MLA-C01 Practice Test 10 — 10 Questions

Question 1

A data science team deployed a model on Amazon SageMaker and enabled Model Monitor to detect data drift. After a week, they receive alerts indicating that the distribution of a key feature has shifted significantly. However, the model's accuracy on the recent production data remains high. Which action should the team take next?

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

Accepted Answer

Investigate the root cause of the drift as it may be benign or may lead to future degradation.. Data drift does not always immediately impact model accuracy; the drift may be benign (e.g., a shift in a non-predictive feature) or may indicate a precursor to future degradation. Amazon SageMaker Model Monitor detects distribution shifts using statistical tests like Kolmogorov-Smirnov or Chi-squared, but the team must investigate the root cause—such as changes in data collection, seasonal patterns, or upstream pipeline issues—before taking corrective action. Disabling alerts or retraining blindly could mask underlying problems or waste resources.

Answer

Disable the data drift alert since accuracy is not affected.

Answer

Increase the sample size for monitoring to reduce false positives.

Answer

Retrain the model immediately because data drift always degrades performance.

Question 2

Refer to the exhibit. A data engineer deploys this Glue job via CloudFormation. When running, the job fails with a timeout after 2 hours. The job processes a large dataset and expected to take 3 hours. Which change would resolve the issue?

Accepted Answer

Increase the Timeout property to 240 minutes. The Glue job failed due to a timeout after 2 hours, but the expected runtime is 3 hours. The default timeout for AWS Glue jobs is 2880 minutes (48 hours), but the CloudFormation template likely set a lower value. Increasing the Timeout property to 240 minutes (4 hours) provides enough time for the job to complete without being prematurely terminated.

Answer

Increase NumberOfWorkers to 20

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Set MaxRetries to 3

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Change WorkerType to G.2X

Question 3

A team is migrating their ML infrastructure to AWS and wants to use infrastructure as code to manage SageMaker Studio domains, user profiles, and associated resources. Which services can they use for this purpose? (Select THREE.)

Accepted Answer

AWS CDK (Cloud Development Kit). AWS CDK (Cloud Development Kit) is correct because it allows you to define AWS infrastructure, including SageMaker Studio domains and user profiles, using familiar programming languages like Python or TypeScript. CDK synthesizes these definitions into CloudFormation templates, enabling infrastructure as code (IaC) for SageMaker resources. This approach provides type safety and high-level abstractions, making it suitable for managing complex ML environments.

Answer

SageMaker Python SDK

Answer

Boto3

Question 4

A company needs to update a model in production without any downtime. They currently have a single real-time endpoint serving traffic. Which approach allows them to deploy a new model version and switch traffic gradually while being able to roll back quickly?

Accepted Answer

Use a canary deployment by creating a new production variant with the new model and shifting traffic incrementally. SageMaker supports production variants with traffic splitting. By creating a new variant with the new model and shifting traffic gradually, the old variant remains available for rollback. Blue/green deployment with a new endpoint and endpoint configuration swap also allows quick rollback. The key is to have both variants active during the transition.

Answer

Use a multi-model endpoint and replace the model file

Answer

Stop the endpoint, update the model, and restart the endpoint

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Update the existing endpoint's model directly using UpdateEndpoint

Question 5

A team is training a deep learning model on Amazon SageMaker using a custom Docker container. Which three practices should they follow to optimize training performance? (Choose three.)

Accepted Answer

Store training data in Amazon S3 in a shuffled and compressed format. Storing training data in Amazon S3 in a shuffled and compressed format (Option A) optimizes training performance because shuffling prevents biased gradient updates during stochastic gradient descent, while compression reduces I/O overhead and network transfer time. SageMaker's Pipe mode can then stream this compressed data directly to the training algorithm without intermediate disk writes, further accelerating throughput.

Answer

Use the largest instance type available

Answer

Increase the number of layers in the model to improve accuracy

Question 6

An organization needs to ensure that all data transmitted between containers in a SageMaker training job is encrypted. In the training job configuration, which setting should they enable?

Accepted Answer

Enable inter-container traffic encryption. SageMaker training jobs support inter-container traffic encryption, which ensures that data transmitted between containers (e.g., distributed training workers) is encrypted in transit. This setting uses TLS to protect the communication channel, meeting the organization's requirement for encrypted data transmission between containers.

Answer

Use a KMS key for data encryption

Answer

Configure the training job in VPC-only mode

Answer

Enable network isolation mode

Question 7

A machine learning practitioner is building a binary classifier with severe class imbalance (1:1000). They want to use SMOTE for oversampling. What is a potential drawback of applying SMOTE on the entire dataset before splitting into training and test sets?

Accepted Answer

It causes data leakage, making validation metrics overly optimistic. Applying SMOTE before splitting causes data leakage because synthetic samples are generated using information from both training and test sets, leading to overly optimistic performance estimates.

Answer

SMOTE increases the risk of overfitting to the minority class

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SMOTE cannot be applied to categorical features

Answer

SMOTE generates synthetic samples that may not be realistic

Question 8

A company uses Amazon SageMaker to train a custom XGBoost model. The training job runs on a single ml.m5.large instance and takes 2 hours. To reduce training time without changing the algorithm, what should the data scientist do?

Accepted Answer

Use a larger instance type. Increasing the instance type (e.g., from ml.m5.large to ml.m5.xlarge or ml.p3.2xlarge) provides more CPU/GPU cores, memory, and network bandwidth, directly reducing training time for the same XGBoost algorithm. Since the training job is compute-bound on a single instance, scaling vertically is the most straightforward way to accelerate training without modifying the algorithm or hyperparameters.

Answer

Increase the number of epochs

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Use SageMaker's built-in XGBoost algorithm

Answer

Enable automatic model tuning

Question 9

A data scientist is evaluating data quality for a machine learning project. The dataset has missing values, outliers, and inconsistent formatting. Which TWO steps should the data scientist perform during the data preparation phase? (Choose 2.)

Accepted Answer

Standardize numeric features. Standardizing numeric features (Option C) is a critical data preparation step because it rescales features to have zero mean and unit variance, which prevents features with larger magnitudes from dominating distance-based algorithms like k-nearest neighbors or gradient descent optimization. This transformation is essential for many machine learning models to converge faster and perform correctly.

Answer

Normalize text data to lowercase

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Remove all outliers blindly

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Use a large neural network to handle all transformations

Question 10

A team is training a large language model and needs to split the model layers across multiple GPUs due to memory constraints. Which distributed training strategy should they use?

Accepted Answer

Model parallelism

Answer

Data parallelism

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Hyperparameter tuning

Answer

Autopilot