MLA-C01 Practice Test 32 — 15 Questions

Question 1

A company is building a fraud detection model on credit card transactions. The dataset contains a column 'merchant_id' with 50,000 unique values, many with low frequency. The team wants to avoid overfitting while preserving predictive signal. Which feature engineering approach is most appropriate?

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

Accepted Answer

Apply target encoding with smoothing based on global mean. Target encoding with smoothing (e.g., using the mean of the target per category) captures signal for high-cardinality features while reducing overfitting via regularization. One-hot encoding would create too many columns, and label encoding may impose ordinality.

Answer

Drop the 'merchant_id' column to avoid overfitting

Answer

Apply label encoding and treat it as a numeric feature

Answer

One-hot encode the 'merchant_id' column

Question 2

A company uses SageMaker Pipelines for model training and wants to incorporate model evaluation before deployment into production. Which THREE components are essential? (Choose three.)

Accepted Answer

A model registry approval step. A model registry approval step is essential because it gates the deployment of a model based on its evaluation results. In SageMaker Pipelines, you register the model to the Model Registry after training, and the approval status (e.g., Approved or Rejected) determines whether downstream deployment steps execute. This ensures only models meeting quality thresholds are promoted to production.

Answer

A batch transform step for evaluation

Answer

A human review step

Question 3

A company has a dataset of 2 billion records stored as text files in Amazon S3. The data is partitioned by year and month. The data science team wants to read only the last 6 months of data for model training using SageMaker. To minimize data scanned and reduce costs, which approach should the team use?

Accepted Answer

Use AWS Glue to create a catalog table with partitions, then query with Athena to create a filtered dataset in S3.. AWS Glue can crawl the S3 data to create a catalog table with partitions by year and month. Athena can then query only the partitions corresponding to the last 6 months, scanning minimal data and writing the filtered results back to S3 for SageMaker training. This approach leverages partition pruning to reduce costs and avoids loading or processing the full 2 billion records.

Answer

Use S3 Select to retrieve only the last 6 months of data by applying an SQL expression on each object.

Answer

Use SageMaker Processing with a script that lists all objects in the bucket and reads only those with the desired prefixes.

Answer

Use SageMaker Processing with Input Mode 'File' and specify the S3 prefix for the last 6 months.

Question 4

A company uses Amazon SageMaker Ground Truth to label images for object detection. They want to minimize labeling costs while maintaining high accuracy. Which feature should they enable?

Accepted Answer

Active learning to automatically select samples for labeling. Active learning in Ground Truth selects the most informative images for labeling, reducing the number of labels needed while maintaining model quality.

Answer

Use of mechanical turk for all labeling

Answer

Pre-built annotation workflows for bounding boxes

Answer

Automated data labeling with AWS Lambda

Question 5

Refer to the exhibit. A user is unable to invoke a SageMaker endpoint. The IAM policy shown is attached to the user. Which permission is missing to allow invocation?

Accepted Answer

sagemaker:InvokeEndpoint. To invoke a SageMaker endpoint, the user needs the `sagemaker:InvokeEndpoint` permission. The IAM policy shown lacks this action, which is required for making real-time inference requests to the endpoint. Without it, any attempt to call the endpoint via the SDK or CLI will fail with an access denied error.

Answer

sagemaker:DescribeEndpoint

Answer

sagemaker:CreateEndpoint

Answer

sagemaker:ListEndpoints

Question 6

A financial services company deploys a credit risk model using an Amazon SageMaker endpoint with data capture enabled. The model uses a custom container. The compliance team requires that all inference requests and responses are logged to an S3 bucket with server-side encryption using AWS KMS. The IAM role for the endpoint has the following policy. What must be added to meet the compliance requirement?

Accepted Answer

Add kms:GenerateDataKey and kms:Decrypt permissions to the IAM role.. The IAM role for the SageMaker endpoint needs permissions to generate a data key (kms:GenerateDataKey) for encrypting captured data and to decrypt (kms:Decrypt) the KMS key when writing to the S3 bucket. Without these, the endpoint cannot use the customer-managed KMS key for server-side encryption, even if the bucket policy allows it.

Answer

Add s3:PutObjectAcl permission to the IAM role.

Answer

Enable S3 default encryption on the bucket.

Answer

Modify the container to handle encryption internally.

Question 7

A team uses AWS Auto Scaling for a SageMaker real-time endpoint. They notice that when scaling in, the latest instance is always terminated first, causing disruption to recent requests. How can they configure the scaling policy to terminate the oldest instance first?

Accepted Answer

Configure the termination policy as 'OldestInstance'. AWS Auto Scaling for SageMaker endpoints supports a termination policy of 'OldestInstance', which explicitly instructs the scaling process to terminate the instance that has been running the longest. By default, Auto Scaling terminates the newest instance (the default termination policy), which can disrupt recent requests. Configuring the termination policy to 'OldestInstance' ensures that the oldest, most stable instance is removed first, minimizing disruption to in-flight requests.

Answer

No action needed; this is the default behavior

Answer

Use lifecycle hooks

Answer

Use AWS CloudFormation to manage the endpoint

Question 8

A company has deployed a SageMaker real-time endpoint for a model that predicts customer churn. The endpoint uses a single ml.m5.large instance. After deployment, the team notices that during peak hours, the endpoint returns 5xx errors for about 20% of requests. The endpoint has not been configured with any scaling policy. The team needs to resolve this issue with minimal cost increase. Which solution should the team implement?

Accepted Answer

Enable Auto Scaling for the endpoint with a target tracking policy based on the average InvocationsPerInstance metric.. Enabling Auto Scaling with a target tracking policy based on the average InvocationsPerInstance metric dynamically adjusts the number of instances in response to traffic spikes, preventing 5xx errors during peak hours without over-provisioning. This approach minimizes cost by scaling only when needed, unlike manual instance upgrades or batch transforms that either increase baseline cost or introduce latency.

Answer

Deploy the model to a multi-model endpoint to reduce resource utilization.

Answer

Increase the instance type to ml.m5.xlarge to handle more concurrent requests.

Answer

Use SageMaker batch transform instead of real-time inference to process peak traffic asynchronously.

Question 9

An ML team uses SageMaker Pipelines to automate retraining. After a pipeline failure, they need to reprocess only the failed step without rerunning the entire pipeline. What should they do?

Accepted Answer

Use SageMaker Pipelines Cache with step-level caching.. SageMaker Pipelines Cache with step-level caching allows you to reuse outputs from previous successful runs of unchanged steps. When a pipeline fails, only the failed step and any downstream steps that depend on it need to be re-executed, because cached results from prior successful steps are automatically retrieved. This avoids rerunning the entire pipeline, saving time and compute resources.

Answer

Create a new pipeline version for each run.

Answer

Use SageMaker Model Monitor to detect drift and trigger retraining.

Answer

Manually rerun the pipeline with updated parameters.

Question 10

Refer to the exhibit. A company configures a SageMaker Model Monitor Data Quality monitoring schedule as shown. The schedule runs every hour. However, the team notices that the monitoring job fails intermittently with an AccessDenied error when accessing the S3 bucket for output. The IAM role SageMakerMonitorRole has permissions to write to s3://my-bucket/monitor-output. What is the MOST likely cause of the failure?

Accepted Answer

The monitoring job runs in a VPC that does not have an S3 VPC endpoint, and the bucket policy denies requests from outside the VPC.. The intermittent AccessDenied error when SageMaker Model Monitor attempts to write to the S3 output bucket strongly indicates a network or policy restriction. If the monitoring job is configured to run inside a VPC (common for security compliance) and that VPC lacks an S3 VPC endpoint, traffic to S3 traverses the public internet. If the S3 bucket policy explicitly denies requests from outside the VPC (using a condition like `aws:SourceVpce` or `aws:SourceVpc`), then jobs running inside the VPC without an endpoint will be denied access intermittently, especially if the job's execution role is assumed from within the VPC.

Answer

The S3UploadMode is set to Continuous, which is only supported for batch transform jobs.

Answer

The cron expression is invalid; it should use rate(1 hour) instead.

Answer

The baseline constraints and statistics files are missing from the S3 bucket.

Question 11

An e-commerce company uses a machine learning model to predict customer churn. They notice that the model's performance degrades after a major marketing campaign changes customer behavior. Which approach is MOST effective to detect and respond to this type of concept drift?

Accepted Answer

Use SageMaker Model Monitor to track prediction distribution and trigger retraining.. SageMaker Model Monitor can automatically detect concept drift by tracking the distribution of predictions over time and comparing them against a baseline. When drift is detected, it can trigger a retraining pipeline, enabling the model to adapt to the new customer behavior caused by the marketing campaign without manual intervention.

Answer

Deploy an A/B test to compare the current model with a baseline.

Answer

Manually review model accuracy each month.

Answer

Set up a weekly batch transform job to compute accuracy against historical data.

Answer

Increase the number of instances for the endpoint.

Question 12

A team uses SageMaker Neo to compile a model for deployment on a target device. After compilation, they deploy the compiled model to a SageMaker endpoint using the Neo-optimized container. The endpoint fails to start with error "RuntimeError: Unable to load model". What could be the issue?

Accepted Answer

The target device architecture during compilation does not match the endpoint instance architecture.. SageMaker Neo compiles a model for a specific target architecture (e.g., ARM, x86, GPU). When deploying the compiled model to a SageMaker endpoint, the endpoint instance type must have a CPU or accelerator architecture that matches the target device specified during compilation. If they do not match, the Neo-optimized runtime cannot load the compiled binary, resulting in a 'RuntimeError: Unable to load model'.

Answer

The compiled model was not uploaded to the correct S3 path.

Answer

The Neo compilation job failed silently.

Answer

The endpoint instance type does not support Neo.

Question 13

A company wants to automate the deployment of a SageMaker model into production whenever a new model version is approved in the Model Registry. Which service can be used to trigger the deployment pipeline?

Accepted Answer

Amazon CloudWatch Events (EventBridge). Amazon EventBridge (formerly CloudWatch Events) can detect state changes in SageMaker Model Registry, such as when a model version is approved. It can then trigger a target like AWS CodePipeline or a Lambda function to automate the deployment pipeline, making it the correct choice for event-driven automation of model deployment.

Answer

AWS Lambda

Answer

Amazon S3 Events

Answer

Amazon SNS

Answer

AWS Config

Question 14

A machine learning engineer needs to deploy a TensorFlow model to Amazon SageMaker and wants to use the built-in TensorFlow Serving container. What should the engineer provide in the model archive?

Accepted Answer

A tar.gz file containing the TensorFlow SavedModel.. The built-in TensorFlow Serving container in Amazon SageMaker expects a TensorFlow SavedModel packaged in a tar.gz archive. This is because TensorFlow Serving natively loads models from the SavedModel format, which includes the model's computational graph, weights, and assets in a standardized directory structure. Providing a tar.gz of the SavedModel ensures compatibility with the container's default serving stack without requiring custom inference code.

Answer

A frozen graph of the TensorFlow model.

Answer

Model artifacts and a Python inference script.

Answer

A Dockerfile and model artifacts.

Question 15

During deployment of a Hugging Face model, the endpoint logs show this error. Which step was likely missed?

Accepted Answer

The inference container does not include the transformers library; the team should use a pre-built Hugging Face container.. The error indicates that the inference container cannot find the `transformers` library, which is required to load and run the Hugging Face model. By using a pre-built Hugging Face container from AWS, the team ensures that all necessary dependencies (like `transformers`, `tokenizers`, and `torch`) are pre-installed and compatible with the SageMaker inference environment. Option A is correct because the most likely missed step was selecting a generic container instead of the purpose-built Hugging Face container.

Answer

The IAM role does not have permissions to download additional libraries.

Answer

The model artifact was not packaged correctly; the inference script is missing.

Answer

The endpoint configuration specifies the wrong instance type.