MLS-C01 Mock Exam 76 — 25 Questions

Question 1

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?

Accepted Answer

ml.m5.large (2 vCPU, 8 GB memory). ml.m5.large provides 8 GB memory and is cost-effective for real-time inference with moderate latency requirements. Option A is wrong because ml.c5.large has only 4 GB memory, insufficient for 4 GB model plus overhead. Option B is wrong because ml.p3.2xlarge is GPU-accelerated and expensive, overkill for this model. Option D is wrong because Serverless Inference has cold start latency that may exceed 100 ms.

Answer

SageMaker Serverless Inference

Answer

ml.c5.large (2 vCPU, 4 GB memory)

Answer

ml.p3.2xlarge (8 vCPU, 61 GB memory, 1 GPU)

Question 2

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.)

Accepted Answer

F-beta score with beta = 2. Precision-Recall curve and F-beta score (with beta > 1) emphasize recall, which is important when false negatives are costly. Option B (ROC-AUC) is less sensitive to class imbalance. Option D (accuracy) is misleading for imbalanced data. Option E (log loss) is not directly tied to cost.

Answer

Accuracy

Answer

Log loss

Answer

ROC-AUC

Question 3

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?

Accepted Answer

Enable auto-scaling for the endpoint based on latency metrics. Using SageMaker's production variants with auto-scaling can help handle traffic spikes without over-provisioning, thus managing latency and cost. Switching to a larger instance would increase cost. Reducing the number of instances would increase latency. Enabling data capture adds overhead and increases latency.

Answer

Enable data capture for the endpoint to log requests

Answer

Switch to a larger instance type

Answer

Reduce the number of instances behind the endpoint

Question 4

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?

Accepted Answer

Use managed spot training with checkpointing. Option D is correct because managed spot training can reduce cost and training time by using preemptible instances, often with checkpointing. Option A is wrong because increasing instance count may not linearly reduce time and could increase cost. Option B is wrong because reducing epochs may sacrifice accuracy. Option C is wrong because using a smaller instance is likely to increase time.

Answer

Use a larger instance type

Answer

Use a smaller instance type

Answer

Reduce the number of epochs

Question 5

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?

Accepted Answer

ml.m5.large. Option A is correct because ml.m5.large (2 vCPU, 8 GB) is more than sufficient for memory and throughput, and is cost-effective. Option B is wrong because ml.c5.xlarge (4 vCPU, 8 GB) is more expensive than needed. Option C is wrong because ml.t2.medium (2 vCPU, 4 GB) has enough memory but may have burstable CPU limitations. Option D is wrong because ml.p3.2xlarge is GPU-optimized and overkill.

Answer

ml.c5.xlarge

Answer

ml.t2.medium

Answer

ml.p3.2xlarge

Question 6

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?

Accepted Answer

Target encoding. Option A is correct because target encoding replaces categories with the mean target value. Option B is correct because count encoding replaces categories with frequency counts. Option D is correct because one-hot encoding can be used if the number of categories is manageable after pruning. Option C is wrong because label encoding implies ordinal relationship, not ideal for nominal high cardinality. Option E is wrong because binary encoding is another option, but the question asks for THREE; typically target, count, and one-hot are common.

Answer

Binary encoding

Answer

Label encoding

Question 7

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.)

Accepted Answer

Apply L1 or L2 regularization. Feature selection (removing rare words), regularization (L1/L2), and lowering n-gram range reduce model complexity and overfitting. Option A (increasing max_features) can increase overfitting. Option D (using TF-IDF) is a weighting scheme, not a regularization technique.

Answer

Increase max_features to include more words

Answer

Use TF-IDF instead of raw counts

Question 8

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?

Accepted Answer

The features are not normalized.. Feature scaling is crucial for linear models; 'age' range is large compared to other features if they are not scaled. Option A (wrong instance type) is unlikely. Option C (outliers) could cause issues but scaling is more fundamental. Option D (learning rate too high) is possible but scaling is a common first step.

Answer

There are outliers in the target variable.

Answer

The instance type is too small.

Answer

The learning rate is too high.

Question 9

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?

Accepted Answer

Amazon SageMaker real-time endpoint. Amazon SageMaker real-time endpoints are designed for low-latency inference. Option B is wrong because AWS Lambda has a 250 MB deployment package limit and higher latency for large models. Option C is wrong because Amazon EMR is for big data processing, not real-time inference. Option D is wrong because AWS Glue is for ETL jobs.

Answer

AWS Glue

Answer

AWS Lambda with a container image

Answer

Amazon EMR

Question 10

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?

Accepted Answer

Synthetic Minority Over-sampling Technique (SMOTE). SMOTE generates synthetic samples of the minority class to balance the dataset. Option A is wrong because undersampling discards majority class data, losing information. Option B is wrong because class weights adjust loss function but are not specific to random forest. Option D is wrong because threshold tuning is post-training, not during training.

Answer

Use class weights inversely proportional to class frequencies

Answer

Random undersampling of the majority class

Answer

Adjust the decision threshold after training

Question 11

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.)

Accepted Answer

Adjust the decision threshold to increase precision at the cost of recall.. Precision is low, recall is high. To improve precision, the data scientist can adjust the decision threshold or use a different algorithm. Option A (increasing regularization) may help but is not direct. Option D (using class weights) addresses imbalance. Option C (collecting more data) may not help. Option E (changing to random forest) could improve precision. Option B (removing features) might hurt.

Answer

Increase the regularization strength (C) in logistic regression.

Answer

Collect more training data.

Answer

Remove features that have low correlation with the target.

Question 12

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?

Accepted Answer

The model that is a linear learner. SageMaker Autopilot explores various algorithms including linear models, tree-based ensembles, and neural networks. For real-time inference with low latency, simpler models like linear or logistic regression or shallow decision trees are preferred. XGBoost with many trees or deep neural networks increase latency.

Answer

The model with the smallest memory footprint

Answer

The model with the highest validation accuracy

Answer

The model with the lowest validation loss

Question 13

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

Accepted Answer

Remove rows with missing values. Options A and B are correct. A is correct because imputation with mean/median fills missing values. B is correct because removing rows with missing values is a valid approach. C is wrong because one-hot encoding is for categorical data, not missing values. D is wrong because PCA is for dimensionality reduction, not missing value handling. E is wrong because standardization is for scaling, not missing values.

Answer

Standardization

Answer

Principal Component Analysis (PCA)

Answer

One-hot encoding

Question 14

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?

Accepted Answer

iam:PassRole. The policy allows creating training jobs, models, endpoint configs, and endpoints, but does not allow invoking the endpoint for inference. The error is for creating the endpoint, which requires `sagemaker:CreateEndpoint` which is present. However, the error might be due to missing `sagemaker:InvokeEndpoint` which is needed for testing? Actually the question says creating the endpoint fails, so maybe the policy lacks `sagemaker:CreateEndpointConfig`? But that is present. Possibly the missing permission is `sagemaker:ListTags`? No. The most common missing permission is `sagemaker:DescribeEndpoint`? But the error is authorization error, likely missing `sagemaker:CreateEndpoint`? Wait, it is present. Perhaps the policy needs `iam:PassRole` to pass a role to SageMaker. Yes, SageMaker requires `iam:PassRole` to allow the service to assume a role. So the missing permission is `iam:PassRole`. Option D is correct. Option A: `sagemaker:InvokeEndpoint` is for inference, not creation. Option B: `sagemaker:UpdateEndpoint` is for updating. Option C: `sagemaker:ListEndpoints` is for listing.

Answer

sagemaker:ListEndpoints

Answer

sagemaker:InvokeEndpoint

Answer

sagemaker:UpdateEndpoint

Question 15

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?

Accepted Answer

The S3 data type is AugmentedManifestFile, but Linear Learner requires RecordIO or CSV. The command uses `AugmentedManifestFile` as the S3 data type, but Linear Learner expects `RecordIO` or `CSV` format, not augmented manifest. Augmented manifest is for algorithms that support it, like object detection. Linear Learner requires `S3DataType` to be `RecordIO` or `CSV`. Also the content type is `application/x-recordio` which is correct for RecordIO, but the data type is wrong. So the error is due to the S3 data type. Option C is correct. Option A: The IAM role is present. Option B: Instance type is fine. Option D: Max runtime is fine.

Answer

The IAM role does not have sufficient permissions

Answer

The instance type ml.m5.large does not support the Linear Learner algorithm

Answer

The MaxRuntimeInSeconds is too short

Question 16

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?

Accepted Answer

The input data format does not match the model's expected format (e.g., CSV vs JSON). The most common cause of ModelError during inference is that the input format does not match what the model expects. XGBoost models typically expect CSV without headers. The serializer setting in SageMaker must be configured correctly. If the model expects text/csv but the endpoint is configured as JSON, the error occurs. The other options are less likely: model weights are loaded correctly if the model deployed, and the instance type affects latency not errors.

Answer

The inference instance type is too small

Answer

The model is not properly loaded into memory

Answer

The model weights are corrupted during deployment

Question 17

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?

Accepted Answer

Use a smaller image size (e.g., 128x128). Reducing image resolution (e.g., to 128x128) significantly reduces the number of pixels and thus the computational cost per epoch, often with minimal impact on accuracy for many tasks. Using a smaller batch size increases the number of iterations but can actually slow down training. Distributed training with multiple GPUs would reduce time but the question asks for a change that does not sacrifice accuracy; distributed training can sometimes affect convergence but is generally safe. However, reducing resolution is a direct and effective method.

Answer

Reduce the batch size

Answer

Increase the learning rate

Answer

Switch to a distributed training setup with multiple GPUs

Question 18

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"?

Accepted Answer

It defines the probability threshold used to convert model output to binary predictions for monitoring. In Model Monitor, `ProbabilityThresholdAttribute` is used for binary classification models to define the threshold for converting probabilities to predicted labels. It is used to capture baseline distribution of predictions. It does not set the threshold for the endpoint inference; that is done in the model. It is used for monitoring drift in prediction distribution. Option B is correct. Option A: It does not sample data. Option C: It does not define the metric. Option D: It does not filter input data.

Answer

It filters the input data to only include predictions above the threshold

Answer

It specifies the threshold for sampling data for monitoring

Answer

It sets the threshold for the accuracy metric

Question 19

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?

Accepted Answer

Combine the small files into larger files (e.g., TFRecord format). Combining small files into larger ones (e.g., TFRecord or Parquet) reduces the number of S3 GET requests and improves throughput. Using Pipe mode reads data sequentially, which is less efficient for random access. Increasing instance count or using P3 instances addresses compute, not I/O. Amazon EFS is not recommended for training jobs due to higher latency.

Answer

Use SageMaker Pipe mode instead of File mode

Answer

Increase the number of training instances

Answer

Use a P3 instance type for better GPU performance

Question 20

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

Accepted Answer

The endpoint is scaling up to meet desired capacity. Option B is correct because the endpoint is InService but the current instance count (2) is less than the desired count (5), indicating scaling is in progress. Option A is wrong because the status is InService, not OutOfService. Option C is wrong because the endpoint is running but at lower capacity. Option D is wrong because the endpoint is not failed.

Answer

The endpoint has failed

Answer

The endpoint is running at full capacity

Answer

The endpoint is out of service