MLA-C01 Practice Test 47 — 15 Questions

Question 1

A healthcare analytics team trains models in Amazon SageMaker and needs an immutable, queryable record of which dataset version and training job produced each registered model version, so an auditor can trace a deployed model back to its inputs months later. Which SageMaker capability should they rely on?

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

Accepted Answer

SageMaker ML Lineage Tracking, which automatically records entities such as datasets, training jobs, and model package versions and their relationships.. ML Lineage Tracking is the SageMaker feature that records artifacts, trials, actions, and their associations automatically as training and registration proceed, producing a queryable graph from a model package version back to the training job and dataset. Other SageMaker capabilities address monitoring, training-run observability, or run comparison, none of which yields the end-to-end provenance record the audit requires.

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SageMaker Model Monitor, which schedules jobs that compare production traffic against a baseline and emit violations to CloudWatch.

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SageMaker Experiments, which groups training runs into experiments and trials so you can compare their metrics side by side.

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SageMaker Debugger, which captures tensors and system metrics during training and can halt a job when a rule is triggered.

Question 2

A small team runs a SageMaker real-time endpoint in production. They want a low-effort way to know when the endpoint's invocations are failing so they can react quickly, and they want the alert delivered to their on-call channel. Which approach requires the least custom code?

Accepted Answer

Create an Amazon CloudWatch alarm on the endpoint's ModelLatency and Invocation4XXErrors metrics, and route the alarm to Amazon SNS with an email or chat subscription.. SageMaker endpoints emit CloudWatch metrics automatically, including invocation counts and 4XX and 5XX error metrics, so a CloudWatch alarm wired to an SNS topic gives failure notification without any custom instrumentation. Model Monitor and log-parsing pipelines solve different problems and require substantially more configuration.

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Use AWS CloudTrail to capture InvokeEndpoint API calls and create an Amazon EventBridge rule that notifies the on-call channel when errors occur.

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Enable SageMaker Model Monitor on the endpoint and configure the monitor to raise an alarm when constraint violations are detected.

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Subscribe an AWS Lambda function to the endpoint's CloudWatch log group and have the function parse logs and publish to Amazon SNS when errors appear.

Question 3

A machine learning engineer needs to give a data scientist read-only access to the model artifacts, training metrics, and monitoring reports stored in a single Amazon S3 bucket used by a SageMaker project, while ensuring the data scientist cannot delete or overwrite any object. Which approach follows least-privilege practice?

Accepted Answer

Attach an IAM policy to the data scientist's role granting s3:GetObject and s3:ListBucket scoped to the project bucket and its prefix, and deny s3:DeleteObject and s3:PutObject on the same resources.. Least privilege for this scenario means a scoped identity policy that allows only object reads and bucket listing on the project bucket and prefix, with write and delete actions denied. Managed read-only policies are too broad across the account, wildcard bucket policies grant write access, and presigned URLs are transient and cannot support ongoing browsing of project outputs.

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Apply an S3 bucket policy that allows the data scientist's role s3:* on the bucket, and rely on S3 versioning to recover any deleted objects.

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Attach the AmazonS3ReadOnlyAccess AWS managed policy to the data scientist's role so they can read any bucket in the account.

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Create a presigned URL for each object and distribute the URLs to the data scientist as needed.

Question 4

A machine learning engineer needs to monitor a SageMaker endpoint for data drift and receive alerts when drift is detected. They want to use a fully managed AWS service to schedule the monitoring jobs and send notifications. Which AWS service should they use to orchestrate the monitoring schedule and trigger alerts?

Accepted Answer

SageMaker Model Monitor with a monitoring schedule. SageMaker Model Monitor provides the ability to create a monitoring schedule that runs on a recurring basis. It evaluates data against a baseline and publishes results to CloudWatch, where you can set alarms to send notifications. The other services can be part of the notification chain, but the core scheduling and drift detection is handled by SageMaker Model Monitor.

Answer

AWS Lambda

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Amazon CloudWatch

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Amazon EventBridge

Question 5

A machine learning engineer is using Amazon SageMaker Processing to preprocess a large dataset. The processing job runs a custom Python script that uses the pandas library to read multiple CSV files from an S3 input prefix. The script must write the processed output to a different S3 prefix. Which configuration of the ProcessingInput and ProcessingOutput parameters is correct for this scenario?

Accepted Answer

ProcessingInput with source set to the S3 input prefix and destination set to '/opt/ml/processing/input'; ProcessingOutput with source set to '/opt/ml/processing/output' and destination set to the S3 output prefix.. In SageMaker Processing, ProcessingInput specifies the S3 source and the local destination path where the data will be mounted in the container. ProcessingOutput specifies the local source path where the script writes output and the S3 destination where it will be uploaded. This bidirectional mapping ensures that the container can access input data and persist output data to S3. The correct configuration uses S3 URIs for external locations and local paths for container locations.

Answer

ProcessingInput with source set to the S3 input prefix and destination set to the S3 output prefix; ProcessingOutput with source set to the S3 input prefix and destination set to '/opt/ml/processing/output'.

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ProcessingInput with source set to '/opt/ml/processing/input' and destination set to '/opt/ml/processing/output'; ProcessingOutput with source set to the S3 input prefix and destination set to the S3 output prefix.

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ProcessingInput with source set to '/opt/ml/processing/input' and destination set to the S3 input prefix; ProcessingOutput with source set to the S3 output prefix and destination set to '/opt/ml/processing/output'.

Question 6

A machine learning engineer is deploying a model to a SageMaker endpoint and wants to ensure that the model's predictions can be explained. The engineer needs to understand which features contributed most to each prediction. Which SageMaker feature should be used?

Accepted Answer

SageMaker Clarify. SageMaker Clarify is specifically designed to provide feature attributions and explain model predictions. It uses SHAP to compute the contribution of each feature to a prediction, which is exactly what the engineer needs. Other services like Debugger, Model Monitor, and Experiments serve different purposes and do not offer prediction-level explainability.

Answer

SageMaker Model Monitor

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SageMaker Debugger

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SageMaker Experiments

Question 7

A machine learning engineer is using SageMaker Debugger to monitor a training job and wants to detect issues early. The engineer wants to receive alerts when the training job is likely to fail due to vanishing gradients and when the loss is not decreasing. Which two actions should the engineer take to achieve this? (Choose two.)

Accepted Answer

Configure a built-in rule for vanishing gradients, such as the VanishingGradient rule.. The VanishingGradient and LossNotDecreasing built-in rules in SageMaker Debugger are designed to automatically detect the specified training issues and trigger alerts. They are easy to configure and provide real-time monitoring, which is exactly what the engineer needs. Custom rules or other services would require more effort or are not suited for this purpose.

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Use SageMaker Clarify to detect bias in the gradients.

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Set up a custom rule using a Python script that analyzes gradients and loss.

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Configure the training job to save all tensors to Amazon S3 for manual analysis.

Question 8

A machine learning engineer is using Amazon SageMaker Feature Store to manage features for a fraud detection model. The engineer needs to ensure that the feature group can serve both batch and real-time predictions. The feature group is configured with an online store enabled. Which additional configuration is required to support batch predictions?

Accepted Answer

Enable an offline store for the feature group and specify an S3 bucket for storage.. Feature Store's online store is for real-time serving, while the offline store is for batch serving and training. To support batch predictions, the feature group must have an offline store enabled, which stores feature data in S3. This allows the engineer to retrieve historical features in bulk for batch inference jobs. Without an offline store, only real-time serving is possible.

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Increase the online store's read capacity to handle batch loads.

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Set the feature group's event time to the ingestion time to allow batch queries.

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Configure the feature group to use a custom KMS key for encryption, which enables batch access.

Question 9

A machine learning engineer has trained a model using SageMaker and wants to deploy it to a real-time endpoint. The engineer needs to specify the model artifacts, the inference code, and the environment. Which SageMaker resource should the engineer create first?

Accepted Answer

A SageMaker model. The deployment sequence in SageMaker begins with creating a model, which specifies the model artifacts and the inference container. This model is then referenced in an endpoint configuration, which defines the deployment settings. Finally, the endpoint is created from that configuration. Therefore, the model is the first resource to create.

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An endpoint configuration

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A SageMaker pipeline

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A SageMaker endpoint

Question 10

A machine learning engineer is using SageMaker Pipelines to automate the training and deployment of a model. The pipeline includes a processing step for feature engineering, a training step, and a model registration step. The engineer wants to ensure that the pipeline is reproducible and that the model artifacts are versioned. Which two actions should be taken? (Choose two.)

Accepted Answer

Store the training dataset in Amazon S3 with versioning enabled and reference the specific version in the pipeline.. Reproducibility requires pinning inputs, and versioning requires a registry for artifacts. Enabling S3 versioning and referencing a specific version ensures the training data is immutable and traceable. Registering the model in the SageMaker Model Registry creates a versioned entry that links to the artifacts and metadata. Together, these actions provide a reproducible pipeline and a versioned model catalog.

Answer

Configure the training step to use the latest SageMaker training image without specifying a version.

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Enable pipeline caching to reuse previous step outputs.

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Use SageMaker Experiments to log the pipeline execution parameters and metrics.

Question 11

A data engineer is preparing a large training dataset stored in Amazon S3 as many small Parquet files, and a SageMaker training job that reads directly from S3 is spending most of its time on the input channel rather than on model computation. The engineer needs to improve the input throughput without changing the model code or the training algorithm. Which action should the engineer take?

Accepted Answer

Use the SageMaker File System Input with Amazon FSx for Lustre linked to the S3 bucket.. Feeding a training job from a high-performance shared file system removes the request-per-small-object overhead that dominates when many tiny Parquet files are read directly from S3. Amazon FSx for Lustre linked to the S3 bucket presents the data as files that the training container mounts and reads at high throughput, improving the input channel without altering model code or the training algorithm, which is exactly what the scenario requires.

Answer

Enable SageMaker Training Compiler and set the framework to accelerated mode.

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Increase the number of records per S3 GET request by enabling S3 Transfer Acceleration on the bucket.

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Convert the output to TFRecord format and use Pipe mode with the SageMaker TensorFlow estimator.

Question 12

A financial services company is training a fraud detection model using SageMaker. The dataset is highly imbalanced, with only 0.2% fraudulent transactions. The team wants to optimize the model for recall at a fixed precision of 90%. They are using the SageMaker built-in XGBoost algorithm with binary:logistic objective. Which evaluation metric should they monitor during training and hyperparameter tuning?

Accepted Answer

Precision-Recall AUC (PR-AUC). For imbalanced classification with a precision constraint, the precision-recall curve is the right tool. PR-AUC summarizes performance across thresholds and emphasizes the minority class. The team can use the PR curve to find the threshold where precision is 90%, then read off recall. This directly aligns with their goal of maximizing recall at that precision, unlike ROC-AUC or single-threshold metrics such as F1 or MCC.

Answer

F1 score

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Area Under the ROC Curve (AUC)

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Matthews Correlation Coefficient (MCC)

Question 13

A healthcare company has deployed a SageMaker model that predicts patient risk scores. The security team requires that all access to the model's endpoint be authenticated and authorized, and that every invocation be traceable to a specific user or application for audit purposes. The team also wants to enforce least privilege so that only specific applications can invoke the endpoint. Which TWO actions should the machine learning engineer take to meet these requirements? (Choose two.)

Accepted Answer

Attach an IAM policy to each calling application's IAM role that allows the sagemaker:InvokeEndpoint action on the specific endpoint ARN.. IAM policies scoped to the specific endpoint ARN enforce authentication and least privilege, ensuring only authorized applications can invoke the endpoint. CloudTrail data events for SageMaker endpoints record each invocation with caller identity and request details, providing the required audit trail. Together they satisfy authentication, authorization, and traceability.

Answer

Enable network isolation on the endpoint to prevent unauthorized outbound calls.

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Store the endpoint's invocation URL in AWS Secrets Manager and require applications to retrieve it before calling.

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Configure the endpoint to use a resource-based policy that allows anonymous invocation from the VPC.

Question 14

A data engineer must prepare a 4 TB Parquet dataset stored in Amazon S3 for a SageMaker training job that runs on 8 ml.p4d.24xlarge instances. The engineer wants the fastest possible data throughput during training while minimizing per-epoch I/O overhead. The dataset is immutable for the duration of the training run. Which approach BEST meets these requirements?

Accepted Answer

Copy the dataset once into an Amazon FSx for Lustre file system linked to the S3 bucket, and point the training job's channel at the FSx mount.. FSx for Lustre linked to S3 offers a shared, high-throughput file system that materializes data on first access and serves subsequent epochs at much lower latency than repeated S3 reads. Because the dataset is immutable during training, the lazy-load-then-cache behavior is safe and efficient, and all eight instances read from the same fast mount rather than each pulling from S3.

Answer

Use SageMaker FastFile mode, which streams objects from S3 on demand with a local cache and exposes a POSIX-like interface.

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Use SageMaker File mode, which copies the dataset to the attached Amazon EBS volume before training begins.

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Use SageMaker Pipe mode with the dataset converted to protobuf RecordIO and streamed directly to the training algorithm.

Question 15

A data scientist has a 200 GB Parquet dataset in Amazon S3 that will be used to train a SageMaker model. The training script reads the data with the SageMaker training toolkit's File mode, and the job currently spends a long time downloading before training begins. The team wants to reduce startup time without changing the training algorithm. Which change should the data scientist make?

Accepted Answer

Switch the input channel to FastFile mode so the training container streams data instead of downloading the full dataset first.. FastFile mode removes the blocking full download by presenting S3 objects as a streamed filesystem inside the container, so the training process starts as soon as it reads the first bytes. This directly shortens the pre-training wait for a large Parquet dataset and requires no change to the training algorithm or data format.

Answer

Enable Pipe mode and reformat the Parquet files into the recordIO-protobuf format.

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Add more instances to the training cluster and enable distributed data parallel.

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Increase the volume size of the training instance so the full dataset fits in local storage.