MLA-C01 Practice Test 45 — 15 Questions

Question 1

A team deploys a SageMaker real-time endpoint and configures it with an auto scaling policy targeting a variant. During a flash sale, traffic spikes and the team notices that the number of instances increases, but the average model latency still climbs above the target. The team wants the scaling behavior to react faster to sudden bursts without over-provisioning during steady periods. Which change should they make to the scaling policy?

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

Accepted Answer

Lower the target value of the predefined InvocationsPerInstance metric and shorten the scale-out cooldown so capacity is added sooner.. Target tracking keeps a chosen metric near its target. Lowering the InvocationsPerInstance target causes scale-out to trigger at a lower request rate per instance, so additional capacity arrives earlier during a burst. Shortening the scale-out cooldown lets successive scale-out actions fire sooner. Scaling in still occurs during quiet periods, so steady-state cost is not inflated.

Answer

Switch the policy from target tracking to a step scaling policy with a larger scale-out cooldown and a smaller scale-in cooldown.

Answer

Replace target tracking with a scheduled scaling policy that adds instances at fixed times each day.

Answer

Increase the target value of the predefined InvocationsPerInstance metric and enable a longer scale-in cooldown to stabilize the fleet.

Question 2

A machine learning engineer is preparing data for a SageMaker training job and needs to split a large dataset into training, validation, and test sets while avoiding data leakage from the same entity appearing in multiple splits. The dataset contains multiple rows per customer, and the target is customer churn. Which TWO strategies are appropriate? (Choose two.)

Accepted Answer

Split the data by a hash of the customer ID so that all rows for a given customer land in exactly one split.. Entity-level leakage occurs when rows from the same customer appear in more than one split, letting the model memorize customer-specific patterns and inflating validation metrics. Hashing the customer ID into split buckets and using GroupShuffleSplit with the customer ID as the group both ensure that all rows for a customer stay together, which is the correct way to partition this churn dataset.

Answer

Duplicate rows for customers in the minority class so that each split contains examples of every customer.

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Sort the dataset by timestamp and take the first 80 percent of rows for training and the last 20 percent for validation.

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Use a random row-level split with a fixed seed, because a fixed seed makes the split reproducible and therefore leakage-free.

Question 3

A team runs a SageMaker Pipeline that trains a model and registers it in the Model Registry. Compliance requires that the pipeline run automatically every time new labeled data lands in S3, and that each run record the exact S3 data prefix, the training image URI, and the git commit hash as lineage metadata. The engineer wants the least operational overhead. Which approach meets these requirements?

Accepted Answer

Configure an S3 event notification to invoke a Lambda function that calls StartPipelineExecution with the data prefix and commit hash passed as pipeline parameters.. S3 event notifications driving a Lambda that invokes StartPipelineExecution provides immediate, event-driven execution with parameter overrides for the data prefix and commit hash. Those parameters propagate into pipeline steps and are captured as lineage metadata in the Model Registry, satisfying compliance. It avoids polling, scheduling waste, and manual gates, giving the lowest operational overhead of the listed designs.

Answer

Enable SageMaker Data Wrangler scheduled jobs to export data and manually start the pipeline after reviewing the export in SageMaker Studio.

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Create an EventBridge scheduled rule that runs the pipeline every 15 minutes and relies on the pipeline's caching to skip runs when data is unchanged.

Answer

Use AWS Step Functions with a Wait state polling S3 every minute, then call StartPipelineExecution once new objects are detected.

Question 4

A data science team is using AWS Step Functions to orchestrate a machine learning workflow that includes a SageMaker training job followed by a model deployment. They want to ensure that if the training job fails, the workflow retries up to three times with exponential backoff before sending a notification to an Amazon SNS topic. Which Step Functions feature should they use to implement this?

Accepted Answer

Retry and Catch fields on the training task state, with a retry policy specifying MaxAttempts and BackoffRate, and a Catch field that transitions to an SNS publish state.. Step Functions' Retry and Catch fields on a state provide built-in error handling. By configuring Retry with MaxAttempts and BackoffRate, the training task will be retried with exponential backoff. The Catch field can then route to an SNS publish state after all retries fail, ensuring notification only on persistent failure.

Answer

A Map state that iterates over a list of retry attempts, invoking the training job each time until it succeeds.

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A Choice state that checks the training job status and loops back to the training state if it failed, with a counter to limit attempts.

Answer

A Parallel state that runs the training job and an SNS notification simultaneously, with a retry policy on the training job.

Question 5

A company is deploying a large language model (LLM) to a SageMaker endpoint. They want to minimize inference latency and cost by using GPU acceleration and model parallelism. The model is too large to fit on a single GPU. Which SageMaker feature should they use?

Accepted Answer

SageMaker model parallelism library. The SageMaker model parallelism library is designed to partition large models across multiple GPUs, enabling inference for models that exceed a single GPU's memory. It supports various parallelism strategies and can reduce latency and cost. The other options are either for training, deprecated, or only provide recommendations, not the required model parallelism.

Answer

SageMaker Elastic Inference

Answer

SageMaker distributed data parallel library

Answer

SageMaker Inference Recommender

Question 6

A team is building a SageMaker Pipeline that trains a model and then registers it in the SageMaker Model Registry. They want the pipeline to automatically deploy the model to a real-time endpoint only after a human approves the model package. Which two actions should the team take to implement this approval-gated deployment? (Choose two.)

Accepted Answer

Add a RegisterModel step to the pipeline that creates a model package with a PendingManualApproval status.. Manual approval gating relies on the model package lifecycle: RegisterModel creates a package in PendingManualApproval, and a reviewer later approves it. Because the pipeline execution does not pause for review, an EventBridge rule watching for the Approved state change is what triggers the downstream deployment automation, keeping humans in the loop.

Answer

Set the model package status to Approved directly in the RegisterModel step configuration so the endpoint deploys without human intervention.

Answer

Use a SageMaker Clarify processing step to approve the model package when bias metrics fall within thresholds.

Answer

Add a ConditionStep to the pipeline that checks the model package status immediately after RegisterModel and branches to a deployment step.

Question 7

A machine learning engineer needs to run a one-time scoring job over 500 GB of data stored in Amazon S3 using a trained model, and the results must be written back to S3. There is no requirement for a persistent HTTPS endpoint. Which SageMaker feature should the engineer use?

Accepted Answer

Run a SageMaker Batch Transform job that reads input objects from S3 and writes inference output back to S3.. Batch Transform exists precisely for offline, high-volume inference where inputs and outputs live in Amazon S3 and no persistent endpoint is required. It manages the compute fleet for the duration of the job, parallelizes across input data, writes results to S3, and then releases resources, which fits a one-time 500 GB scoring task.

Answer

Create a real-time endpoint and send each S3 object as an HTTPS request through the InvokeEndpoint API.

Answer

Use a SageMaker Processing job with a custom script that loads the model and writes predictions to S3.

Answer

Deploy a serverless inference endpoint and invoke it repeatedly until all 500 GB has been processed.

Question 8

A data engineer is preparing a dataset for a SageMaker training job. The dataset contains a timestamp column and is stored in Amazon S3 as CSV files. The engineer needs to ensure that the training job reads the data efficiently and that the data is partitioned by date to improve query performance in Amazon Athena. Which action should the engineer take?

Accepted Answer

Convert the CSV files to Parquet format and partition them by date in S3 using a Hive-style partition scheme (e.g., year=2023/month=01/day=01/).. Converting CSV to Parquet reduces storage size and enables columnar reads, which speeds up SageMaker training jobs. Partitioning by date with Hive-style prefixes allows Athena to prune partitions, reducing query cost and latency. Together, these actions satisfy both the training efficiency and Athena query performance requirements.

Answer

Convert the CSV files to Parquet format and store them in a single prefix without partitioning.

Answer

Keep the CSV format but partition the files by date using a flat prefix structure (e.g., 2023-01-01/).

Answer

Use AWS Glue to crawl the CSV files and create a table in the AWS Glue Data Catalog, then use Athena to query the data directly without converting or partitioning.

Question 9

A company wants to deploy a scikit-learn model to a SageMaker AI real-time endpoint. The model must be loaded from a custom Python module that contains preprocessing logic not present in the built-in scikit-learn container. The team wants to minimize operational overhead and does not need to change system-level libraries. Which approach should the engineer take?

Accepted Answer

Package the preprocessing logic in an inference.py file and pass it as the entry_point to a SageMaker AI framework estimator using the scikit-learn framework.. A SageMaker AI framework estimator with an entry_point script lets the team inject custom Python preprocessing while reusing the managed scikit-learn container. This satisfies the functional requirement with far less operational effort than building and maintaining a custom Docker image.

Answer

Use the SageMaker AI built-in scikit-learn container without an entry point and rely on the default inference handler.

Answer

Build a fully custom Docker image with a Bring Your Own Container (BYOC) approach and push it to Amazon ECR.

Answer

Deploy the model with SageMaker AI batch transform and invoke it from the application on demand.

Question 10

A media company runs a real-time recommendation model on a SageMaker endpoint. Traffic triples every evening between 18:00 and 22:00 and drops to near zero overnight, and the team wants to cut costs without underserving evening users. They want the endpoint to scale out automatically as invocations rise and scale back in when traffic falls. Which solution should they implement?

Accepted Answer

Enable automatic scaling on the endpoint with a target-tracking scaling policy based on the SageMakerVariantInvocationsPerInstance metric.. Target-tracking automatic scaling based on SageMakerVariantInvocationsPerInstance is the native mechanism for elastic real-time endpoints. It registers the variant as a scalable target with Application Auto Scaling, then adds or removes instances to hold the metric near the target. This directly matches a recurring evening peak followed by an overnight trough while keeping latency low, unlike fixed serverless concurrency or static over-provisioning.

Answer

Create a second endpoint and use an Application Load Balancer to route evening traffic between the two endpoints.

Answer

Deploy the model to a SageMaker Serverless Inference endpoint and set the maximum concurrency to a fixed value equal to the evening peak.

Answer

Increase the instance count of the endpoint to the evening peak permanently and rely on Savings Plans to offset the extra cost.

Question 11

A fraud detection team runs a SageMaker real-time endpoint that logs every request and response to an Amazon S3 bucket. Compliance requires that the model's prediction inputs and outputs be encrypted at rest with a customer-managed AWS KMS key, and that the endpoint be able to read the capture bucket only when necessary. The team enables data capture and specifies a KMS key on the endpoint configuration. Which additional configuration is required for the captured data written to S3 to be encrypted with that customer-managed key?

Accepted Answer

Enable default encryption on the target S3 bucket using SSE-KMS with the customer-managed key, and grant the SageMaker execution role kms:GenerateDataKey and kms:Decrypt permissions on that key.. Captured data is written to S3 by the endpoint's execution role, so encryption with a customer-managed KMS key depends on the destination bucket applying SSE-KMS default encryption with that key and the role holding kms:GenerateDataKey and kms:Decrypt. A bucket policy or VPC endpoint can restrict access but does not encrypt capture objects. The endpoint's KmsKeyId protects attached volumes, not the S3 capture objects.

Answer

Attach the AWS managed policy AmazonSageMakerFullAccess to the execution role and set the endpoint's KmsKeyId to the capture bucket's bucket key.

Answer

Create a VPC endpoint for S3 and configure the endpoint policy to require server-side encryption with the customer-managed key for all capture objects.

Answer

Configure an S3 bucket policy that denies PutObject unless the request includes the aws:kms header, and grant the SageMaker execution role kms:GenerateDataKey and kms:Decrypt.

Question 12

A machine learning engineer runs a SageMaker Processing job that must load a 200 GB dataset from S3, compute statistics, and write a small summary to S3. The job repeatedly fails with an out-of-disk-space error on the processing instance. Which change is MOST likely to resolve the failure?

Accepted Answer

Increase the processing instance's volume size and use ShardedByS3Key so each instance downloads only its share of objects.. Processing jobs download input channels to local storage, so a 200 GB dataset can exhaust the default volume. Enlarging the volume gives the container room, and ShardedByS3Key spreads objects across multiple instances so each holds only a fraction. Pipe mode is not applicable to Processing jobs, and the other options adjust runtime or cleanup behavior without changing disk consumption.

Answer

Add a lifecycle configuration script that deletes files from the input directory after each epoch.

Answer

Raise the max_runtime_in_seconds parameter and enable network isolation on the processing job.

Answer

Switch the input channel to Pipe mode so the container streams records instead of downloading files.

Question 13

An ML platform team must orchestrate a workflow that trains a model, evaluates it against a baseline, and only registers the model if evaluation passes. If evaluation fails, the workflow must notify the data science channel and stop without registering. The team wants the orchestration logic to be expressed as code, versioned in git, and integrated with SageMaker training jobs and Model Registry. Which approach best fits these requirements?

Accepted Answer

Author a SageMaker Pipeline with a Condition step that compares evaluation metrics to the baseline and branches to a RegisterModel step or a Fail step.. SageMaker Pipelines express orchestration as versionable code and include a Condition step for metric-based branching, plus native RegisterModel and Fail steps. This enforces the evaluation gate before registration without custom glue, and the pipeline definition can live in git and be executed reproducibly. It best satisfies the code-as-orchestration, integration, and versioning requirements.

Answer

Write a Python script on an EC2 instance that calls the SageMaker APIs sequentially and uses if/else statements to decide whether to register.

Answer

Use AWS Glue workflows to chain the training and evaluation jobs and branch on the evaluation result.

Answer

Define an AWS Step Functions state machine with a Choice state that inspects evaluation output and calls SageMaker APIs directly.

Question 14

A retailer runs a nightly batch scoring job that processes 40 GB of transaction data and writes predictions to S3. Occasionally a single partition is corrupt, causing the entire job to fail after several hours. The team wants the job to skip the corrupt partition, log which partition failed, and still complete processing of the remaining data with minimal changes to their existing SageMaker Processing job. Which change should they make?

Accepted Answer

Wrap the per-partition processing logic in try/except inside the Processing container script, record the failing partition key to CloudWatch Logs, and continue to the next partition.. Adding per-partition exception handling inside the Processing container isolates corruption to the affected partition, logs its key for follow-up, and allows the job to finish the rest of the data. This is a localized code change that preserves the existing SageMaker Processing job structure and S3 outputs. It meets fault isolation, failure visibility, and completion requirements with the least disruption.

Answer

Configure the Processing job with a retry policy and a maximum retry count of three so transient failures are retried automatically.

Answer

Convert the Processing job to a SageMaker Training job with checkpointing enabled so it can resume after the corrupt partition.

Answer

Increase the Processing job's instance count and volume size so the corrupt partition is retried on a different instance.

Question 15

A company has a SageMaker endpoint that serves a recommendation model. The security team wants to ensure that the model artifacts stored in Amazon S3 are encrypted at rest and that access to the S3 bucket is limited to the SageMaker execution role only. The team also wants to receive alerts if the bucket policy is changed. Which combination of actions should the machine learning engineer take?

Accepted Answer

Enable default encryption on the S3 bucket using AWS KMS, restrict the bucket policy to the SageMaker execution role, and enable AWS CloudTrail logging for S3 bucket policy changes with a CloudWatch alarm.. Encryption at rest is achieved with S3 default encryption using AWS KMS. Least privilege access is enforced by a bucket policy that allows only the SageMaker execution role. Alerting on bucket policy changes requires CloudTrail to log the event and a CloudWatch alarm to notify when the policy is modified.

Answer

Enable S3 Transfer Acceleration and restrict access using an IAM policy attached to the SageMaker execution role.

Answer

Enable S3 server access logging and use Amazon GuardDuty to monitor for unauthorized access to the bucket.

Answer

Use S3 Object Lock in compliance mode and attach a bucket policy that denies all principals except the SageMaker execution role.