MLA-C01 Practice Test 11 — 15 Questions

Question 1

A company is using Amazon SageMaker Data Wrangler to prepare a dataset with over 200 features. The dataset includes a categorical feature with more than 10,000 unique values (high cardinality). The ML engineer wants to transform this feature into a numeric representation suitable for a linear model without increasing dimensionality too much. Which built-in transform in Data Wrangler should the engineer use?

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

Accepted Answer

Target encoding. Target encoding is the correct choice because it replaces each category with the mean of the target variable for that category, producing a single numeric column that captures predictive signal without exploding dimensionality. This is ideal for high-cardinality features (e.g., >10,000 unique values) when used with linear models, as it avoids the sparsity and multicollinearity issues of one-hot encoding while retaining correlation with the target.

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Ordinal encoding

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One-hot encoding

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Frequency encoding

Question 2

A company uses Amazon SageMaker to deploy a real-time inference endpoint. They notice increased latency in predictions during peak hours. Which should they investigate first to address the issue?

Accepted Answer

Review the endpoint auto-scaling policy. Increased latency during peak hours is a classic symptom of insufficient compute capacity to handle the request volume. The first step is to review the endpoint's auto-scaling policy to ensure it is configured to scale out instances proactively or reactively based on a relevant metric like 'SageMakerVariantInvocationsPerInstance'. If the policy has a high cooldown period or a low target metric value, it may not add instances quickly enough, causing requests to queue and latency to spike.

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Check the data labeling job status

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Modify the training instance type

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Increase the model artifact size

Question 3

A machine learning team at a retail company has deployed a product recommendation model using Amazon SageMaker. The model is updated weekly with new data. Recently, the team noticed that the model's accuracy on a holdout evaluation set has been declining over the past month. The data pipeline that feeds the training job has not changed. The team suspects data drift. They have SageMaker Model Monitor enabled on the inference endpoint and have set up Amazon CloudWatch metrics for feature distribution distances. Upon reviewing the CloudWatch dashboards, they see that the feature distribution distance metric for the most important feature 'product_category' has increased significantly. However, the team is unsure if this is the root cause. Which remediation step should the team take FIRST?

Accepted Answer

Investigate the data pipeline that feeds the training job to ensure consistent data collection and encoding of the 'product_category' feature. The first step when data drift is suspected is to investigate the data pipeline to ensure consistent data collection and encoding. Since the model's accuracy is declining and the feature distribution distance for 'product_category' has increased, the root cause may be a change in how the feature is collected or encoded upstream, not necessarily a change in the underlying data distribution. SageMaker Model Monitor detects drift in feature distributions, but it cannot diagnose the cause; the team must verify the pipeline before retraining or modifying the model.

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Retrain the model using the most recent week of data and redeploy to the endpoint

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Rebuild the SageMaker endpoint with a different instance type to improve performance

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Reduce the number of features in the model by removing 'product_category'

Question 4

A data scientist is using SageMaker Autopilot for a regression problem. They want to see which data preprocessing steps Autopilot applied. Which TWO sources can they use to find this information?

Accepted Answer

Candidate definition notebook. The candidate definition notebook (option A) is generated by SageMaker Autopilot for each candidate and contains the full ML pipeline code, including the exact data preprocessing and feature engineering transforms that were applied, so it directly answers the question. The data exploration report (option D) is produced during the Autopilot job's data exploration phase and documents the dataset's characteristics along with the preprocessing and feature engineering steps Autopilot selected, making it another valid source. The model leaderboard (option B) only ranks trained candidates by objective metric and does not describe preprocessing. The Autopilot job description in AWS CloudTrail (option C) records API-level audit events, not the internal preprocessing steps. The explainability report (option E) covers feature attributions and model behavior, not the preprocessing pipeline.

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Model leaderboard

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Autopilot job description in AWS CloudTrail

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Explainability report

Question 5

A company is using Amazon SageMaker Ground Truth to create a labeled dataset for object detection in images. The team wants to minimize labeling costs while maintaining high accuracy. Which feature should they use to achieve this?

Accepted Answer

Enable automated data labeling with active learning. Amazon SageMaker Ground Truth's automated data labeling uses active learning to train a model on the labels already produced by humans, then automatically labels the high-confidence data and only sends low-confidence samples to human labelers. This dramatically reduces the number of human-labeled items required, cutting costs while preserving accuracy because humans still validate uncertain cases. This is the specific cost-optimization feature designed for exactly this scenario.

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Use a private workforce of internal employees

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Use a larger initial training set with pre-labeled public datasets

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Reduce the number of label categories

Question 6

A team wants to automate the retraining and deployment of an ML model whenever new labeled data arrives in S3. The workflow includes data preprocessing, training, evaluation, and conditional deployment. Which AWS service is best suited for orchestrating this end-to-end pipeline?

Accepted Answer

Amazon SageMaker Pipelines triggered by S3 events via EventBridge.. Amazon SageMaker Pipelines is purpose-built for ML workflows, offering native integration with SageMaker for training, evaluation, and conditional deployment steps. Triggered by S3 events via Amazon EventBridge, it automates the end-to-end pipeline from data preprocessing to conditional model deployment without requiring custom orchestration code.

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AWS Step Functions with Lambda functions for each step.

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AWS Glue workflows with triggers based on S3 events.

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AWS CodePipeline with source from S3 and build from CodeBuild.

Question 7

A company uses Amazon SageMaker Pipelines for automated retraining. The pipeline includes a processing step that runs a Python script. The script uses the boto3 library to call an AWS service, but the calls are being throttled. What is the MOST effective way to address this within the pipeline?

Accepted Answer

Modify the Python script to include retry with exponential backoff when receiving throttling exceptions.. Implementing retry with exponential backoff directly in the Python script is the most effective way to handle transient throttling exceptions from AWS service API calls. This approach is a best practice for managing service limits within a SageMaker Pipeline processing step, as it allows the script to automatically recover from throttling without modifying the pipeline structure or requiring manual intervention.

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Increase the instance count for the processing step to distribute the API calls.

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Request a service quota increase for the throttling limit.

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Add a wait step in the pipeline before the processing step.

Question 8

A machine learning engineer needs to deploy a TensorFlow model to Amazon SageMaker and wants to use the built-in TensorFlow Serving container. What should the engineer provide in the model archive?

Accepted Answer

A tar.gz file containing the TensorFlow SavedModel.. The built-in TensorFlow Serving container in Amazon SageMaker expects a TensorFlow SavedModel packaged in a tar.gz archive. This is because TensorFlow Serving natively loads models from the SavedModel format, which includes the model's computational graph, weights, and assets in a standardized directory structure. Providing a tar.gz of the SavedModel ensures compatibility with the container's default serving stack without requiring custom inference code.

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A frozen graph of the TensorFlow model.

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Model artifacts and a Python inference script.

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A Dockerfile and model artifacts.

Question 9

A team is evaluating classification models for a medical diagnosis application. The cost of a false negative is much higher than the cost of a false positive. Which metric should be optimized during model selection?

Accepted Answer

Recall. Recall (sensitivity) measures the proportion of actual positives correctly identified, which directly minimizes false negatives. In medical diagnosis, missing a disease (false negative) is far more costly than a false alarm, so optimizing recall ensures the model captures as many true positive cases as possible.

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Accuracy

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F1 score

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Precision

Question 10

A machine learning team notices an increase in 5XXError count for a SageMaker endpoint. They want to set up automated remediation. Which THREE actions should they take? (Select THREE)

Accepted Answer

Add an SNS topic as the alarm action. Option C is correct because a CloudWatch Alarm on the SageMaker endpoint's 5XXError metric is the foundational detection mechanism that turns the rising error count into an actionable state change. Option A is correct because attaching an SNS topic as the alarm action provides the notification/event distribution channel that can fan out the alert to subscribers or trigger downstream automation. Option E is correct because a Lambda function invoked by the alarm (directly or via SNS) performs the automated remediation, such as restarting the endpoint or scaling out its instance count to restore availability. Option B is wrong because manually increasing the instance count is a human intervention, not automated remediation, which is what the team requires. Option D is wrong because enabling detailed monitoring only increases metric granularity (e.g., 1-minute CloudWatch metrics) and does not by itself detect-and-remediate the 5XXError increase.

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Increase the endpoint instance count manually

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Enable detailed monitoring on the endpoint

Question 11

A company is deploying a foundation model using SageMaker JumpStart. They want to minimize inference costs while maintaining low latency. Which TWO strategies should they consider? (Select TWO)

Accepted Answer

Enable auto-scaling with a target tracking policy based on Invocations per instance. Auto-scaling with a target tracking policy based on Invocations per instance dynamically adjusts the number of instances to match demand, ensuring you only pay for the compute capacity you need while maintaining low latency. This avoids over-provisioning and reduces idle costs, directly addressing the goal of minimizing inference costs.

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Enable data capture for all requests to analyze usage patterns

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Use SageMaker Savings Plans for discounted compute rates

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Deploy the model on a single large instance to maximize throughput

Question 12

A company uses an Amazon SageMaker endpoint for real-time inference. The security team requires that all traffic between the endpoint and the client application be encrypted in transit. Which configuration ensures this?

Accepted Answer

The endpoint is automatically served over HTTPS; no additional configuration is needed.. Amazon SageMaker endpoints are automatically served over HTTPS, which encrypts all data in transit between the client application and the endpoint. This is a default behavior of SageMaker real-time inference endpoints, so no additional configuration is required to meet the encryption-in-transit requirement.

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Deploy the endpoint in a VPC and use VPC Endpoints.

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Use AWS Key Management Service (KMS) to encrypt the data in transit.

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Attach an AWS Certificate Manager (ACM) certificate to the endpoint.

Question 13

A data scientist notices that a production model's accuracy has degraded over the past week. The training data distribution remains unchanged, but the relationship between features and the target has shifted. Which type of drift is occurring, and which monitoring approach should be used?

Accepted Answer

Concept drift; use SageMaker Model Monitor model quality monitoring with ground truth labels. Concept drift occurs when the underlying relationship between features and target changes. Model quality monitoring (comparing predictions against ground truth) detects this. Data drift monitors feature distribution changes, which are not present here.

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Bias drift; use SageMaker Clarify post-deployment bias monitoring

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Data drift; use SageMaker Model Monitor data quality monitoring

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Feature attribution drift; use SageMaker Clarify

Question 14

A data engineer needs to prepare a large dataset (10 TB) stored in Amazon S3 for a training job on SageMaker. The data is in CSV format, but the training algorithm expects Parquet for performance. The engineer must transform the data with minimal cost and without writing custom code. Which service should be used?

Accepted Answer

Use AWS Glue to create a crawler and ETL job that converts CSV to Parquet.. AWS Glue is the correct choice because it provides a serverless, pay-per-use ETL service that can automatically convert CSV to Parquet without writing custom code. The Glue crawler infers the schema, and the ETL job uses built-in transforms to efficiently handle 10 TB of data with minimal cost, as it only charges for the resources consumed during the job execution.

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Use SageMaker Processing with a TensorFlow script to read CSV and write Parquet.

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Use Amazon S3 Select to convert the data to Parquet during retrieval.

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Use Amazon EMR with a Spark job to convert the files.

Question 15

An ML team uses SageMaker Pipelines to automate model retraining. They want to skip redundant training steps when input data has not changed. Which feature should they enable?

Accepted Answer

Pipeline caching. SageMaker Pipelines caching stores the output of a step keyed by the step's inputs (code, data, hyperparameters, etc.). When a pipeline run executes and the inputs are unchanged, the cached output is reused and the step is skipped, avoiding redundant training. This directly addresses the requirement to skip training when input data hasn't changed.

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Pipeline variable expressions

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Model registry approval

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Step parallelism