MLA-C01 Practice Test 3 — 10 Questions

Question 1

A data science team is using Amazon SageMaker Pipelines to orchestrate a multi-step workflow that includes data preprocessing, training, and model evaluation. They want to reuse the preprocessed data across multiple pipeline executions without re-running the preprocessing step if the source data hasn't changed. What should they configure?

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

Accepted Answer

Use SageMaker Processing steps with caching. SageMaker Processing steps support caching, which allows the pipeline to skip re-execution of the preprocessing step if the input data and pipeline parameters have not changed. This is achieved by configuring a `CacheConfig` with a caching key based on the input data source and step parameters, ensuring that the preprocessed data is reused across multiple pipeline executions without redundant computation.

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Use SageMaker Training steps with checkpointing

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Use SageMaker Feature Store to store the preprocessed features

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Use SageMaker Data Wrangler for the preprocessing

Question 2

A data science team deploys a real-time inference endpoint on Amazon SageMaker. They want to monitor for data drift in the input features over time. Which AWS service should they use to capture and analyze the input data distribution?

Accepted Answer

Amazon SageMaker Model Monitor. Amazon SageMaker Model Monitor is the correct service because it is specifically designed to continuously monitor machine learning models in production for data drift and quality issues. It automatically captures input data distributions from real-time inference endpoints and compares them against a baseline to detect statistical changes, alerting the team when drift occurs.

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Amazon Athena

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AWS CloudTrail

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Amazon CloudWatch Logs

Question 3

A data scientist is using SageMaker Data Wrangler to prepare a large dataset. The data contains duplicate rows, which could bias the model. Which built-in step in Data Wrangler can automatically detect and remove duplicates?

Accepted Answer

Handle Duplicates transform in Data Wrangler. The Handle Duplicates transform is a built-in step in SageMaker Data Wrangler specifically designed to detect and remove duplicate rows from a dataset. It provides configurable options such as selecting a subset of columns for duplicate detection and choosing whether to keep the first or last occurrence, directly addressing the bias risk from duplicate rows in ML training data.

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Amazon QuickSight duplicate detection

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AWS Glue Studio FindDuplicates transform

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Amazon DataZone catalog

Question 4

A machine learning engineer is using Amazon SageMaker Experiments to track multiple training runs. They want to compare the performance of different hyperparameter configurations visually. Which SageMaker tool provides an interactive interface to compare experiments?

Accepted Answer

SageMaker Studio. SageMaker Studio provides an interactive, web-based interface that allows you to visually compare experiment runs, including hyperparameter configurations and performance metrics, through built-in experiment management and visualization tools. This is the correct answer because the question specifically asks for an interactive interface, which Studio offers natively, unlike the other options which are programmatic or monitoring-focused.

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SageMaker Model Monitor

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SageMaker Experiments SDK

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SageMaker Debugger Insights

Question 5

A company uses SageMaker endpoints with auto-scaling. The endpoint is experiencing high latency during peak hours. The metrics show CPU utilization is low but memory is high. What is the most likely cause?

Accepted Answer

The auto-scaling policy is based on CPU utilization, which does not trigger scaling.. The auto-scaling policy is based on CPU utilization, which remains low during the memory-bound issue. Since the scaling trigger is not met, the endpoint does not add more instances to handle the increased load, leading to high latency. Memory pressure without CPU spikes indicates the bottleneck is memory, not compute, so a CPU-based metric fails to scale appropriately.

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The model is not optimized for inference, causing memory leaks.

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The instance type has insufficient network bandwidth.

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The endpoint is deployed in a VPC without a NAT gateway.

Question 6

A company deploys a machine learning model as a SageMaker real-time endpoint. They need to implement a mechanism to automatically roll back to the previous model version if performance degrades after a deployment. Which approach should they use?

Accepted Answer

Configure the SageMaker endpoint deployment with traffic shifting and set up CloudWatch alarms to trigger automatic rollback. SageMaker endpoints support deployment with traffic shifting (e.g., canary or linear patterns) via the 'DeploymentConfig' parameter, and you can attach CloudWatch alarms to the endpoint's variant metrics. If the alarm triggers (e.g., due to increased error rate or latency), SageMaker automatically rolls back the traffic to the previous model version, ensuring minimal manual intervention and fast recovery.

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Manually update the endpoint to point to the previous model version

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Create multiple endpoints and use Amazon Route 53 weighted routing to shift traffic

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Use AWS CodeDeploy with Amazon EC2 instances behind an Elastic Load Balancer

Question 7

A machine learning team deploys a custom container image for an Amazon SageMaker training job. The container needs to access an S3 bucket that contains sensitive data. The team wants to follow the principle of least privilege. How should the team grant access?

Accepted Answer

Create an IAM role with S3 access and assign it as the SageMaker execution role for the training job.. SageMaker training jobs use an IAM execution role to grant permissions to AWS services like S3. By creating a dedicated IAM role with only the necessary S3 actions (e.g., s3:GetObject, s3:PutObject) and assigning it as the SageMaker execution role, the team follows the principle of least privilege. SageMaker automatically assumes this role via AWS Security Token Service (STS) to access the S3 bucket on behalf of the container, without embedding credentials.

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Attach an IAM instance profile to the training instance with permissions to the bucket.

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Configure an S3 bucket policy that grants access to the training job's ARN.

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Store AWS access keys in the container image and use them to access the bucket.

Question 8

Refer to the exhibit. A user has the above IAM policy attached but cannot access files in SageMaker Studio. What additional permission is most likely needed?

Accepted Answer

s3:GetObject on the relevant S3 buckets. The IAM policy shown likely grants permissions for SageMaker API actions (e.g., CreateApp, DescribeDomain) but does not include S3 data plane permissions. SageMaker Studio notebooks and experiments read training data, scripts, and model artifacts from S3 buckets. Without s3:GetObject on the relevant S3 buckets, the user can launch the Studio environment but cannot load or save any files, resulting in access failures.

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sagemaker:ListApps

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sagemaker:DescribeUserProfile

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kms:Decrypt

Question 9

A data scientist trained a model using SageMaker and wants to automate the retraining process when new data becomes available. Which AWS service is best suited to trigger a SageMaker training job based on an S3 event?

Accepted Answer

Amazon EventBridge with a rule matching S3 object creation.. Amazon EventBridge is the correct choice because it can directly capture S3 events (such as ObjectCreated) via a rule and invoke a SageMaker training job as a target. This event-driven architecture eliminates the need for polling or scheduled checks, enabling immediate retraining when new data arrives in S3.

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AWS Step Functions with a scheduled trigger.

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Amazon Simple Workflow Service (SWF) decider.

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Amazon Simple Queue Service (SQS) with a polling script.

Question 10

A company has a batch transform job in Amazon SageMaker that processes large datasets every night. Recently, the job has been failing sporadically with an out-of-memory error. The data size has not increased. What is the MOST likely cause?

Accepted Answer

The custom inference code has a memory leak that gradually consumes available memory.. A memory leak in custom inference code would cause gradual memory consumption over time, eventually leading to out-of-memory errors even if the dataset size remains unchanged. SageMaker batch transform jobs run inference on each record independently, so a leak that accumulates across records (e.g., not releasing tensors or file handles) would cause sporadic failures as the job processes more data.

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The data distribution has shifted, causing different memory usage patterns.

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The instance type is not large enough to handle the dataset.

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The batch transform input data has increased in size.