants to evaluate the model's ability to rank classes correctly. Which metric is most appropriate?

Question 317easymultiple choice

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A machine learning engineer is deploying a model using Amazon SageMaker and wants to automatically scale the endpoint based on the number of incoming requests. Which scaling policy should be used?

Question 318mediummultiple choice

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A data scientist is training a neural network on a dataset with 1 million images. The training loss decreases steadily but the validation loss starts to increase after 10 epochs. Which action should the scientist take to improve generalization?

Question 319hardmulti select

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Which THREE techniques are effective for reducing overfitting in a deep neural network?

Question 320mediummultiple choice

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Refer to the exhibit. A data scientist is assigned an IAM policy to deploy a SageMaker model. When the scientist tries to create an endpoint, the action fails with an authorization error. What is the missing permission?

Exhibit

Refer to the exhibit.

```
{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Effect": "Allow",
            "Action": [
                "sagemaker:CreateTrainingJob",
                "sagemaker:CreateModel",
                "sagemaker:CreateEndpointConfig",
                "sagemaker:CreateEndpoint"
            ],
            "Resource": "*"
        }
    ]
}
```

Question 321mediummultiple choice

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A data scientist is training a binary classifier using logistic regression on a dataset that is highly imbalanced (95% negative class, 5% positive class). The model achieves 95% accuracy but only predicts the negative class. Which metric should the scientist use to evaluate the model's performance on the positive class?

Question 322hardmultiple choice

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A team is training a deep learning model on Amazon SageMaker using a large dataset stored in S3. The training job is taking a long time, and the team suspects that data loading is the bottleneck. The dataset consists of many small files (average size 10KB). Which change would most effectively reduce the I/O bottleneck?

Question 323easymultiple choice

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A company is using Amazon SageMaker to deploy a machine learning model for real-time inference. The model was trained using XGBoost and achieves high accuracy. However, during deployment, the endpoint returns a 'ModelError' when receiving input data. The input is a CSV string. What is the most likely cause?

Question 324mediummultiple choice

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A data scientist is using Amazon SageMaker to perform hyperparameter tuning for a neural network. The tuning job uses the 'Random' search strategy. After 10 training jobs, the best objective metric has plateaued. The scientist wants to improve the results without increasing the total number of training jobs. Which approach should they take?

Question 325easymultiple choice

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A data scientist is training a random forest model on a dataset with 50 features. After training, the model achieves 98% accuracy on the training set but only 85% on the test set. Which technique is most appropriate to reduce the generalization error?

Question 326easymultiple choice

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A company uses Amazon SageMaker to host a model for real-time predictions. The model endpoint is experiencing high latency during peak hours. The data scientist wants to reduce latency without increasing cost. Which action should they take?

Question 327mediummulti select

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A data scientist is training a linear regression model on a dataset with 10 numerical features. After training, the model's R-squared value is 0.99 on the training set but only 0.60 on the test set. Which TWO of the following are appropriate actions to reduce overfitting? (Choose TWO.)

Question 328easymulti select

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A data scientist is training a k-means clustering model on a dataset with 1,000 points. The scientist uses the elbow method to choose the number of clusters. The elbow plot shows a clear bend at k=4. After running k-means with k=4, the scientist wants to evaluate the quality of the clustering. Which THREE of the following are suitable internal clustering validation metrics? (Choose THREE.)

Question 329mediummultiple choice

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A data scientist is training a binary classification model on imbalanced data (95% negative, 5% positive). The model achieves 99% accuracy on the test set but fails to detect any positive cases. Which metric should the scientist focus on to evaluate model performance?

Question 330hardmultiple choice

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A machine learning engineer is deploying a model for real-time inference using Amazon SageMaker. The model is a large ensemble that requires 8 GB of memory and 4 vCPUs. The expected traffic is 100 requests per second with a 200 ms latency requirement. Which instance configuration should they choose?

Question 331easymultiple choice

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A data scientist is using Amazon SageMaker to train a linear regression model. The training data has 10 features, and the scientist wants to interpret the model's coefficients. Which algorithm should they use?

Question 332hardmultiple choice

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A data scientist is training an LSTM model for time series forecasting using Amazon SageMaker. The model is overfitting. Which action is LEAST likely to reduce overfitting?

Question 333mediummultiple choice

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A company is using Amazon SageMaker to deploy a model for real-time inference. The model requires 500 MB of memory and has a latency requirement of 100 ms. The endpoint is receiving 10 requests per second. Which instance type should be chosen for cost-effectiveness?

Question 334easymultiple choice

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A data scientist is using Amazon SageMaker to train a model. The training job fails with an error 'Insufficient instance capacity'. Which action should the scientist take to resolve this?

Question 335hardmulti select

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A company is using Amazon SageMaker to deploy a model for real-time inference. The model is a deep neural network that requires GPU for low latency. The endpoint currently uses a single ml.p3.2xlarge instance. Traffic is expected to increase by 5x. Which TWO actions should the company take to handle the increased traffic?

Question 336mediummultiple choice

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A data scientist is training a binary classification model on an imbalanced dataset where the positive class represents 5% of the data. The model achieves 99% accuracy but only identifies 10% of the actual positive cases. Which metric should the data scientist focus on to evaluate the model's performance on the positive class?

Question 337hardmulti select

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An ML team trains a deep learning model using Amazon SageMaker with a custom Docker container. Training completes successfully, but the model's accuracy on the test set is significantly lower than expected. The team suspects overfitting. Which two actions should they take to mitigate overfitting? (Choose TWO.)

Question 338mediummultiple choice

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A data scientist is using Amazon SageMaker to train a linear regression model. The training job fails with the error: 'AlgorithmError: Input data has NaN values'. Which step should the data scientist take to resolve this issue?

Question 339easymultiple choice

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A company is building a recommendation system using collaborative filtering. The dataset contains implicit feedback (clicks) from users on items. Which algorithm is best suited for this scenario?

Question 340mediummultiple choice

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A data scientist is training a gradient boosting model using SageMaker's built-in XGBoost algorithm. The model is overfitting on the training data. Which hyperparameter adjustment is most likely to reduce overfitting?

Question 341easymultiple choice

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A machine learning team is developing a model to predict housing prices. They have a dataset with numerical features like square footage and number of bedrooms, and categorical features like neighborhood. Which preprocessing step is essential before training a linear regression model?

Question 342mediummultiple choice

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A data scientist is using Amazon SageMaker to train a deep learning model on a large dataset stored in S3. The training job is taking too long. The data scientist wants to reduce training time without changing the model architecture. Which action should they take?

Question 343mediummulti select

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A data scientist is building a text classification model using a bag-of-words approach. The dataset contains 100,000 documents with a vocabulary of 50,000 unique words. The model is overfitting. Which THREE techniques can help reduce overfitting? (Choose THREE.)

Question 344mediummultiple choice

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A company is training a deep learning model on SageMaker using a large dataset stored in S3. The training job is taking a long time due to I/O bottlenecks. Which action would MOST effectively reduce the I/O bottleneck?

Question 345mediummultiple choice

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A machine learning engineer is deploying a PyTorch model on SageMaker for real-time inference. The model requires GPU for low latency. Which instance type and configuration should the engineer choose?

Question 346hardmultiple choice

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A data scientist is using SageMaker to train a random forest model. The dataset has 100 features and 1 million rows. The training job fails with a 'ResourceLimitExceeded' error. What is the MOST likely cause?

Question 347easymultiple choice

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A company wants to use Amazon SageMaker to automatically tune hyperparameters for a XGBoost model. Which built-in SageMaker feature should be used?

Question 348mediummultiple choice

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A data scientist trains a model using SageMaker and notices that the training loss decreases but validation loss increases after a few epochs. What is the MOST likely issue?

Question 349hardmultiple choice

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A company is using SageMaker to train a deep learning model with TensorFlow. The training job is running on an ml.p3.16xlarge instance. The data scientist wants to maximize GPU utilization. Which configuration should be used?

Question 350mediummulti select

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A data scientist is training a deep learning model on SageMaker using a custom container. The training job fails with an 'OutOfMemory' error. Which THREE actions could resolve this issue? (Choose 3.)

Question 351mediummultiple choice

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A data scientist is training a binary classification model on a dataset with 100 features and 10,000 samples. The model achieves 99% accuracy on the training set but only 65% on the test set. Which technique should be applied first to address this issue?

Question 352easymultiple choice

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A machine learning team needs to deploy a model that makes real-time predictions with latency under 100 ms. The model is a deep neural network with 500 MB of parameters. Which AWS service should they use?

Question 353hardmultiple choice

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A data scientist is building a time series forecasting model for daily sales data. The data exhibits strong seasonality with a weekly pattern and a yearly trend. The scientist wants to use Amazon SageMaker's built-in algorithm. Which algorithm is most appropriate?

Question 354easymultiple choice

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A company is training a large language model on Amazon SageMaker using a single GPU instance. The training is taking too long. Which change would most likely reduce training time?

Question 355mediummultiple choice

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A data scientist is using Amazon SageMaker to train a linear regression model. After training, the scientist notices that the model has a high bias. What is the most likely cause?

Question 356hardmultiple choice

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A team is deploying a model for fraud detection. The dataset is highly imbalanced (99% legitimate, 1% fraudulent). They trained a logistic regression model and achieved 99% accuracy on the test set. However, the model fails to detect most fraud cases. Which metric should the team focus on to evaluate the model?

Question 357mediummultiple choice

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A company wants to use Amazon SageMaker to train a model using a custom algorithm packaged in a Docker container. Which approach should they use?

Question 358easymultiple choice

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A data scientist is training a neural network on Amazon SageMaker and wants to automatically stop training if the validation loss does not improve for 5 consecutive epochs. Which feature should they use?

Question 359hardmultiple choice

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A team is building a model to predict customer churn. They have 50 features, including categorical variables with high cardinality (e.g., zip code with 10,000 unique values). Which feature engineering technique is most appropriate?

Question 360mediummulti select

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A data scientist is training a random forest classifier on Amazon SageMaker and wants to reduce overfitting. Which TWO actions should the scientist take? (Choose TWO.)

Question 361hardmulti select

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A company is using Amazon SageMaker to deploy a model for real-time inference. The model takes 200 ms to respond, but the requirement is 100 ms. Which THREE actions could reduce latency? (Choose THREE.)

Question 362easymulti select

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A data scientist is performing feature selection for a linear regression model. Which TWO methods are appropriate? (Choose TWO.)

Question 363mediummultiple choice

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Refer to the exhibit. An IAM policy is attached to a SageMaker notebook instance. The data scientist runs a training job that reads from s3://my-bucket/training-data/ and writes to s3://my-bucket/output/. The training job fails with an access denied error. What is the most likely cause?

Exhibit

Refer to the exhibit.

```
{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Effect": "Allow",
            "Action": [
                "s3:GetObject",
                "s3:PutObject"
            ],
            "Resource": "arn:aws:s3:::my-bucket/training-data/*"
        },
        {
            "Effect": "Allow",
            "Action": [
                "sagemaker:CreateTrainingJob",
                "sagemaker:CreateModel"
            ],
            "Resource": "*"
        }
    ]
}
```

Question 364hardmultiple choice

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Refer to the exhibit. A custom training job using Pipe input mode fails. The logs indicate the algorithm cannot read the data. What is the most likely issue?

Network Topology

Question 365easymultiple choice

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Refer to the exhibit. A data scientist wants to update the endpoint to use a new model image. The scientist updates the endpoint configuration with the new image and calls UpdateEndpoint. After the update, the endpoint status is 'Updating' but remains in that state for a long time. What is the most likely cause?

Network Topology

Question 366mediummultiple choice

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A data scientist is training a binary classification model on a dataset with 100,000 positive samples and 1,000 negative samples. The model achieves 99% accuracy on the test set but a very low F1 score. What is the most likely cause?

Question 367easymultiple choice

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A machine learning engineer needs to deploy a real-time inference endpoint for a model that requires GPU acceleration for low latency. Which AWS service should be used?

Question 368hardmultiple choice

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A data scientist is training a deep learning model on Amazon SageMaker and notices that training is taking much longer than expected. The training job uses a single GPU instance. The model is a large transformer with millions of parameters. Which change would most likely reduce training time?

Question 369mediummulti select

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A data scientist is building a regression model to predict housing prices. The dataset includes numerical features such as square footage, number of bedrooms, and year built, as well as categorical features such as neighborhood and roof type. Which TWO preprocessing steps are most important to apply before training a linear regression model?

Question 370hardmulti select

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A machine learning engineer is evaluating a multi-class classification model that predicts product categories. The model outputs probabilities for 10 classes. The engineer wants to improve the model's calibration so that the predicted probabilities reflect the true likelihood of each class. Which THREE techniques can help?

Question 371hardmultiple choice

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A company is using Amazon SageMaker to train a large language model with billions of parameters. The training job uses multiple GPU instances in a distributed fashion. The training is converging but the loss is not decreasing as expected. The data scientist suspects that the learning rate is too high. Which technique should the data scientist use to automatically adjust the learning rate during training?

Question 372mediummultiple choice

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A data scientist is building a recommendation system using collaborative filtering. The dataset contains user-item interactions in a sparse matrix. The model will be trained on Amazon SageMaker using the built-in Factorization Machines algorithm. Which data format should the scientist use for the training data?

Question 373mediummultiple choice

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A machine learning engineer is deploying a model to an Amazon SageMaker endpoint for real-time inference. The model requires a preprocessing step that involves tokenizing text and converting it to a numerical format. To minimize latency, where should the preprocessing logic be implemented?

Question 374hardmultiple choice

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A data scientist is training a deep learning model for image classification on Amazon SageMaker. The dataset consists of 10,000 images of size 224x224 pixels. The training job uses a single ml.p3.2xlarge instance. The data scientist notices that the GPU utilization is very low (~20%) and the training is slow. Which change would most likely improve GPU utilization?

Question 375easymultiple choice

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A data scientist is training a binary classification model on an imbalanced dataset where the positive class represents 5% of the data. Which metric is most appropriate for evaluating model performance?

Question 376mediummultiple choice

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A team is using Amazon SageMaker to train a deep learning model for image classification. The training job is taking too long, and they want to reduce training time without sacrificing model accuracy. Which approach is most effective?

Question 377hardmultiple choice

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A machine learning engineer is tuning hyperparameters for a gradient boosting model using Amazon SageMaker Automatic Model Tuning. The objective metric is validation accuracy. After several tuning jobs, the best accuracy achieved is 0.85, but the engineer suspects the model is overfitting. Which hyperparameter adjustment is most likely to reduce overfitting?

Question 378mediummultiple choice

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A data scientist needs to choose an algorithm for a regression problem with 50 features and 1 million training examples. The model must be interpretable and the training data fits in memory. Which algorithm is most appropriate?

Question 379easymultiple choice

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A company is building a sentiment analysis model for customer reviews. The dataset includes 10,000 positive and 10,000 negative reviews. The data scientist splits the data into 70% training, 15% validation, and 15% test sets. After training, the model achieves 99% accuracy on training set but only 82% on validation set. What is the most likely issue?

Question 380hardmultiple choice

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A company uses a linear regression model to predict house prices. The model's R-squared is 0.95 on the training set but 0.60 on the test set. Which of the following is the most likely cause?

Question 381easymultiple choice

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A data scientist needs to perform feature scaling for a dataset containing numerical features with different units (e.g., age in years and income in dollars). Which scaling method is most appropriate when the algorithm assumes data is normally distributed?

Question 382hardmulti select

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Which THREE of the following are appropriate methods to reduce overfitting in a decision tree model?

Question 383easymulti select

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Which TWO of the following are true about the bias-variance tradeoff?

Question 384mediummultiple choice

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A team deployed a SageMaker endpoint with the configuration shown in the exhibit. During a traffic spike, the endpoint becomes unresponsive. Which change to the endpoint configuration would best improve availability?

Network Topology

Question 385hardmultiple choice

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A machine learning engineer is using Amazon SageMaker to train a deep learning model. The training job is taking longer than expected. The engineer notices that the GPU utilization is low (around 30%) while CPU utilization is high. Which action is most likely to improve training speed?

Question 386mediummultiple choice

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A data scientist is building a regression model to predict house prices. The dataset includes features such as square footage, number of bedrooms, and location. After training a linear regression model, the scientist notices that the residuals have a pattern: they increase as the predicted value increases. Which action is most appropriate?

Question 387hardmultiple choice

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A machine learning team is using Amazon SageMaker to train a large language model. The training script uses PyTorch and the model requires significant memory. The team wants to use model parallelism across multiple GPUs. Which SageMaker feature should they use?

Question 388hardmultiple choice

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A machine learning team is using Amazon SageMaker to train a model using a custom Docker container. The training job fails with an error: 'Unable to write to /opt/ml/model'. The container does not have root access. What is the most likely cause?

Question 389easymultiple choice

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A data scientist is building a classification model to predict customer churn. The dataset has 10,000 samples with 100 features. After training a logistic regression model, the scientist observes that the model has high variance (overfitting). Which technique can reduce overfitting?

Question 390mediummulti select

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Which TWO of the following are valid approaches to handle missing values in a dataset for a machine learning model?

Question 391hardmulti select

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Which THREE of the following are best practices for training a deep learning model on Amazon SageMaker?

Question 392mediummultiple choice

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A data scientist is training a neural network on image data using TensorFlow with GPU instances on SageMaker. The training is slow because the GPU utilization is low. The data pipeline uses tf.data with a large number of preprocessing operations. Which action would most likely increase GPU utilization?

Question 393easymultiple choice

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A data scientist is using Amazon SageMaker to train a linear regression model. The dataset has 500 features and 50,000 observations. The model converges but has high bias. Which technique should the data scientist use to reduce bias?

Question 394hardmultiple choice

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A data science team is deploying a machine learning model to production using SageMaker. The model is a PyTorch model that requires custom inference logic including image preprocessing. The team needs to ensure that the endpoint can handle variable batch sizes and has low latency. Which deployment approach should the team use?

Question 395easymultiple choice

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A data scientist trains a convolutional neural network (CNN) for image classification. The training loss decreases steadily, but the validation loss starts increasing after 10 epochs. Which technique should the data scientist use to address this problem?

Question 396hardmulti select

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A data scientist is using Amazon SageMaker to train a deep learning model for natural language processing. The training job is taking too long to converge. The data scientist wants to speed up training without significantly sacrificing model accuracy. Which THREE strategies should the data scientist consider? (Choose three.)

Question 397easymulti select

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A data scientist is evaluating a binary classification model that predicts whether a customer will churn. The model achieves an AUC of 0.85 on the test set. Which TWO statements about AUC are correct? (Choose two.)

Question 398easymultiple choice

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A data scientist trains a linear regression model to predict house prices. The model has high bias (underfitting). Which action is most likely to reduce bias?

Question 399mediummultiple choice

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A data scientist builds a Random Forest model using SageMaker. The model performs well on training data but poorly on test data. Which step is most likely to reduce overfitting?

Question 400easymultiple choice

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A data scientist needs to implement a recommendation system for an e-commerce website. Which Amazon service is specifically designed for building and deploying recommendation models?

Question 401hardmultiple choice

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A data scientist trains a neural network using TensorFlow on SageMaker. The training job fails with a 'CUDA out of memory' error. What is the most likely cause and solution?

Question 402mediummulti select

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Which TWO metrics are suitable for evaluating a regression model? (Select TWO.)

Question 403hardmulti select

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Which THREE techniques help reduce overfitting in a neural network? (Select THREE.)

Question 404easymulti select

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Which TWO services can be used to serve machine learning models for real-time inference? (Select TWO.)

Question 405easymultiple choice

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A data scientist is training a binary classifier on an imbalanced dataset (95% negative, 5% positive). The model achieves 99% accuracy but only correctly identifies 2% of the positive samples. Which metric should the data scientist focus on to improve the model's performance?

Question 406easymultiple choice

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A data scientist is building a text classification model. The dataset contains 10,000 documents, each labeled with one of 5 categories. Which algorithm is most suitable for this task?

Question 407hardmultiple choice

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A team is training a neural network for image classification using Amazon SageMaker. The training loss decreases rapidly but the validation loss starts increasing after a few epochs. Which action should the team take?

Question 408easymultiple choice

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A data scientist is using Amazon SageMaker to train a linear regression model. The target variable is right-skewed. Which transformation should the data scientist apply to the target variable to improve model performance?

Question 409mediummultiple choice

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A company wants to deploy a machine learning model that predicts customer churn. The model must provide interpretable predictions to explain why a customer is likely to churn. Which algorithm is most appropriate?

Question 410hardmultiple choice

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A data scientist is tuning hyperparameters for an XGBoost model on a large dataset using Amazon SageMaker. The training job is taking too long, and they want to speed up the tuning process. Which strategy is most effective?

Question 411easymulti select

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Which TWO of the following are common techniques to handle missing values in a dataset?

Question 412mediummulti select

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Which THREE of the following are valid metrics for evaluating a regression model?

Question 413hardmulti select

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Which TWO of the following are techniques used to reduce overfitting in a neural network?

Question 414easymultiple choice

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A data scientist is training a binary classification model on imbalanced data (95% negative, 5% positive). The model achieves 95% accuracy but only 10% recall on the positive class. Which metric should be used to evaluate model performance?

Question 415mediummultiple choice

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A company uses SageMaker to train a large language model. The training job is taking too long. The data scientist wants to use distributed training with data parallelism. Which SageMaker feature should be used?

Question 416easymultiple choice

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A machine learning engineer is deploying a model to SageMaker for real-time inference. The model is a TensorFlow SavedModel. Which SageMaker capability should be used to create an endpoint?

Question 417mediummultiple choice

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A data scientist is training a deep learning model on a GPU instance. The training loss is decreasing, but the validation loss starts increasing after a few epochs. Which action should the data scientist take to address this?

Question 418hardmultiple choice

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A data scientist is building a recommendation system for an e-commerce platform. The dataset contains user interactions (clicks, purchases) and item metadata. The scientist wants to use matrix factorization. Which algorithm should be used?

Question 419hardmultiple choice

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A machine learning engineer is deploying a PyTorch model to SageMaker. The model requires custom inference logic. Which approach should the engineer use?

Question 420mediummulti select

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Which THREE techniques can help reduce overfitting in a neural network? (Choose 3)

Question 421mediummultiple choice

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A data scientist is training a binary classifier on an imbalanced dataset where the positive class represents only 2% of the data. The model achieves 99% accuracy but only identifies 5% of actual positives. Which metric should the scientist use to evaluate the model's ability to detect the positive class?

Question 422hardmultiple choice

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A team is training a large deep learning model on Amazon SageMaker. The training job is taking too long and they want to reduce training time without changing the model architecture. Which action is MOST effective?

Question 423easymultiple choice

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A machine learning engineer needs to deploy a model that makes real-time predictions with latency under 100ms. The model is a small ensemble of decision trees. Which AWS service is MOST suitable?

Question 424mediummultiple choice

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A data scientist is using Amazon SageMaker to train a model. The training data is stored in an S3 bucket encrypted with AWS KMS. During training, the job fails with an access denied error. What is the MOST likely cause?

Question 425hardmultiple choice

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A team is training a deep learning model using TensorFlow on a single GPU instance in SageMaker. The GPU utilization is below 30%. Which change will MOST improve GPU utilization?

Question 426easymultiple choice

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A machine learning team is using Amazon SageMaker to build a regression model. The target variable is heavily right-skewed with a long tail. Which data transformation should the team apply to the target variable before training?

Question 427hardmultiple choice

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A data scientist is using Amazon SageMaker to train a model with a large dataset that does not fit into memory on a single instance. The training algorithm supports distributed training. Which approach should the scientist use to train the model efficiently?

Question 428mediummultiple choice

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A machine learning engineer is monitoring a deployed model on SageMaker and notices that the prediction latency is increasing over time. The model is a linear regression with a small number of features. Which is the MOST likely cause?

Question 429hardmulti select

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A company uses a SageMaker endpoint for real-time inference. They need to ensure high availability during deployment updates. Which THREE steps achieve this? (Choose 3)

Question 430easymulti select

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A data scientist is building a classification model and wants to evaluate its performance. Which TWO metrics are appropriate for a multi-class classification problem? (Choose 2)

Question 431mediummultiple choice

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A company is training a deep learning model on Amazon SageMaker. The training job is taking a long time and the data scientist suspects that the model is overfitting. Which of the following actions can help reduce overfitting and improve generalization?

Question 432hardmultiple choice

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A data scientist is building a binary classification model to predict customer churn. The dataset is highly imbalanced, with only 5% of customers churning. The scientist evaluates several models using accuracy, precision, recall, and F1 score. Which metric is most appropriate for comparing model performance in this scenario?

Question 433easymultiple choice

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A company is using Amazon SageMaker to train a linear regression model. The data scientist notices that the training loss is decreasing but the validation loss has started to increase after a few epochs. What is the most likely cause?

Question 434mediummultiple choice

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A data scientist is using Amazon SageMaker built-in XGBoost algorithm to train a regression model. The training job completes successfully but the model performance on the test set is poor, with high bias. Which hyperparameter adjustment is most likely to help reduce bias?

Question 435hardmultiple choice

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A data scientist is training a neural network on Amazon SageMaker. The network has many layers and the training is very slow. The scientist suspects that the gradients are vanishing. Which technique is most specifically designed to mitigate the vanishing gradient problem?

Question 436easymultiple choice

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A data scientist is using Amazon SageMaker to train a classification model. The dataset contains categorical features with high cardinality. Which encoding method is most appropriate for handling high-cardinality categorical features in a linear model?

Question 437mediummulti select

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A data scientist is building a deep learning model using Amazon SageMaker. The model is overfitting the training data. Which THREE actions can help reduce overfitting?

Question 438easymultiple choice

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A data scientist wants to use a linear regression model to predict house prices. After training, the model shows high bias and low variance. Which action would most likely improve the model's performance?

Question 439hardmultiple choice

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A company uses SageMaker to deploy a model for real-time inference. The model is a large ensemble that requires 8 GB of memory and has high latency. The team wants to reduce latency without increasing cost. Which strategy is most effective?

Question 440easymultiple choice

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During training, a binary classification model has an AUC of 0.99 on the training set but only 0.72 on the validation set. Which of the following is the most likely cause?

Question 441mediummultiple choice

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A data scientist is using Amazon SageMaker to train a model using a built-in algorithm. The training job fails with an error indicating that the algorithm expects the data to be in recordIO-protobuf format, but the input is CSV. What is the most efficient way to resolve this?

Question 442hardmultiple choice

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A data scientist is training a recurrent neural network (RNN) for time series forecasting. The model's training loss is not decreasing, and the gradients are vanishing. Which technique should the scientist apply to address vanishing gradients?

Question 443mediummultiple choice

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A team is using SageMaker to train a model with hyperparameter tuning. The training jobs are taking too long. The team wants to reduce time without sacrificing model quality. Which approach should they take?

Question 444hardmultiple choice

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A data scientist is using Amazon SageMaker to deploy a custom model container. The model is a large transformer that requires 16 GB of memory. The scientist wants to minimize inference latency. Which SageMaker hosting option should they choose?

Question 445hardmulti select

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Which TWO techniques are used to handle missing values in a dataset before training? (Choose 2.)

Question 446mediummultiple choice

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A data scientist is training a binary classification model on an imbalanced dataset where the positive class represents only 5% of the data. The model currently achieves 95% accuracy but only 10% recall on the positive class. Which metric should the scientist focus on to improve the model's ability to detect the positive class?

Question 447mediummultiple choice

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An e-commerce company wants to build a recommendation system. They have user-item interaction data (clicks, purchases) and user demographic data. The goal is to recommend items that a user is likely to purchase. Which approach should be used?

Question 448hardmultiple choice

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A data scientist is training a deep learning model for image classification using TensorFlow on Amazon SageMaker. The model trains slowly, and the GPU utilization is below 20%. Which action will MOST effectively increase GPU utilization and reduce training time?

Question 449easymultiple choice

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A company uses Amazon SageMaker to train a model and wants to track metrics like loss and accuracy in real-time. Which SageMaker feature should be used?

Question 450mediummultiple choice

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A data scientist is using Amazon SageMaker to build a text classification model. The dataset has 100,000 labeled samples and 20 classes. The scientist wants to use a pre-trained BERT model and fine-tune it. Which approach is MOST cost-effective?

Question 451mediummultiple choice

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A data scientist is training a regression model. The training loss is decreasing but the validation loss starts to increase after a few epochs. Which technique should the scientist use to address this issue?

Question 452mediummulti select

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Which TWO of the following are best practices for hyperparameter tuning using Amazon SageMaker? (Choose 2)

Question 453hardmulti select

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Which THREE of the following are valid strategies to reduce overfitting in a deep neural network? (Choose 3)

Question 454easymulti select

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Which TWO of the following are appropriate use cases for using Amazon SageMaker BlazingText? (Choose 2)

Question 455easymultiple choice

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A retail company uses Amazon SageMaker to train a model for product demand forecasting. The dataset contains daily sales data for 10,000 products over 3 years. The data includes features like price, promotions, holidays, and seasonality. The data scientist uses a linear regression model and gets an RMSE of 50 units. However, the business requires more accurate forecasts, especially for products with high variability. The scientist notices that the residuals show a pattern: the model underestimates demand during promotional periods. Which approach should the scientist take to improve the model?

Question 456hardmultiple choice

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A machine learning engineer is using SageMaker to train an XGBoost model on a dataset with a severe class imbalance (1:1000). The goal is to maximize recall on the minority class. Which hyperparameter tuning strategy is MOST appropriate?

Question 457easymultiple choice

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A data scientist is evaluating a regression model. The RMSE on the training set is 2.5, and on the test set is 2.7. The R² on the test set is 0.98. What does this indicate?

Question 458mediummultiple choice

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A company uses SageMaker to host a real-time inference endpoint for a classification model. The endpoint receives traffic spikes that cause high latency. The team wants a solution that automatically scales based on demand while keeping costs low. Which approach is BEST?

Question 459easymultiple choice

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A data scientist is building a binary classifier and obtains the following confusion matrix on the test set: TP=80, FP=20, TN=70, FN=30. What is the precision?

Question 460mediummultiple choice

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A team is training a large language model using SageMaker's distributed training. They notice that the training loss is not decreasing after the first few epochs. Which action is MOST likely to resolve this issue?

Question 461hardmultiple choice

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A company is deploying a model that predicts customer churn. The model's recall for the churn class is 0.9, but precision is 0.4. The business cost of false positives is high. Which strategy would MOST likely improve precision without significantly harming recall?

Question 462mediummulti select

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A data scientist is training a linear regression model and wants to handle multicollinearity among features. Which TWO actions are appropriate?

Question 463easymulti select

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A data scientist is evaluating a binary classification model. The model's AUC-ROC is 0.95. Which TWO statements are true?

Question 464mediummultiple choice

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A data scientist is working on a regression problem to predict house prices. The dataset has 80 features, including categorical variables with high cardinality (e.g., zip code with 10,000 unique values). The target variable is log-transformed. The data scientist trains a linear regression model and obtains an R² of 0.45 on the test set. To improve performance, the data scientist considers: A) Applying one-hot encoding to all categorical features and using Ridge regression. B) Using target encoding for high-cardinality features and using a tree-based model like XGBoost. C) Removing all categorical features and using polynomial features for numerical features. D) Using principal component analysis (PCA) on all features before training a linear model. Which approach is MOST likely to improve the model's performance?

Question 465easymultiple choice

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A data scientist is using Amazon SageMaker to train a model with the built-in XGBoost algorithm. The dataset contains missing values. What is the default behavior of SageMaker XGBoost regarding missing values?

Question 466hardmultiple choice

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A data scientist is using Amazon SageMaker Autopilot to automatically build a model for a regression problem. The dataset has 100 features and 50,000 rows. Autopilot recommends a model with an R² of 0.85 on the validation set. However, when deployed to production, the model performs poorly (R² of 0.2). What is the most likely cause?

Question 467mediummultiple choice

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A data scientist is training a recurrent neural network (RNN) for time series forecasting. The training loss decreases steadily for the first 10 epochs, then plateaus. The validation loss starts increasing after epoch 10. What is the most appropriate action?

Question 468easymulti select

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A data scientist is using Amazon SageMaker to train a linear regression model. The dataset has outliers. Which TWO techniques can help reduce the impact of outliers? (Choose TWO.)

Question 469mediummulti select

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A data scientist is tuning a random forest model using SageMaker Hyperparameter Tuning. The objective metric is validation:accuracy. Which THREE hyperparameters are most commonly tuned for random forest? (Choose THREE.)

Question 470hardmulti select

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A company is using SageMaker to train a TensorFlow model for image classification. The training is slow on a single GPU instance. Which TWO strategies can reduce training time? (Choose TWO.)

Question 471mediummultiple choice

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A data science team is using Amazon SageMaker to train a deep learning model for object detection using the built-in SSD algorithm. The dataset consists of 100,000 labeled images stored in a SageMaker Pipe Mode input. The training job uses a single ml.p3.2xlarge instance. After 2 hours, the training job fails with the error 'ResourceLimitExceeded: The account-level service limit for ml.p3.2xlarge for training job usage is 1. Contact AWS Support to request a limit increase'. However, the team has already submitted a limit increase request and it was approved for 5 instances. What is the most likely cause of the error?

Question 472easymultiple choice

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A startup is deploying a machine learning model for real-time recommendation on Amazon SageMaker. The model is a TensorFlow model (1 GB) and the endpoint uses a single ml.c5.2xlarge instance. The inference latency is currently 500 ms per request. The startup expects traffic to increase 10x in the next month. They want to maintain latency under 500 ms. What is the most cost-effective solution?

Question 473mediummulti select

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A company uses Amazon SageMaker to train a linear regression model. During evaluation, they observe that the model has high bias (underfitting). Which THREE actions can reduce bias?

Question 474hardmultiple choice

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Refer to the exhibit. A data scientist is trying to run a SageMaker training job using a script that reads training data from 's3://my-bucket/training/data.csv'. The job fails with an access denied error. What is the MOST likely reason?

Exhibit

Refer to the exhibit.

{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Action": [
        "sagemaker:CreateTrainingJob",
        "sagemaker:DescribeTrainingJob"
      ],
      "Resource": "*"
    },
    {
      "Effect": "Allow",
      "Action": "s3:GetObject",
      "Resource": "arn:aws:s3:::my-bucket/training/*"
    }
  ]
}

Question 475hardmultiple choice

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A company uses Amazon SageMaker to train a custom TensorFlow model for image classification. The training job runs on a single ml.p3.2xlarge instance. The dataset contains 500,000 images stored in S3. The training time is too long (over 24 hours). The data scientist wants to reduce training time without changing the model architecture. The dataset is already in TFRecord format. The training script uses the default TensorFlow data pipeline. Which change will MOST significantly reduce training time?

Question 476mediummultiple choice

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A company uses Amazon SageMaker to train a time-series forecasting model using the built-in DeepAR algorithm. The training data consists of daily sales for 1000 products over 2 years. The model performs well on most products, but for a few products with intermittent demand (sporadic sales), the predictions are poor. Which action should the data scientist take to improve predictions for these products?

Question 477easymultiple choice

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A data scientist is using Amazon SageMaker to train a linear learner model for regression. After reviewing the training logs, the data scientist notices that the loss is not decreasing and remains high. The learning rate is set to 0.01. The data is normalized. What should the data scientist do to improve convergence?

Question 478hardmultiple choice

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A company uses Amazon SageMaker to host a model for real-time inference. The model is a large ensemble that takes 2 seconds to load into memory. To reduce cold start latency, the data scientist uses SageMaker's managed warm pools. However, they notice that during a sudden traffic spike, new instances still experience high latency. What is the BEST way to ensure consistently low latency for all requests?

Question 479mediummultiple choice

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A data scientist is training a text classification model using Amazon SageMaker's BlazingText algorithm. The dataset consists of 1 million documents, each labeled with one of 10 categories. The model achieves 92% accuracy on a held-out test set. However, when deployed, the model performs poorly on documents containing slang and typos. What should the data scientist do to improve model robustness?

Question 480mediummultiple choice

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A data scientist is using Amazon SageMaker to train a model using the built-in XGBoost algorithm. The training job is taking a long time. The data scientist notices that the input data is in CSV format and the training job is using File mode. The data size is 50 GB. What is the BEST way to reduce training time?

Question 481mediummulti select

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A team is building a regression model to predict house prices. They observe that the model performs well on training data but poorly on validation data. Which THREE actions can help reduce overfitting? (Choose THREE.)

Question 482hardmultiple choice

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A research team is training a deep learning model for image classification using Amazon SageMaker. The model is a convolutional neural network (CNN) with 50 layers. The team uses a single ml.p3.2xlarge instance. After 10 hours of training, the model has not converged and the loss is decreasing very slowly. The team suspects vanishing gradients. They want to diagnose and fix the issue without significant code changes. Which action should they take?

Question 483easymultiple choice

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A data scientist is building a binary classification model to predict whether a customer will subscribe to a service. The dataset contains 20 features, including categorical variables with high cardinality (e.g., zip code with 10,000 unique values). The scientist uses a logistic regression model and obtains a training AUC of 0.85 and a test AUC of 0.60. The scientist suspects overfitting due to high cardinality features. Which approach should the scientist use to address this issue?

Question 484hardmultiple choice

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A data scientist is training a time series forecasting model using Amazon SageMaker's DeepAR algorithm. The dataset contains daily sales data for 10,000 products over 2 years. The scientist splits the data chronologically: training on the first 18 months, validation on the next 3 months, and test on the last 3 months. The model performs well on validation but poorly on test. The data scientist suspects the model is overfitting to the validation period. Which action should the scientist take to improve test performance?

Question 485easymultiple choice

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A company is using Amazon SageMaker to train a linear learner model for predicting customer lifetime value. The target variable is right-skewed with a long tail. The data scientist applies a log transformation to the target variable and trains the model. The model achieves a low root mean squared error (RMSE) on the log scale. However, when the predictions are exponentiated back to the original scale, the RMSE is much higher. Which step should the data scientist take to improve the model's performance on the original scale?

Question 486mediummultiple choice

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A data scientist is training a multiclass classification model to categorize support tickets into 50 categories. The dataset has 100,000 labeled tickets. The scientist uses a random forest classifier with 100 trees. The model achieves 90% accuracy on the test set, but the F1-score for some rare categories is below 0.1. The scientist wants to improve performance on rare categories without significantly reducing overall accuracy. Which approach should the scientist try?

Question 487hardmultiple choice

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A research team is training a deep learning model for object detection using SageMaker's built-in SSD algorithm. The dataset contains 50,000 images with bounding box annotations. The team uses a single ml.p3.2xlarge instance. After 24 hours of training, the model's loss has plateaued, but the mean average precision (mAP) on validation is only 0.45. The team wants to improve mAP without increasing training time. Which action should they take?

Question 488easymultiple choice

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A data scientist is building a regression model to predict energy consumption. The dataset includes features like temperature, humidity, day of week, and holiday flags. The scientist uses a linear regression model and obtains an R-squared of 0.85 on training and 0.40 on test. The scientist suspects the model is not capturing non-linear relationships. Which approach should the scientist use to capture non-linearity?

Question 489mediummultiple choice

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A company is building a recommendation system using matrix factorization. The dataset has 1 million users and 100,000 items. The data scientist trains a model using SageMaker's Factorization Machines algorithm. The model achieves a root mean squared error (RMSE) of 0.95 on the test set. However, the business requires RMSE below 0.90. The data scientist has already tuned hyperparameters like number of factors and learning rate. Which additional step should the data scientist take to improve RMSE?

Question 490mediummulti select

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A data scientist is training a binary classifier using a large dataset with class imbalance (90% negative, 10% positive). After training a logistic regression model, the F1 score is low but accuracy is high. Which TWO actions should the data scientist take to improve model performance? (Choose 2.)

Question 491easymultiple choice

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A data scientist is training a binary classification model on a highly imbalanced dataset where the positive class represents only 1% of the data. The model achieves 99% accuracy but only identifies 5% of the actual positives. Which metric should the data scientist use to evaluate model performance?

Question 492easymultiple choice

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A data scientist is building a regression model to predict house prices. The dataset has 10 features, and the model shows high variance with a low bias. Which technique should the data scientist use to reduce variance?

Question 493easymultiple choice

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A data scientist is using Amazon SageMaker to train a model, but the training job fails with an 'Out of memory' error. The instance type is ml.p3.2xlarge. Which action should the data scientist take to resolve the issue?

Question 494hardmultiple choice

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A data scientist is training a model using SageMaker's built-in XGBoost algorithm with a large dataset stored in CSV format. The training job is using File mode. The data scientist wants to reduce the time it takes to start training. Which approach would be most effective?

Question 495hardmulti select

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A data scientist is using SageMaker to train a deep learning model. The training job runs on a single GPU instance and is taking too long. Which THREE actions can the data scientist take to reduce training time? (Choose three.)

Question 496easymultiple choice

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A data scientist is training a binary classification model on imbalanced data (95% negative, 5% positive). Which metric is most appropriate for evaluating model performance?

Question 497easymultiple choice

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A data scientist is training a linear regression model and notices high bias in the training set. What action is most likely to reduce bias?

Question 498hardmultiple choice

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A machine learning engineer is tuning a gradient boosting model using SageMaker Hyperparameter Tuning. The objective is to minimize MAE. The tuning job uses 20 training jobs. After 10 jobs, the best objective value is 5.2. Which action should the engineer take to potentially improve the result?

Question 499mediummultiple choice

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A company has a time series forecasting problem with daily sales data. The data shows both trend and seasonality. Which Amazon SageMaker built-in algorithm is most suitable?

Question 500easymultiple choice

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A data scientist is using SageMaker to train a model. The training data is stored in an S3 bucket in a different AWS account. What is required to allow SageMaker to access the data?