MLA-C01 Practice Test 20 — 15 Questions

Question 1

A company stores its raw IoT sensor data in Amazon S3. The data is in CSV format and contains timestamps, sensor IDs, and readings. A data engineer needs to catalog this data for discoverability and querying by other team members. Which AWS service should they use to create a searchable metadata catalog?

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

Accepted Answer

AWS Glue Data Catalog. The AWS Glue Data Catalog is a managed metadata repository that stores table definitions, schema information, and locations. It integrates with other services like Athena, EMR, and Redshift Spectrum for querying.

Answer

Amazon DynamoDB

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Amazon Athena data catalog

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Amazon RDS

Question 2

A data scientist is training a large transformer model using SageMaker's model parallelism library. The training job is failing with an out-of-memory (OOM) error. Which two actions can help resolve the OOM error? (Choose two.)

Accepted Answer

Reduce the sequence length. Option A is correct because reducing the sequence length directly lowers the activation memory footprint of a transformer, since attention and intermediate activations scale with sequence length, making it a standard remedy for OOM during SageMaker model-parallel training. Option B is correct because activation checkpointing (gradient checkpointing) recomputes activations during the backward pass instead of storing all of them, substantially reducing memory usage at the cost of some extra compute. Option C is incorrect because increasing the batch size per GPU raises memory consumption and would worsen the OOM. Option D is incorrect because switching to a smaller instance type reduces available GPU memory, making OOM more likely. Option E is incorrect because decreasing the pipeline parallelism degree spreads the model across fewer stages, increasing per-GPU memory pressure rather than relieving it.

Answer

Increase the batch size per GPU

Answer

Switch to a smaller instance type

Answer

Decrease the pipeline parallelism degree

Question 3

A machine learning engineer is setting up an Amazon SageMaker notebook instance. The instance needs to access a private S3 bucket that contains training data. The notebook instance is in a VPC. Which combination of steps will grant access to the S3 bucket? (Choose TWO.)

Accepted Answer

Create a VPC endpoint for S3 in the same VPC and subnet.. Option A is correct because a VPC gateway endpoint for S3 lets instances in the VPC reach S3 privately over the AWS network without needing internet access, NAT, or public IPs, which is exactly what a notebook instance in a VPC requires to reach a private S3 bucket. Option D is correct because S3 access is authorized through IAM: the notebook instance must assume an IAM role whose policy grants the required s3 actions (e.g., s3:GetObject, s3:ListBucket) on the bucket and its objects, and that role must be attached to the instance. Option B is not needed and does not by itself grant S3 permissions, since a public IP only provides internet routing and S3 access still requires IAM authorization. Option C is unnecessary because a NAT gateway only enables outbound internet traffic and would not be required when using an S3 VPC endpoint. Option E is also unnecessary, as an internet gateway only provides VPC internet connectivity and does not authorize or privately route S3 access.

Answer

Assign a public IP address to the notebook instance.

Answer

Set up a NAT gateway in the public subnet.

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Attach an internet gateway to the VPC.

Question 4

A team wants to fine-tune a pre-trained Hugging Face transformer model for text classification using SageMaker. They have a custom training script. Which SageMaker estimator should they use?

Accepted Answer

SageMaker Hugging Face estimator. The SageMaker Hugging Face estimator is purpose-built for Hugging Face transformer models: it uses the official Hugging Face Deep Learning Containers, supports the transformers, datasets, and accelerate libraries, and accepts hyperparameters like epoch, learning_rate, and model_name_or_path. It is the natural choice for fine-tuning a pre-trained Hugging Face model with a custom training script.

Answer

SageMaker generic estimator with a custom container

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SageMaker PyTorch estimator

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SageMaker TensorFlow estimator

Question 5

A company wants to deploy its trained model to edge devices such as cameras and IoT devices. The model must run efficiently with low latency and minimal memory footprint. Which THREE actions should the company take to prepare the model for edge deployment? (Choose THREE.)

Accepted Answer

Use SageMaker Edge Manager to package and manage the model on devices.. Option A is correct because SageMaker Edge Manager packages the model with its runtime, signs it, and manages deployment and monitoring across fleets of edge devices, which is exactly what is needed to operate models on cameras and IoT hardware. Option B is correct because quantization reduces the numerical precision of weights and activations (for example from FP32 to INT8), shrinking the model size and memory footprint while speeding up inference, directly addressing the low-latency and minimal-memory requirements. Option D is correct because SageMaker Neo compiles the trained model for the specific target edge hardware and instruction set, producing an optimized artifact that runs efficiently on the device. Option C is not appropriate because increasing model complexity raises compute and memory demands, working against the edge efficiency goal. Option E is not appropriate because hosting the model as a SageMaker endpoint and calling it over the internet introduces network latency and dependency on connectivity, which contradicts the requirement for low-latency on-device inference.

Answer

Increase the model's complexity to improve accuracy on edge devices.

Answer

Deploy the model directly as a SageMaker endpoint and have the edge devices call it over the internet.

Question 6

A machine learning engineer wants to set up a retraining pipeline that triggers when model quality degrades. Which TWO components are essential for this automated retraining pipeline? (Select TWO)

Accepted Answer

CloudWatch Alarm on model quality metric

Answer

SageMaker Ground Truth to collect new labels

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SageMaker Data Wrangler to preprocess data

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EventBridge rule to schedule retraining weekly

Question 7

A data engineer is designing a feature engineering pipeline using Amazon SageMaker Feature Store. The team needs to support both real-time inference (millisecond latency) and batch training jobs that require access to historical feature values at specific points in time. Which configuration should the engineer choose?

Accepted Answer

Create a feature group with both online and offline stores enabled. Feature Store supports dual storage: an online store (low-latency, key-value) for real-time inference and an offline store (S3-backed, queryable) for batch processing and point-in-time queries.

Answer

Create a feature group with only an online store

Answer

Create separate feature groups — one for online and one for offline — and manage data synchronization manually

Answer

Store features only in the offline store and use a separate low-latency cache like ElastiCache

Question 8

A company is building a CI/CD pipeline for ML models using AWS CodePipeline and SageMaker. The pipeline should include steps to automatically retrain, evaluate, and deploy models. Which THREE components are essential for this pipeline? (Choose three.)

Accepted Answer

SageMaker Pipelines to orchestrate training and evaluation steps.. SageMaker Pipelines (A) is essential because it natively orchestrates the ML workflow steps—data preprocessing, training jobs, and evaluation processing jobs—and integrates directly with CodePipeline as the ML-specific orchestration layer. An Amazon S3 bucket (B) is required since SageMaker training jobs read training data from S3 and write model artifacts (model.tar.gz) back to S3, and CodePipeline itself uses S3 as its artifact store. SageMaker Model Registry (D) is essential to catalog, version, and track approval status of trained models, enabling controlled promotion from evaluation to deployment in the CI/CD flow. Amazon CloudWatch (C) is not essential here because it provides monitoring and logging rather than being a required pipeline component for retrain/evaluate/deploy. AWS Lambda (E) is not essential because evaluation is handled by SageMaker Pipelines processing steps, and CodePipeline can invoke SageMaker actions directly without a custom Lambda trigger.

Answer

Amazon CloudWatch to log API calls.

Answer

AWS Lambda function to trigger evaluation.

Question 9

A company uses SageMaker Model Monitor to detect bias drift in their real-time inference endpoint. They have collected ground truth labels and want to monitor for bias across different demographic groups. Which type of monitoring should they configure?

Accepted Answer

SageMaker Clarify – Bias Drift Monitoring. SageMaker Clarify – Bias Drift Monitoring is the correct choice because it specifically monitors bias metrics over time by comparing ground truth labels with model predictions across different demographic groups. It detects changes in bias metrics such as disparate impact, which is exactly what the company needs.

Answer

SageMaker Model Monitor – Feature Attribution Drift Monitoring

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SageMaker Model Monitor – Model Quality Monitoring

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SageMaker Model Monitor – Data Quality Monitoring

Question 10

Refer to the exhibit. A data engineer investigates why a SageMaker endpoint is returning errors. The endpoint configuration has been updated to point to a new model version. What is the MOST likely cause of the error?

Accepted Answer

The endpoint is still using the previous configuration.. When a SageMaker endpoint configuration is updated to point to a new model version, the endpoint itself does not automatically switch to the new configuration unless a subsequent UpdateEndpoint or Deploy call is made. The endpoint continues serving the previous model until the update is explicitly applied, so the errors are most likely caused by the endpoint still using the old configuration.

Answer

The endpoint instance type is insufficient.

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The container image for the new model is not compatible.

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The IAM role does not have permission to invoke the endpoint.

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The new model artifact is not properly uploaded to S3.

Question 11

A company is adopting Amazon SageMaker Pipelines to automate their ML workflow. They want to choose three key benefits that SageMaker Pipelines provides over traditional manual scripts and ad-hoc steps. Which THREE benefits are correct?

Accepted Answer

Model lineage tracking from raw data to trained model artifacts.. SageMaker Pipelines provides model lineage tracking (Option A) because it automatically records the relationships between data, code, and model artifacts as they flow through pipeline steps, enabling end-to-end traceability from raw data to trained model artifacts. Option C is correct because Pipelines can be triggered by Amazon EventBridge events, such as when new data arrives in S3, enabling event-driven execution rather than manual invocation. Option E is correct because SageMaker Pipelines defines workflows as a directed acyclic graph (DAG) of steps, which makes executions reproducible and allows individual steps or entire pipelines to be re-run with consistent parameters. Option B is not a guaranteed built-in benefit, since deployment to endpoints requires explicit steps such as a model registration or deployment step and is not automatic upon pipeline completion. Option D is also not a SageMaker Pipelines feature, as automatic scaling of compute resources based on data volume is handled by other services or configurations, not by the pipeline orchestration itself.

Answer

Automated deployment of models to endpoints upon pipeline completion.

Answer

Automatic scaling of compute resources based on data volume.

Question 12

A company wants to enable cross-account access to a SageMaker model endpoint. The model is in Account A, and Account B needs to invoke it. Which TWO steps are required? (Select TWO)

Accepted Answer

Attach a resource-based policy to the SageMaker model in Account A allowing access from Account B's IAM role. SageMaker endpoints support resource-based policies that allow cross-account access. By attaching a resource-based policy to the model endpoint in Account A, you can grant the IAM role from Account B explicit permission to invoke the endpoint. This is the standard AWS mechanism for cross-account SageMaker endpoint invocation without needing to duplicate the model.

Answer

Export the model from Account A and re-deploy in Account B

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Configure VPC peering between the two accounts

Answer

Use a SageMaker notebook instance cross-account sharing

Question 13

A company uses SageMaker Pipelines to orchestrate their ML workflow. They notice that if a pipeline step fails due to a transient error (e.g., a brief network issue), the entire pipeline fails and they must manually rerun from the beginning. They want to automatically retry failed steps a few times before failing. What should they do?

Accepted Answer

Configure a RetryPolicy in the pipeline step definition to specify the number of retry attempts and backoff. SageMaker Pipelines supports retry policies for steps. By setting a RetryPolicy in the step definition with a maximum number of retry attempts, the pipeline will automatically retry the step on failure. The other options do not achieve automatic retry within the pipeline: Step Functions would require rebuilding the pipeline, Lambda cannot retry pipeline steps, and CreatePipelineExecution does not handle retries.

Answer

Use a Lambda function to catch step failures and re-invoke the step

Answer

Use the CreatePipelineExecution API with a flag to ignore failures

Answer

Use AWS Step Functions to orchestrate the workflow instead of SageMaker Pipelines

Question 14

A data scientist uses SageMaker Model Monitor to track feature attribution drift. Which technique does SageMaker Model Monitor use to compute feature attributions?

Accepted Answer

SHAP. SageMaker Model Monitor uses SHAP (SHapley Additive exPlanations) to compute feature attributions for model explainability and drift detection. SHAP provides a unified measure of feature importance based on cooperative game theory, ensuring consistent and locally accurate attributions across all features.

Answer

Permutation Feature Importance

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Partial Dependence Plots

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LIME

Question 15

An ML engineer needs to create a feature store that supports both low-latency online inference and large-scale offline training. The features are updated hourly from a streaming source. Which Amazon SageMaker Feature Store configuration should the engineer use?

Accepted Answer

Create a feature group with both online and offline stores enabled.. A SageMaker Feature Store feature group can be configured with both an online store (for low-latency real-time inference) and an offline store (for large-scale training). This single feature group design supports both use cases and is the recommended configuration when features are updated hourly from a streaming source.

Answer

Create a feature group with only an online store enabled.

Answer

Create two separate feature groups: one for online and one for offline.

Answer

Create a feature group with only an offline store enabled.