MLA-C01 Practice Test 19 — 15 Questions

Question 1

A data engineer is using AWS Glue to prepare a dataset for ML. The engineer wants to split the dataset into training and testing sets while preserving the distribution of the target variable. Which TWO methods achieve this goal? (Select TWO)

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

Accepted Answer

Use the `train_test_split` function from scikit-learn in a SageMaker notebook. The `train_test_split` function from scikit-learn supports the `stratify` parameter, which preserves the distribution of the target variable when splitting a dataset into training and testing sets. This is a standard, reliable method for stratified splitting in Python-based ML workflows, and it can be used directly in a SageMaker notebook.

Answer

Use Amazon Athena to create views with random sampling

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Use AWS Glue's built-in random split transform

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Use Amazon SageMaker's built-in SplitType parameter in a Processing Job

Question 2

A SageMaker endpoint is logging an error when processing inference requests that require database access. What is the most likely cause?

Accepted Answer

The endpoint lacks a VPC configuration with proper security groups. When a SageMaker endpoint needs to access an external database (e.g., Amazon RDS) during inference, it must be launched within a VPC that has proper security group and subnet configurations. If the endpoint is not in a VPC or the security groups do not allow outbound traffic to the database, the endpoint will be unable to connect, resulting in errors. The other options are less likely: data capture is for logging requests (A), instance size affects performance not connectivity (B), model compatibility affects deployment but not specifically database access (C).

Answer

Data capture is not enabled

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The endpoint instance type is too small

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The model is not compatible with the instance

Question 3

A team uses SageMaker Clarify to monitor bias drift on a deployed model. They have defined a baseline with training data and set up a monitoring schedule. After one month, they receive a violation report indicating that the post-training metrics have deviated from the baseline. What does this violation indicate?

Accepted Answer

The model's predictions relative to sensitive attributes have shifted compared to the training baseline. SageMaker Clarify bias drift monitoring compares predicted outcomes (post-training) against the baseline to detect changes in fairness metrics like disparate impact. It does not measure prediction accuracy or data quality.

Answer

The SHAP values for features have changed

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The model's predictions are becoming less accurate

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The distribution of input features has changed

Question 4

A machine learning team is building a model to predict customer churn. They have historical data that includes customer activity logs, each with a timestamp. The team wants to ensure that the training data does not contain any data leakage from the future. Which approach should they take when preparing the training and validation datasets?

Accepted Answer

Split the data by time, using data before a certain date for training and after for validation. Splitting by time (chronological split) prevents data leakage by ensuring that the validation set contains only future data relative to the training set. In time-series or timestamped data, random splits can allow the model to learn from future patterns, artificially inflating performance. This approach respects the temporal dependency inherent in customer churn prediction.

Answer

Use stratified sampling based on churn label

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Randomly split the data 80/20 for training and validation

Answer

Use k-fold cross-validation with shuffling

Question 5

An MLOps team is designing a SageMaker Pipeline to automate model retraining. The pipeline must: (1) run training only if new training data is available, (2) register the model in SageMaker Model Registry only if evaluation metrics exceed a threshold, (3) deploy the approved model to a staging endpoint automatically. Which THREE steps should they include? (Choose THREE.)

Accepted Answer

ConditionStep to check if evaluation metrics exceed the threshold. Option E (ProcessingStep to check for new training data availability) is correct because a ProcessingStep can run a script or job that inspects the input data source and outputs a boolean/property indicating whether new data exists, which is exactly what requirement (1) needs before training runs. Option A (ConditionStep to check if evaluation metrics exceed the threshold) is correct because ConditionStep is the pipeline construct that evaluates a condition (e.g., a metric from a previous evaluation step against a threshold) and gates downstream steps, satisfying requirement (2). Option B (RegisterModel step to register the model in the Model Registry) is correct because RegisterModel is the dedicated SageMaker Pipelines step that creates a model package in the Model Registry, which is required to register the model once the metric condition passes. Option C (TuningStep) is not correct because hyperparameter optimization is not required by any of the stated requirements. Option D (TransformStep) is not correct because TransformStep runs batch transform inference jobs, not endpoint deployment; deploying to a staging endpoint would use a different mechanism such as a LambdaStep or a model deployment step, so it does not satisfy requirement (3).

Answer

TuningStep to perform hyperparameter optimization

Answer

TransformStep to deploy the model to a staging endpoint

Question 6

A machine learning engineer is using SageMaker Automatic Model Tuning (AMT) to optimize hyperparameters for a random forest model. The engineer notices that the tuning job is taking too long and many hyperparameter combinations are being evaluated but not improving the objective metric. Which action should the engineer take to make the tuning more efficient?

Accepted Answer

Enable early stopping for the tuning job. Enabling early stopping in SageMaker Automatic Model Tuning (AMT) terminates poorly performing training jobs before they complete, which reduces wasted compute time and speeds up the tuning process. This is especially effective when using Bayesian optimization, as it allows the algorithm to focus on promising hyperparameter regions and avoid evaluating combinations that are unlikely to improve the objective metric.

Answer

Switch the strategy from Bayesian to random search

Answer

Use a smaller instance type for each training job

Answer

Increase the maximum number of training jobs

Question 7

An ML team is deploying a model using SageMaker. The model requires GPU inference and must be available in multiple AWS regions for low latency. The team has created a multi-model endpoint with GPU instances. After deployment, they notice high latency spikes when a new model is loaded. What is the most likely cause?

Accepted Answer

The team is using a multi-model endpoint, which loads models on demand; loading a model into GPU memory causes latency spikes.. A multi-model endpoint (MME) loads models on demand from Amazon S3 into the instance's memory. When a new model is requested and not already cached, SageMaker must download the model artifacts and load them into GPU memory, which is a time-consuming operation that causes a latency spike for the first inference request. This cold-start behavior is inherent to MMEs and explains the observed spikes.

Answer

The endpoint is configured with a single production variant, causing all traffic to overload one instance.

Answer

The endpoint is using the wrong instance type that lacks sufficient GPU memory.

Answer

The model is too large for the specified container memory, causing swap to disk.

Question 8

A team needs to deploy a model that has compliance requirements to log all inference requests and responses for auditing. The model will be served using a real-time endpoint. How can they achieve this without custom code?

Accepted Answer

Enable SageMaker Data Capture on the endpoint. SageMaker Data Capture is the native, no-code feature that automatically logs inference requests and responses for real-time endpoints. It captures payload data to an S3 bucket without requiring any custom code, directly meeting the compliance requirement for audit logging.

Answer

Add a custom Lambda function using a container

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Use SageMaker Debugger to monitor inference

Answer

Enable CloudTrail for the endpoint

Question 9

An ML team has deployed a model to a SageMaker real-time endpoint and wants to set up automated monitoring for model quality. Which TWO elements are required to configure SageMaker Model Monitor for model quality? (Select TWO.)

Accepted Answer

A ground truth labels dataset for comparison. SageMaker Model Monitor for model quality requires a ground truth labels dataset to compare the model's predictions against actual outcomes. This comparison is essential for calculating quality metrics like accuracy, precision, recall, or F1 score, which indicate how well the model is performing over time.

Answer

SHAP values for feature attribution

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A constraints file with allowed deviation thresholds

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A baseline statistics file derived from the training data

Question 10

A company's ML pipeline runs in multiple AWS accounts (dev, test, prod). They want to enforce that only approved models from a central Model Registry can be deployed to the production account. Which combination of services is MOST appropriate to implement this governance?

Accepted Answer

AWS Organizations with SCPs, AWS CodePipeline with cross-account actions, and SageMaker Model Registry with approval status.. It combines AWS Organizations with SCPs to enforce cross-account deployment policies, AWS CodePipeline with cross-account actions to orchestrate the pipeline across dev/test/prod accounts, and SageMaker Model Registry with approval status to gate deployments to only approved models. This ensures that only models with an 'Approved' status in the central registry can be deployed to the production account, meeting the governance requirement.

Answer

AWS Config, Amazon GuardDuty, and AWS Security Hub.

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Amazon API Gateway, AWS Step Functions, and Amazon DynamoDB.

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AWS Service Catalog, AWS KMS, and AWS CloudTrail.

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AWS CloudFormation StackSets, Amazon EventBridge, and AWS Lambda.

Question 11

A data engineer is preparing a dataset for a k-means clustering algorithm. The features have different scales: age (18-100), income ($20k-$200k), and number of purchases (0-50). Without scaling, which feature will dominate the distance calculations?

Accepted Answer

Income. Income has the largest range (180,000 compared to 82 and 50), so it will dominate Euclidean distance calculations. Standardization or normalization is needed before clustering.

Answer

All features will contribute equally

Answer

Number of purchases

Answer

Age

Question 12

A company needs to deploy a large language model (LLM) on SageMaker with the Triton Inference Server to maximize GPU utilization and reduce latency. They have an NVIDIA A100 GPU. Which SageMaker inference option supports Triton?

Accepted Answer

SageMaker real-time endpoint using a Triton Inference Server container. SageMaker real-time endpoints support the Triton Inference Server through a pre-built container that integrates with NVIDIA A100 GPUs, enabling dynamic batching and concurrent model execution to maximize GPU utilization and reduce latency. Triton is designed for high-throughput inference on GPU hardware, making it the correct choice for this scenario.

Answer

SageMaker Batch Transform with Triton

Answer

SageMaker Serverless Inference with a custom container

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SageMaker Neo compiled model on a CPU endpoint

Question 13

A team uses SageMaker Pipelines to automate retraining. They want to skip the training step if the data has not changed since the last run. Which feature should they enable?

Accepted Answer

Step caching. Step caching in SageMaker Pipelines allows you to reuse the output from a previous execution of a step if its input data and configuration parameters have not changed. By enabling caching on the training step, the pipeline automatically skips re-executing that step when the data is identical, saving time and cost. This directly addresses the requirement to skip retraining when data has not changed.

Answer

Parameterized executions

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Lineage tracking

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Condition step with a custom check

Question 14

A data scientist is building a regression model to predict house prices. The dataset contains a feature 'neighborhood' with 500 distinct values, and most neighborhoods have fewer than 10 samples. Which approach is MOST appropriate for handling this high-cardinality categorical feature?

Accepted Answer

Use target encoding with proper cross-validation to avoid data leakage. Target encoding replaces each neighborhood with a statistic (e.g., mean house price) computed from the target, which handles high cardinality efficiently. Because the encoding uses the target, it must be computed with cross-validation (out-of-fold) to prevent leakage of the target into training features. This preserves the predictive signal of neighborhood while keeping dimensionality low.

Answer

Drop the neighborhood feature entirely

Answer

Apply frequency encoding, replacing each neighborhood with its count in the training set

Answer

One-hot encode the feature and use L1 regularization

Question 15

A data scientist needs to ingest streaming customer clickstream data from a website into an S3 data lake for ML training. The data must be delivered within 1 minute of ingestion, and JSON records must be converted to Parquet. Which AWS service combination should be used?

Accepted Answer

Amazon Kinesis Data Firehose with a 60-second buffer and Parquet conversion enabled. Amazon Kinesis Data Firehose can buffer incoming data and deliver to S3 with a 60-second buffer window, and it supports converting JSON to Parquet. KDS alone does not convert to Parquet. Glue ETL can do the conversion but adds latency. Lambda with S3 trigger is not streaming-oriented.

Answer

Amazon S3 Transfer Acceleration with direct PUT requests by clients

Answer

Amazon Kinesis Data Streams (KDS) with a Lambda consumer that writes JSON to S3

Answer

AWS Glue ETL job triggered every minute to pull from Kinesis Data Streams and write Parquet to S3