MLS-C01 Mock Exam 76 — 25 Questions

Question 1

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?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Apply target encoding with smoothing to the zip code feature. (target encoding with smoothing) reduces cardinality while preserving predictive power. Option A (label encoding) may introduce ordinality issues. Option B (remove zip code) may lose important information. Option D (one-hot encoding) increases dimensionality drastically.

Answer

Apply label encoding to the zip code feature

Answer

Remove the zip code feature entirely

Answer

Apply one-hot encoding to the zip code feature

Question 2

A data scientist is using SageMaker to build a model for fraud detection. The dataset is highly imbalanced. Which THREE techniques should be applied to address class imbalance?

Accepted Answer

Apply SMOTE to generate synthetic samples of the minority class.. SMOTE (Synthetic Minority Oversampling Technique) generates synthetic samples for the minority class by interpolating between existing minority instances, which helps balance the dataset without simply duplicating data. This is effective for fraud detection because it provides the model with more diverse examples of fraudulent transactions, reducing the bias toward the majority class.

Answer

Train the model only on the majority class.

Answer

Use accuracy as the evaluation metric.

Question 3

A data scientist is training a model on Amazon SageMaker and notices that the training job is taking much longer than expected. The instance type is ml.m5.xlarge and the dataset is 10 GB in CSV format. Which action is MOST likely to reduce training time without changing the instance type?

Accepted Answer

Convert the dataset to RecordIO or Parquet format before training.. Converting the dataset from CSV to a columnar format like Parquet or RecordIO reduces I/O overhead and improves data throughput during training. SageMaker's built-in algorithms and many deep learning frameworks can read these formats more efficiently, especially for large datasets, because they enable better compression and predicate pushdown, reducing the time spent on data loading.

Answer

Change the instance type to ml.p3.2xlarge (GPU) for faster computation.

Answer

Reduce the number of training epochs to speed up convergence.

Answer

Increase the batch size to the maximum supported by the instance memory.

Question 4

Which TWO SageMaker features can be used to monitor and debug training jobs? (Choose 2.)

Accepted Answer

SageMaker Debugger. SageMaker Debugger (A) is correct because it provides real-time monitoring and debugging of training jobs by capturing tensors, gradients, and other metrics during training, allowing you to detect issues like vanishing gradients or overfitting. Amazon CloudWatch Logs (D) is correct because it automatically collects and stores logs from SageMaker training jobs, including algorithm output and framework logs, which you can monitor for errors or anomalies.

Answer

SageMaker Model Monitor

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SageMaker Ground Truth

Answer

SageMaker Clarify

Question 5

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.)

Accepted Answer

AUC represents the probability that a randomly chosen positive instance is ranked higher than a randomly chosen negative instance.. AUC measures the probability that a randomly chosen positive instance is ranked higher than a randomly chosen negative instance, which is correctly stated in option A. AUC is also threshold-independent, evaluating model ranking across all thresholds, as stated in option E. Option B is incorrect because an AUC of 0.85 is better than random (0.5). Option C is incorrect because AUC is not average precision; average precision is a different metric. Option D is incorrect because AUC is not equivalent to accuracy at any specific threshold.

Answer

An AUC of 0.85 indicates the model is no better than random guessing.

Answer

AUC is the average precision across all thresholds.

Answer

AUC is equivalent to the accuracy of the model at the default threshold of 0.5.

Question 6

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?

Accepted Answer

Decision tree. Decision trees are inherently interpretable because they produce a clear, rule-based structure that shows exactly which features and thresholds lead to a churn prediction. This white-box nature allows stakeholders to trace the reasoning for each prediction, meeting the requirement for interpretability without needing post-hoc explanation methods.

Answer

Gradient boosting machine

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Support vector machine (SVM)

Answer

Deep neural network

Question 7

A machine learning team is deploying a model using Amazon SageMaker. They need to automatically retrain the model every week with new data and update the endpoint without downtime. Which approach should they use?

Accepted Answer

Use SageMaker Pipelines to automate retraining and deploy a new endpoint with blue/green deployment. The correct approach is to use SageMaker Pipelines, which provides a fully managed service to automate the machine learning workflow, including retraining on a schedule. SageMaker Pipelines supports blue/green deployment patterns using `CreateEndpointConfig` and `UpdateEndpoint` to update the endpoint without downtime. Option E correctly describes this. Option D is incorrect because automatic model tuning is for hyperparameter optimization, not scheduled retraining. Option C (AWS Lambda) could be used, but it requires more manual orchestration and is not the best practice recommended by AWS.

Answer

Use SageMaker Ground Truth to label new data and trigger retraining

Answer

Use SageMaker batch transform to periodically generate predictions and replace the model

Answer

Use AWS Lambda to trigger retraining on a schedule and deploy a new endpoint

Answer

Use SageMaker automatic model tuning with a schedule and update the endpoint using CreateEndpointConfig and UpdateEndpoint

Question 8

A company is using SageMaker Ground Truth to label images for a computer vision model. After launching the labeling job, they notice that the labeling throughput is lower than expected. What should they do to increase throughput?

Accepted Answer

Use a private workforce with more workers.. SageMaker Ground Truth labeling throughput is primarily limited by the number of workers available to process tasks. Using a private workforce allows you to directly control and scale the number of workers, which increases parallelism and overall throughput. Public or vendor workforces have fixed capacity and may not scale as quickly, so adding more private workers is the most effective way to boost throughput.

Answer

Change the labeling task to use a single annotator per image.

Answer

Reduce the number of workers assigned to each task.

Answer

Increase the time allowed for each labeling task.

Question 9

An IAM policy is attached to a data engineering role that writes to an S3 bucket. The policy is shown in the exhibit. What is the effect of this policy?

Accepted Answer

The role must use SSE-KMS when writing objects; reading is allowed only if the object is encrypted with SSE-KMS. The IAM policy uses a `Condition` block with `s3:x-amz-server-side-encryption` set to `aws:kms`, which enforces that any `PutObject` request must include SSE-KMS encryption. The `Deny` effect on `s3:GetObject` when encryption is not `aws:kms` ensures that reading objects without SSE-KMS is blocked. This combination allows writing only with SSE-KMS and reading only of objects encrypted with SSE-KMS, making option D correct.

Answer

The role can write objects with any encryption, but reading is restricted to SSE-KMS only

Answer

The role can read and write any object without encryption restrictions

Answer

The role can only read objects; writing is always denied

Question 10

A data engineer needs to collect and analyze log data from multiple EC2 instances in real-time. The solution should be serverless and scalable. Which TWO AWS services should be used?

Accepted Answer

Amazon Kinesis Data Firehose. Amazon Kinesis Data Firehose is the correct choice because it is a fully managed, serverless service that can capture, transform, and load streaming log data from EC2 instances into destinations like Amazon S3 or Amazon OpenSearch Service in near real-time, with no infrastructure to manage. It automatically scales to handle high-throughput data streams, making it ideal for real-time log analytics.

Answer

Amazon EMR

Answer

Amazon Athena

Answer

Amazon S3

Question 11

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?

Accepted Answer

Decrease the probability threshold for classifying a transaction as fraudulent.. Decreasing the probability threshold for classifying a transaction as fraudulent increases recall because more transactions are predicted as positive, capturing more true positives at the cost of precision. Option A (feature selection) might remove noisy features but could inadvertently eliminate informative ones, potentially reducing recall. Option B (increasing maximum depth of trees) increases model complexity and risk of overfitting without directly improving recall. Option C (increasing class weight for the minority class) can help the model focus on the minority class, but if recall is still insufficient, threshold adjustment is a more direct approach. Option E (increasing number of trees) reduces variance and improves generalization but does not directly increase recall.

Answer

Perform feature selection to remove noisy features.

Answer

Increase the maximum depth of the trees.

Answer

Increase the class weight for the minority class in the algorithm.

Answer

Increase the number of trees in the random forest.

Question 12

Which TWO configuration steps are necessary to deploy a custom Docker container for training in Amazon SageMaker? (Choose two.)

Accepted Answer

Implement the train function in the container that saves model artifacts to /opt/ml/model. Amazon SageMaker expects the training container to save model artifacts to the `/opt/ml/model` directory, which SageMaker automatically copies to Amazon S3 after training completes. This is a required contract for any custom training container used with SageMaker.

Answer

Expose a REST API endpoint for inference

Answer

Define a Docker Compose file to manage multi-container training

Answer

Push the container image to Docker Hub

Question 13

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.)

Accepted Answer

Use the F1 score as the evaluation metric.. The F1 score is the harmonic mean of precision and recall, making it a robust evaluation metric for imbalanced datasets because it captures both false positives and false negatives. Unlike accuracy, which can be misleadingly high when the majority class dominates, the F1 score provides a balanced measure of model performance on the minority class.

Answer

Undersample the majority class randomly.

Answer

Use accuracy as the evaluation metric.

Answer

Apply L1 regularization to the model.

Question 14

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?

Accepted Answer

F1 score. Accuracy is misleading for imbalanced datasets because a model that predicts the majority class all the time can achieve 99% accuracy. F1 score balances precision and recall, making it suitable for imbalanced classification. Precision, recall, and AUC are also useful, but F1 is a common single metric for imbalanced binary classification. Option A: F1 score correctly balances precision and recall. Option B: Accuracy is not suitable. Option C: Precision alone ignores recall. Option D: Recall alone ignores precision.

Answer

Accuracy

Answer

Precision

Answer

Recall

Question 15

A data scientist is performing EDA on a dataset with mixed data types (numerical and categorical). Which TWO visualizations are most appropriate for understanding the distribution of categorical features?

Accepted Answer

Pie chart. Bar charts and pie charts are both effective for visualizing the distribution of categorical features. Bar charts display the count or frequency of each category, while pie charts show the relative proportions. Options A (histogram) and B (box plot) are designed for numerical data, and option D (scatter plot) is for the relationship between two numerical variables.

Answer

Histogram

Answer

Box plot

Answer

Scatter plot

Question 16

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?

Accepted Answer

Use Pipe mode for data input. Using Pipe mode streams training data directly from S3 without first downloading it to the instance's local storage, significantly reducing I/O time and therefore overall training time. Option A is correct. Option B is incorrect because using a smaller instance type reduces compute capacity, which would likely increase training time. Option C is incorrect because increasing the number of epochs would increase training time, not decrease it. Option D is incorrect because decreasing the batch size typically results in more gradient updates per epoch, which can increase training time.

Answer

Use a smaller instance type

Answer

Increase the number of epochs

Answer

Decrease the batch size

Question 17

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?

Accepted Answer

Linear regression. Linear regression is the most appropriate choice because it is interpretable, handles high-dimensional data with 50 features efficiently, scales well to 1 million training examples that fit in memory, and directly addresses regression. Option A (PCA) is a dimensionality reduction technique, not a regression algorithm. Option C (XGBoost) is powerful for regression but is less interpretable due to its ensemble nature. Option D (k-NN) is non-parametric and computationally expensive at inference time, lacking interpretability.

Answer

Principal Component Analysis (PCA)

Answer

XGBoost

Answer

k-Nearest Neighbors

Question 18

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?

Accepted Answer

Reduce the number of features by selecting the most important ones. To reduce inference latency without retraining the XGBoost model, reducing the number of features to the most important ones directly decreases the computational complexity of the model, as fewer tree splits are evaluated per request. This is a model-level optimization that does not require retraining if the feature importance is already known. SageMaker Elastic Inference, however, is designed to accelerate deep learning models by attaching a GPU accelerator; it does not speed up XGBoost or other tree-based models because they do not utilize GPUs effectively. Therefore, only option C is correct.

Answer

Increase the number of instances behind the endpoint

Answer

Use SageMaker's batch transform instead of real-time endpoint

Answer

Use SageMaker's Elastic Inference to attach an acceleration to the endpoint

Question 19

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?

Accepted Answer

Use Bayesian optimization. Bayesian optimization uses results from previous hyperparameter evaluations to choose the next set, reducing the number of training jobs needed to find optimal hyperparameters. This is especially efficient for large datasets where each training job is expensive. Option B (grid search) is exhaustive and slow for many hyperparameters. Option C (random search) is faster but does not learn from past trials. Option D (reducing max depth) may speed up individual jobs but risks underfitting and does not improve the tuning process itself.

Answer

Use grid search with a fine-grained grid

Answer

Use random search with more iterations

Answer

Reduce the max depth of trees

Question 20

A company is using AWS Glue ETL jobs to transform data. The jobs are failing due to insufficient memory. The data processing involves complex joins and aggregations. Which THREE actions can improve job performance and reduce memory usage?

Accepted Answer

Filter and project data early in the transformation to reduce data volume. Filtering and projecting data early in the transformation reduces the volume of data that must be processed in subsequent operations like joins and aggregations. By using pushdown predicates and selecting only necessary columns, you minimize the data shuffled across the cluster, which directly reduces memory pressure and improves job performance in AWS Glue ETL.

Answer

Decrease the number of DPUs allocated to the job

Answer

Use a single node cluster to avoid shuffle overhead