MLA-C01 Practice Test 17 — 15 Questions

Question 1

A machine learning engineer is building an ML pipeline using Amazon SageMaker. The engineer needs to prepare the data, detect bias in the dataset, and then create features for training. Which TWO AWS services or features should the engineer use? (Choose TWO.)

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

Accepted Answer

Amazon SageMaker Clarify. Amazon SageMaker Data Wrangler (D) is the correct service for data preparation and feature engineering within SageMaker, as it provides a visual interface to import, clean, transform, and create features from data sources like S3, Athena, and Redshift, and can export the resulting flow directly into a SageMaker Pipeline. Amazon SageMaker Clarify (C) is the correct service for detecting bias in datasets and models, since it computes bias metrics such as class imbalance (CI) and difference in positive proportions in labels (DPL) and integrates with SageMaker for pre-training bias detection. AWS Glue DataBrew (A) is a separate visual data preparation tool outside the SageMaker-native pipeline, so it does not fit the requirement to build the pipeline within SageMaker. Amazon SageMaker Model Monitor (B) detects data drift and quality issues on deployed endpoints after training, not dataset bias or feature creation. Amazon SageMaker Feature Store (E) stores and serves curated features for training and inference but does not prepare data or detect bias.

Answer

AWS Glue DataBrew

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Amazon SageMaker Model Monitor

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Amazon SageMaker Feature Store

Question 2

A company wants to monitor its Amazon SageMaker real-time endpoint for data quality issues. Which TWO actions should the company take?

Accepted Answer

Create a baseline from the training data to compare against live data.. Option A is correct because SageMaker Model Monitor requires a baseline (statistics and constraints) generated from the training dataset, which is then compared against the live inference data to detect data quality drift. Option E is correct because Model Monitor relies on data capture, which must be enabled on the real-time endpoint to record the request and response payloads into an Amazon S3 location for monitoring. Option B is incorrect because SageMaker Debugger analyzes training jobs for issues like vanishing gradients or overfitting, not live endpoint data quality. Option C is incorrect because a Lambda function for preprocessing requests does not provide data quality monitoring capabilities. Option D is incorrect because S3 bucket notifications on model artifacts only signal object events and do not evaluate inference data quality.

Answer

Use SageMaker Debugger to analyze training jobs.

Answer

Set up an AWS Lambda function to preprocess incoming requests.

Answer

Configure Amazon S3 bucket notifications for model artifacts.

Question 3

A company has 50 small PyTorch models that are used infrequently for inference. They want to minimize costs while maintaining the ability to serve all models from a single endpoint. Which SageMaker feature should they use?

Accepted Answer

Multi-model endpoint. SageMaker multi-model endpoints allow hosting multiple models on a single endpoint, loading them on demand and sharing the same serving container. This is cost-effective for many infrequently used models because you only pay for the endpoint instance and not for separate endpoints per model. It supports hundreds of models and dynamically loads them from S3.

Answer

Multi-container endpoint

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Batch transform job

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Real-time endpoint with 50 production variants

Question 4

A financial services company operates a real-time inference endpoint for a fraud detection model on Amazon SageMaker. The model was trained on historical transaction data from 2023. Over the past month, the model's precision has dropped from 92% to 78%, while recall remains high at 95%. The data science team suspects data drift and has already enabled SageMaker Model Monitor with data capture and a baseline from the training data. The latest monitoring report indicates no statistically significant drift in any of the input features. The team also verified that the inference code and model artifact have not changed. Despite the stable feature distributions, the model is misclassifying an increasing number of legitimate transactions as fraudulent (false positives). The business is concerned about the impact on customer experience. What is the best course of action?

Accepted Answer

Investigate recent ground truth labels to check for label drift or changes in the fraud definition.. The model's precision dropped while recall remained high, meaning it is flagging more legitimate transactions as fraudulent (false positives). Since input feature distributions show no drift and the model artifact is unchanged, the issue likely lies in the target variable—label drift or a change in the definition of fraud. Investigating recent ground truth labels will reveal if the labels used for evaluation or retraining have shifted, which would explain the degradation. This is the best course of action before retraining or changing the model.

Answer

Replace the model with a more complex algorithm such as a gradient-boosted tree.

Answer

Retrain the model using the most recent 30 days of transaction data with automated retraining pipelines.

Answer

Increase the data capture sampling percentage from 10% to 100% for more detailed analysis.

Question 5

A company is building a time series forecasting model using SageMaker DeepAR. The raw data is a CSV with columns: timestamp, item_id, and value. What is the correct data format required for DeepAR training?

Accepted Answer

JSON Lines files with 'start', 'target', and optional fields per time series. DeepAR requires time series data to be provided in JSON Lines format, where each line represents a single time series with a 'start' timestamp (in ISO 8601 format), a 'target' array of values, and optional fields like 'cat' for categorical features. This structured format allows DeepAR to handle variable-length sequences and missing values natively, which is not possible with simple CSV or wide-format data.

Answer

A wide-format CSV where each column is a different time series

Answer

Parquet files with a schema containing timestamp, item_id, and value

Answer

A single CSV file with columns: timestamp, item_id, value

Question 6

A financial services company is deploying a model for loan approval. They must ensure that the model's predictions do not show bias against protected groups. They plan to monitor for bias drift after deployment. Which SageMaker feature should they use?

Accepted Answer

SageMaker Clarify with bias drift detection.. SageMaker Clarify is the correct choice because it provides built-in bias detection and monitoring capabilities, including the ability to detect bias drift over time after deployment. It can analyze predictions for protected groups and generate reports on metrics like disparate impact and conditional demographic disparity, which directly addresses the requirement to monitor for bias drift post-deployment.

Answer

SageMaker Model Monitor with data quality monitoring.

Answer

SageMaker Debugger to capture tensors.

Answer

SageMaker Ground Truth for fairness labels.

Question 7

A machine learning engineer needs to split a dataset for binary classification where the positive class represents only 2% of the data. Which data splitting strategy ensures that both training and test sets maintain the same class proportion as the original dataset?

Accepted Answer

Stratified sampling based on the target variable. Stratified sampling explicitly preserves the class distribution of the original dataset in each split. For a binary classification problem with a 2% positive class, stratification ensures that both the training and test sets contain approximately 2% positive examples, preventing a scenario where random chance could yield a test set with zero or very few positives. This is critical for imbalanced datasets because it maintains representativeness and allows reliable evaluation of metrics like recall or F1-score for the minority class.

Answer

Time-series split respecting the timestamp order

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Simple random split with a 80/20 ratio

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K-fold cross-validation without stratification

Question 8

A company is building a sentiment analysis model for customer reviews. The text data contains many typos, abbreviations, and informal language. Which text preprocessing step would be most beneficial to support the ML model?

Accepted Answer

Apply spell-check and normalize abbreviations. Typos, abbreviations, and informal language introduce noise that can cause the model to treat the same word as different tokens (e.g., 'gr8' vs 'great'), degrading sentiment classification. Applying spell-check and normalizing abbreviations standardizes the text so the model learns consistent patterns. This directly addresses the specific data quality issue described.

Answer

Apply stemming and lowercasing only

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Remove all stop words

Answer

Tokenize using whitespace tokenizer

Question 9

A team has 200 small ML models that need to be served via HTTPS endpoints. Each model is used infrequently, and the team wants to minimize hosting costs. Which SageMaker deployment approach is MOST cost-effective?

Accepted Answer

Use a single multi-model endpoint (MME). A single multi-model endpoint (MME) hosts many models behind one HTTPS endpoint and loads them on demand into shared compute, which is ideal for 200 infrequently used small models because you pay for one endpoint's underlying instances rather than 200 separate ones. SageMaker dynamically loads and unloads models from S3 as requests arrive, dramatically reducing hosting cost while still providing real-time HTTPS inference.

Answer

Use SageMaker Serverless Inference for each model

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Deploy each model on a separate real-time endpoint

Answer

Use Batch Transform for all models

Question 10

A data science team wants to automate the retraining of a model when data drift is detected. Which TWO AWS services should they use in combination to achieve this? (Choose TWO)

Accepted Answer

SageMaker Model Monitor. SageMaker Model Monitor (C) is the correct service for detecting data drift: it continuously monitors a deployed model's endpoint, compares incoming inference data against a baseline, and can emit CloudWatch metrics/alerts when drift (or data quality, bias, or feature attribution issues) is detected. AWS Lambda (D) is the correct companion service because it can be triggered by those CloudWatch alarms/events to run the retraining logic — for example, invoking a SageMaker training job or pipeline to retrain and redeploy the model automatically. Together they form the detect-then-retrain automation loop the team needs. AWS Cloud9 (A) is only a cloud IDE and provides no monitoring or automation capability. Amazon DynamoDB (B) is a NoSQL database and does not detect drift or orchestrate retraining. Amazon Kinesis Data Analytics (E) is for real-time stream processing with SQL/Flink, not for model drift detection or triggering retraining workflows.

Answer

AWS Cloud9

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Amazon DynamoDB

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Amazon Kinesis Data Analytics

Question 11

A data engineer is using Amazon SageMaker Ground Truth to create a labeled dataset for an object detection task. The dataset contains millions of images, and the labeling budget is limited. Which approach can reduce labeling costs while maintaining high model accuracy?

Accepted Answer

Enable active learning in Ground Truth to automatically select a subset of images for human labeling. Ground Truth active learning (automated data labeling) uses a machine learning model to identify the most informative unlabeled images — those where the model is least confident — and sends only those to human labelers. This reduces the number of human annotations required while maintaining model accuracy, directly addressing the limited labeling budget for millions of images. The other options either label everything (expensive), use the wrong tool, or skip human review entirely (risking accuracy).

Answer

Label 100% of the images using a pre-built worker template to ensure accuracy

Answer

Use Amazon SageMaker Data Wrangler to annotate images

Answer

Use automated labeling with a pre-trained model for all images and skip human review

Question 12

A company wants to deploy a PyTorch model on SageMaker using the NVIDIA Triton Inference Server for GPU acceleration. They have an existing Triton configuration. Which approach should they take?

Accepted Answer

Use the SageMaker Triton Inference Server container from the Deep Learning Containers. AWS provides a pre-built SageMaker Triton Inference Server container as part of the Deep Learning Containers (DLCs), which is optimized for GPU acceleration and supports the existing Triton configuration without modification. This container integrates directly with SageMaker hosting endpoints, enabling seamless deployment of PyTorch models with Triton's features like dynamic batching and model concurrency.

Answer

Use SageMaker Neo to compile the model for Triton

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Package Triton as a custom container and use SageMaker batch transform

Answer

Use the standard SageMaker PyTorch container and install Triton at runtime

Question 13

A company deploys a real-time inference endpoint on SageMaker for a customer-facing application. Traffic patterns are unpredictable and sometimes spike. The endpoint must scale automatically to handle load while minimizing cost. Which approach should the company take?

Accepted Answer

Configure a target tracking scaling policy on the endpoint using Amazon CloudWatch metrics.. SageMaker endpoints support automatic scaling through target tracking scaling policies based on Amazon CloudWatch metrics like InvocationsPerInstance. This allows the endpoint to dynamically adjust the number of instances in response to real-time traffic spikes, scaling out when demand increases and scaling in when it decreases, which optimizes cost by only paying for the capacity needed at any given time.

Answer

Switch to batch transform for all inference requests.

Answer

Use a larger instance type to handle peak traffic.

Answer

Deploy multiple models behind an Application Load Balancer.

Question 14

A company uses SageMaker Pipelines to automate their ML workflow. They notice that the pipeline reruns all steps even when the input data has not changed. Which feature should they enable to avoid unnecessary recomputation?

Accepted Answer

Enable pipeline caching. Pipeline caching in SageMaker Pipelines automatically reuses the output of a step if its inputs (including parameters, data, and code) have not changed since the last successful execution. This avoids recomputation by comparing a hash of the step's dependencies against previous runs, making it the correct feature to prevent unnecessary reruns when input data remains identical.

Answer

Use a Lambda step to check input changes

Answer

Use a Conditional step to skip steps

Answer

Set the pipeline execution mode to 'Parallel'

Question 15

A team is using AWS Step Functions to orchestrate a machine learning workflow that includes data preprocessing, training, and model evaluation. The team wants to run the workflow whenever new data arrives in an S3 bucket. Which approach should they use to trigger the Step Functions workflow?

Accepted Answer

Configure the S3 bucket to send events to Amazon EventBridge, and create an EventBridge rule that targets the Step Functions state machine.. Amazon S3 can send event notifications directly to Amazon EventBridge, and EventBridge rules can target AWS Step Functions state machines as a target. This provides a fully managed, serverless integration that allows the Step Functions workflow to be triggered automatically whenever new data arrives in the S3 bucket, without needing intermediate polling or custom code.

Answer

Configure the S3 bucket to send an event notification directly to the Step Functions state machine.

Answer

Use S3 event notifications to send a message to an Amazon SQS queue, and have a Lambda function poll the queue to start the execution.

Answer

Use a CloudWatch Logs metric filter to trigger the Step Functions execution.