MLA-C01 Practice Test 5 — 10 Questions

Question 1

A company wants to version and track ML models, with an approval workflow for promoting models from staging to production. Which SageMaker feature should they use?

Accepted Answer

SageMaker Model Registry. SageMaker Model Registry is the correct choice because it provides a centralized repository to catalog, version, and manage ML models, and it supports approval workflows (e.g., PendingApproval, Approved, Rejected) to promote models from staging to production. This directly addresses the requirement for version tracking and an approval gate for model promotion.

Answer

SageMaker Model Monitor

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SageMaker Experiments

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SageMaker Pipelines

Question 2

A company stores its model training data in Amazon S3. To meet compliance requirements, all data in transit between the S3 bucket and SageMaker must be encrypted. What should the company enforce?

Accepted Answer

Enforce HTTPS for all S3 access. Enforcing HTTPS for all S3 access ensures that data in transit between the S3 bucket and SageMaker is encrypted using TLS. This meets the compliance requirement for encrypting data in transit, as HTTPS uses TLS to protect data as it travels over the network.

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Enable S3 versioning

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Enable S3 access logging

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Use S3 server-side encryption (SSE-S3)

Question 3

An organization uses SageMaker Studio and needs to restrict Studio's internet access while allowing users to install custom packages from a private PyPI mirror hosted in a VPC. Which networking configuration should they use?

Accepted Answer

Disable internet access for Studio and configure VPC-only mode, then use a VPC endpoint to the private PyPI mirror. SageMaker Studio in VPC-only mode (no internet access) combined with VPC-only mode for the domain and VPC endpoints to private PyPI mirror enables package installation from the private mirror. NAT gateway or internet gateway would allow internet access, which is not desired.

Answer

Use a NAT gateway to allow outbound traffic to the private PyPI mirror

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Disable internet access for Studio and rely on SageMaker's default VPC configuration

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Enable internet access for Studio and use a VPC endpoint to the private PyPI mirror

Question 4

Which TWO actions are recommended best practices for securing an Amazon SageMaker notebook instance? (Select TWO.)

Accepted Answer

Use AWS KMS to encrypt the notebook instance's storage volume.. Encrypting the notebook instance's storage volume with AWS KMS ensures data-at-rest protection, which is a fundamental security best practice. SageMaker notebook instances use Amazon EBS volumes for storage, and KMS encryption safeguards sensitive code, datasets, and model artifacts stored on that volume against unauthorized access.

Answer

Use network ACLs to restrict API calls to the SageMaker API.

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Enable Multi-AZ deployment for the notebook instance.

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Associate the notebook instance with a public subnet that has an internet gateway.

Question 5

A machine learning engineer is preparing a dataset for a binary classification model. The dataset has a severe class imbalance (95% class A, 5% class B). The engineer wants to use Amazon SageMaker to train the model. Which data preparation technique should the engineer apply to the training dataset to address the imbalance and improve model performance?

Accepted Answer

Apply Synthetic Minority Over-sampling Technique (SMOTE) to generate synthetic samples for the minority class.. SMOTE generates synthetic samples for the minority class by interpolating between existing minority instances, which directly addresses the severe class imbalance (95% class A, 5% class B) by creating a more balanced training dataset. This technique is particularly effective for tabular data in Amazon SageMaker, as it increases the representation of the minority class without simply duplicating existing samples, thereby reducing overfitting and improving the model's ability to learn decision boundaries for the minority class.

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Apply data augmentation to the majority class by adding noise.

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Use a weighted loss function during training to penalize misclassifications of the minority class.

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Apply random under-sampling to reduce the majority class to match the minority class size.

Question 6

A data engineer is setting up a Glue ETL job to process a large dataset stored in Amazon S3. The job needs to read data in Parquet format, apply a filter, and write the results back to S3 in Parquet. The engineer wants to minimize the cost and runtime. Which optimization technique is MOST effective?

Accepted Answer

Use column pruning to read only the columns needed in the transformation.. Using column pruning (reading only necessary columns) reduces the amount of data scanned, which directly reduces I/O and cost, especially in Parquet format. Other options are also optimizations but column pruning typically has the biggest impact for wide datasets.

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Increase the number of DPUs to the maximum allowed.

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Convert the data to JSON format for faster read performance.

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Use a single large file instead of partitioning.

Question 7

A data scientist is training a deep learning model using SageMaker and wants to use distributed training across multiple GPUs to reduce training time. Which TWO actions should the scientist take to configure distributed training? (Select TWO.)

Accepted Answer

Use the SageMaker distributed data parallelism library. The SageMaker distributed data parallelism library (option B) automatically partitions training data and synchronizes gradients across multiple GPUs, reducing training time without manual data splitting. Configuring the SageMaker estimator with a distribution parameter (option D) enables this library by specifying the distribution strategy (e.g., 'torch_distributed' or 'tensorflow_distributed'), which is required to activate distributed training.

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Reduce the number of epochs to match the number of GPUs

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Manually split the training data into shards and upload to S3

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Set the instance count to 1 with a multi-GPU instance

Question 8

A machine learning engineer at a retail company is monitoring a production model that predicts inventory demand. The model's prediction accuracy has dropped significantly over the past week. The engineer checks the model's input data and notices a new product category was introduced with a different distribution. Which concept is most likely causing the performance degradation?

Accepted Answer

Covariate shift. B is correct because covariate shift occurs when the distribution of the input features changes while the relationship between features and the target remains the same. In this scenario, the introduction of a new product category with a different distribution alters the input data distribution, causing the model to encounter unseen patterns and degrade in prediction accuracy.

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Concept drift

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Data leakage

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Model decay

Question 9

A company uses Amazon SageMaker to train and deploy machine learning models. The security team requires that all data in transit between the training job and S3 be encrypted, and that no data traverses the public internet. Which configuration should the company use?

Accepted Answer

Create a VPC with S3 VPC endpoints, attach a VPC-only policy to the SageMaker execution role, and enable KMS encryption for training jobs.. It ensures that data in transit between SageMaker and S3 stays within the AWS network and is encrypted. By creating a VPC with S3 VPC endpoints, traffic uses AWS private IPs and never traverses the public internet. Attaching a VPC-only policy to the SageMaker execution role restricts the training job to only use VPC endpoints, and enabling KMS encryption for the training job ensures data is encrypted in transit (via TLS) and at rest.

Answer

Use an S3 bucket with SSE-S3 encryption and restrict bucket access to a VPC.

Answer

Enable default encryption on the S3 bucket and use HTTPS for all SageMaker endpoints.

Answer

Create a VPC with a NAT gateway, and configure SageMaker to use the VPC and enforce HTTPS.

Question 10

A financial institution uses SageMaker to train and deploy models. They need to track every experiment, model version, and deployment step for audit purposes. Which SageMaker feature should they use to capture the full lineage of artifacts, actions, and contexts?

Accepted Answer

SageMaker ML Lineage Tracking. SageMaker ML Lineage Tracking creates a graph of artifacts (datasets, models), actions (training jobs, deployment), and contexts (experiments). It provides a complete audit trail. Experiments alone track trials but not lineage. Model Registry tracks model versions but not full pipeline lineage. Clarify is for bias monitoring.

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SageMaker Clarify

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SageMaker Model Registry

Answer

SageMaker Experiments