MLA-C01 Practice Test 43 — 15 Questions

Question 1

A machine learning team is using Amazon SageMaker to train a model. They notice that the training job is taking longer than expected and the logs show repeated warnings about 'loss not decreasing'. Which SageMaker feature should they use to diagnose and visualize the training process?

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

Accepted Answer

Amazon SageMaker Debugger. Amazon SageMaker Debugger is the correct choice because it provides real-time monitoring and visualization of training metrics, including loss values, gradients, and weights. The repeated 'loss not decreasing' warnings indicate a training issue (e.g., vanishing gradients or learning rate problems), and Debugger can capture these tensors and emit alerts or trigger actions (like stopping the job) via built-in or custom rules. It also integrates with SageMaker Studio for interactive visualization of the training progress.

Answer

Amazon SageMaker Clarify

Answer

Amazon SageMaker Experiments

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Amazon SageMaker Model Monitor

Question 2

A machine learning engineer wants to monitor a deployed model for data drift. Which SageMaker feature should they use to automatically detect drift in the input data distribution compared to the training data baseline?

Accepted Answer

SageMaker Model Monitor. SageMaker Model Monitor can be configured to run monitoring jobs that compare live inference data against a baseline created from training data to detect data drift.

Answer

SageMaker Pipelines

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SageMaker Debugger

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SageMaker Clarify

Question 3

A company deploys a model for fraud detection. They need to monitor for bias after deployment, specifically whether the model's false positive rate changes across demographic groups over time. Which SageMaker feature should they use?

Accepted Answer

SageMaker Clarify (post-deployment bias monitoring). SageMaker Clarify provides post-deployment bias monitoring by analyzing predictions against ground truth labels for defined facets. It can track metrics like false positive rate differences over time.

Answer

SageMaker Model Monitor – Model Quality

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SageMaker Model Monitor – Feature Attribution Drift

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SageMaker Model Monitor – Data Quality

Question 4

A data scientist wants to track the lineage of models, datasets, and training jobs in SageMaker. Which SageMaker feature should they use to capture these relationships as artifacts and actions?

Accepted Answer

SageMaker ML Lineage Tracking. SageMaker ML Lineage Tracking creates a graph of artifacts (datasets, models) and actions (training jobs, endpoints) to track the provenance of ML workflows.

Answer

SageMaker Model Registry

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SageMaker Experiments

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SageMaker Feature Store

Question 5

A company is deploying a SageMaker endpoint and must meet strict security requirements: no public internet access, all inter-container traffic must be encrypted, and all data at rest must be encrypted with a customer-managed KMS key. Which THREE configurations should they apply? (Choose THREE.)

Accepted Answer

Configure the endpoint to use VPC-only mode

Answer

Attach a security group that only allows inbound traffic from the VPC CIDR

Answer

Enable network isolation mode on the endpoint

Question 6

An organization needs to ensure that all data used for inference on a SageMaker endpoint is encrypted at rest. The endpoint uses a SageMaker-provided container. Which configuration should be applied?

Accepted Answer

Specify a KMS key in the endpoint configuration. SageMaker endpoints use AWS KMS for encryption at rest. By specifying a KMS key in the endpoint configuration, the data in the attached ML storage volume is encrypted. Inter-container traffic encryption is for encryption in transit.

Answer

Use a custom container with built-in encryption

Answer

Enable network isolation mode

Answer

Enable inter-container traffic encryption

Question 7

An ML engineer monitors a SageMaker endpoint for data drift. They set up SageMaker Model Monitor to compare inference data against a baseline created from the training dataset. The monitoring schedule runs daily and reports violations. Which monitoring type should be configured to detect if the distribution of a numerical feature in real-time inference data differs significantly from the training distribution?

Accepted Answer

Data quality monitoring. SageMaker Model Monitor's data quality monitoring detects feature distribution drift (statistical drift) between baseline and live data. Model quality monitoring requires ground truth labels, bias drift monitors fairness metrics, and feature attribution drift monitors SHAP values.

Answer

Feature attribution drift monitoring

Answer

Bias drift monitoring

Answer

Model quality monitoring

Question 8

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

Answer

SageMaker Data Wrangler to preprocess data

Answer

EventBridge rule to schedule retraining weekly

Question 9

Refer to the exhibit. A team has configured data capture for a SageMaker endpoint. The endpoint is returning predictions but no captured data appears in the S3 bucket. What is the most likely cause?

Accepted Answer

The IAM role for the endpoint does not have s3:PutObject permission.. The most likely cause is that the IAM role associated with the SageMaker endpoint lacks the `s3:PutObject` permission. Without this permission, the endpoint can generate capture data internally but cannot write it to the specified S3 bucket, resulting in no captured data appearing even though predictions are returned successfully.

Answer

The InitialSamplingPercentage is too low.

Answer

The capture status is 'Configured' but not 'Running'.

Answer

The endpoint is not receiving any traffic.

Question 10

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 11

A company runs a batch inference job on 10 TB of image data stored in S3. Each image needs to be processed by a GPU-accelerated model. The job is not time-sensitive and cost is the primary concern. Which SageMaker option is MOST appropriate?

Accepted Answer

SageMaker Batch Transform with GPU instance and spot instances. Batch Transform with GPU spot instances is the most cost-effective choice for a non-time-sensitive, large-scale batch inference job on 10 TB of data. Spot instances offer up to 90% cost savings over on-demand, and Batch Transform natively handles splitting the dataset, distributing work across instances, and writing results to S3 without requiring a persistent endpoint.

Answer

SageMaker Serverless Inference

Answer

SageMaker Async Inference with GPU

Answer

SageMaker real-time endpoint on GPU instances

Question 12

A team is developing a model to predict customer churn. The dataset has 10,000 samples with 20 features. The target variable is binary with 15% churn rate. The team wants to use logistic regression. Which data preprocessing step is MOST important to ensure proper convergence?

Accepted Answer

Standardize the features to have zero mean and unit variance. Logistic regression uses gradient descent or similar optimization algorithms that rely on the scale of the features. When features have different units or magnitudes, the cost function becomes elongated, causing slow or unstable convergence. Standardizing to zero mean and unit variance ensures that all features contribute equally to the gradient updates, leading to faster and more reliable convergence.

Answer

Remove correlated features to reduce multicollinearity

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Impute missing values with the median

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Apply SMOTE to balance the classes

Question 13

A company wants to use SageMaker to serve real-time predictions with a model that has a large memory footprint. They need to ensure the endpoint can handle traffic spikes. Which scaling policy should they use?

Accepted Answer

Target tracking policy. Target tracking scaling policy is the correct choice because it automatically adjusts the number of instances in the SageMaker endpoint based on a target metric, such as InvocationsPerInstance or ModelLatency, to handle traffic spikes without manual intervention. This policy is ideal for real-time inference with large memory models because it dynamically scales resources up or down to maintain the target metric, ensuring consistent performance during unpredictable traffic bursts.

Answer

Simple scaling policy

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Scheduled scaling policy

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Step scaling policy

Question 14

A company is deploying a multi-model endpoint using SageMaker to serve multiple models from a single endpoint. They notice that one model consumes excessive memory and impacts others. What is the BEST practice to isolate resource usage?

Accepted Answer

Use separate endpoints for each model.. Using separate endpoints for each model ensures complete resource isolation at the instance level. When one model consumes excessive memory, it cannot impact others because each model runs on its own dedicated endpoint with its own compute resources. This is the best practice for isolating resource usage in production environments where memory-intensive models are deployed.

Answer

Configure instance type with more memory.

Answer

Use SageMaker Model Parallelism.

Answer

Use multi-model endpoint with model cache size limit.

Question 15

A data scientist wants to train a model on SageMaker using a custom PyTorch script, then register the best model in the SageMaker Model Registry. The training job is part of a SageMaker Pipeline. Which pipeline step should be used to register the model?

Accepted Answer

RegisterModelStep. The `RegisterModelStep` is specifically designed to create a model resource and register it in the SageMaker Model Registry as part of a pipeline. It takes the training output (e.g., model artifacts from a `TrainingStep`) and packages it with the specified inference image and metadata, then creates a model package group version. This is the correct step for registering a model after training, as it directly integrates with the Model Registry for versioning and approval workflows.

Answer

CreateModelStep

Answer

TrainingStep

Answer

TransformStep