MLA-C01 Practice Test 3 — 30 Questions

Question 1

A team needs to deploy a new model version to production while minimizing risk. They want to route 5% of live traffic to the new model and 95% to the current model, and then gradually increase the new model's traffic. Which SageMaker deployment pattern should they use?

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

Accepted Answer

Canary deployment using production variants. Canary deployment using production variants in Amazon SageMaker allows you to route a small percentage of live traffic (e.g., 5%) to the new model version while the rest goes to the existing model. You can then gradually increase the traffic to the new model as confidence grows, minimizing risk. This pattern is specifically designed for progressive rollouts with the ability to roll back if issues arise.

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Shadow testing

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Blue/green deployment

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A/B testing with production variants

Question 2

A team uses SageMaker Pipelines for CI/CD. The training step fails due to insufficient memory. How to fix without rewriting code?

Accepted Answer

Increase the instance type in the pipeline step configuration. SageMaker Pipelines allows you to specify the instance type for each training step in the pipeline definition. By increasing the instance type (e.g., from ml.m5.large to ml.m5.xlarge or a memory-optimized instance like ml.r5.large), you allocate more memory to the training container without modifying the training script or algorithm. This directly resolves the out-of-memory error while preserving the existing code.

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Modify the training algorithm to use less memory

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Reduce the batch size in the training script

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Enable managed spot training

Question 3

A company ingests streaming transaction data from multiple sources using Amazon Kinesis Data Streams. The data must be transformed (e.g., JSON parsing, data type conversions) and then stored in Amazon S3 for ML training. The transformation logic may change over time. Which approach provides the greatest flexibility and ease of maintenance?

Accepted Answer

Use Kinesis Data Firehose with a Lambda function for transformation and delivery to S3.. Kinesis Data Firehose with a Lambda function for transformation gives you a fully managed delivery pipeline to S3 while letting you change transformation logic by updating the Lambda code, with no servers to manage. This satisfies both the flexibility (logic can evolve) and ease-of-maintenance requirements.

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Use a custom application running on Amazon EC2 with the Kinesis Client Library to transform and write to S3.

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Use Kinesis Data Analytics with SQL queries to transform data and output to S3.

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Use Kinesis Data Streams directly with a Lambda consumer to transform and store in S3.

Question 4

A machine learning team is building a fraud detection model. They have a dataset with a categorical feature 'merchant_id' that has over 10,000 unique values. Which feature engineering technique should they apply to 'merchant_id' to reduce dimensionality while retaining predictive power?

Accepted Answer

Target encoding. Target encoding replaces each merchant_id category with a statistic derived from the target variable (e.g., mean fraud rate per merchant), collapsing 10,000+ categories into a single numeric column while preserving the category's relationship to the label. This dramatically reduces dimensionality compared to one-hot encoding, which would create over 10,000 sparse columns. Because the encoded value carries predictive signal about fraud, target encoding retains useful information that label or ordinal encoding would lose.

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One-hot encoding

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Ordinal encoding

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Label encoding

Question 5

A data scientist needs to split a time-series dataset into training and testing sets while avoiding data leakage from future values. Which splitting technique should the data scientist use?

Accepted Answer

Time-series split (walk-forward validation). Time-series split (walk-forward validation) is the correct technique because it preserves the temporal order of observations, ensuring that training data always precedes test data. This prevents data leakage from future values, which is critical for time-series forecasting. Random shuffling or standard cross-validation would mix past and future data, leading to overly optimistic performance estimates.

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Random shuffle followed by a 80/20 split

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K-fold cross-validation with shuffled folds

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Stratified sampling based on the target variable

Question 6

A company uses SageMaker JumpStart to deploy a foundation model for a summarization task. They want to minimize costs while still meeting a latency requirement of under 2 seconds. Which option should they consider?

Accepted Answer

Use SageMaker Inference Recommender to select the cheapest instance that meets latency. SageMaker Inference Recommender runs load tests against your model on various instance types and provides latency and cost metrics. By selecting the cheapest instance that still meets the sub-2-second latency requirement, you directly minimize cost while satisfying the performance constraint. This is the most systematic and cost-effective approach for this scenario.

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Deploy the model on a serverless endpoint

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Enable auto-scaling to handle variable traffic

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Use the largest GPU instance to ensure fast inference

Question 7

A financial services company ingests transaction data from multiple sources into an S3 data lake. They want to use AWS Glue to catalog this data and make it queryable by Amazon Athena. The data schema changes frequently as new sources are added. Which AWS Glue feature should they enable to automatically detect and update the schema?

Accepted Answer

AWS Glue crawlers with schema update policy set to 'UPDATE'. AWS Glue crawlers scan data in S3, infer the schema, and write table definitions to the Glue Data Catalog. By setting the crawler's schema update policy to 'UPDATE', the crawler will modify existing table definitions in the catalog when it detects new columns or changed types, keeping Athena queries working as sources evolve. This is the purpose-built feature for automatically detecting and propagating schema changes.

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AWS Glue DataBrew

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AWS Glue ETL job scheduled to run daily

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Manual schema definition in the AWS Glue Data Catalog

Question 8

A company uses Amazon SageMaker Feature Store to store features for a real-time recommendation model. The feature data is updated continuously, and the model must use the most recent feature values for each user at inference time. Which type of Feature Store should the company use for serving features to the model?

Accepted Answer

Online store. The SageMaker Feature Store online store is optimized for low-latency, real-time reads of the latest feature values, which is exactly what a real-time recommendation model needs at inference time. It stores the most recent record for each entity (e.g., user) in a low-latency store backed by a fast database. Offline store is for batch/historical access, not real-time serving.

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Offline store

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Point-in-time queries

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Both online and offline store

Question 9

A team monitors a production endpoint and notices a sudden increase in 5XXError count. Which of the following is the most likely cause?

Accepted Answer

The model container is out of memory or crashing. A sudden increase in 5XX errors, particularly HTTP 503 or 502, typically indicates that the model container is failing to process requests due to resource exhaustion (e.g., OOM kills) or a crash in the inference process. In a production ML endpoint, such errors often stem from the container running out of memory, leading to the container being terminated by the orchestrator (e.g., Kubernetes OOMKill) or the application crashing internally, which directly causes 5XX responses.

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The endpoint is under-provisioned and requests are throttled

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The input data format has changed

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The model is returning predictions with high latency

Question 10

A data scientist is preparing a dataset for binary classification. The dataset has a target variable with 90% of samples belonging to class 0 and 10% to class 1. Which data splitting strategy should the scientist use to ensure that the training and test sets maintain the same class proportion as the original dataset?

Accepted Answer

Stratified sampling. Stratified sampling preserves the class proportion of the original dataset in each split, which is essential for imbalanced binary classification (90/10). By sampling within each class stratum, the training and test sets both retain roughly 90% class 0 and 10% class 1, preventing a split where the minority class is underrepresented or absent.

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Time-series split

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Simple random split

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k-fold cross-validation

Question 11

A machine learning engineer deploys a new model version to a SageMaker endpoint with production variants. They want to gradually shift traffic from the old model to the new model, monitoring for errors, and automatically roll back if the error rate exceeds 5%. Which deployment pattern should they use?

Accepted Answer

Canary deployment with CloudWatch alarms. Canary deployment with CloudWatch alarms is correct because SageMaker production variants allow you to split traffic between the old and new model (e.g., 90/10), and CloudWatch alarms can monitor the new variant's error rate and trigger automatic rollback when it exceeds 5%. This pattern is purpose-built for gradual, monitored traffic shifting with automated safety nets.

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A/B testing with traffic splitting

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Blue/green deployment

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Shadow testing

Question 12

A machine learning team is developing a deep learning model for image classification. They observe that the training loss decreases rapidly but the validation loss starts increasing after a few epochs. Which strategy should they implement to address this issue?

Accepted Answer

Apply dropout regularization. The scenario describes overfitting, where the model memorizes training data but fails to generalize to validation data. Dropout regularization randomly deactivates a fraction of neurons during training, which prevents co-adaptation and forces the network to learn more robust features, thereby reducing the validation loss increase.

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Increase the batch size

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Add more convolutional layers

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Increase the learning rate

Question 13

An ML engineer is preparing a time-series dataset for a forecasting model that predicts daily sales for the next 30 days. The dataset contains 3 years of daily sales data. Which data splitting strategy should the engineer use to evaluate the model's performance on future data?

Accepted Answer

Walk-forward validation (time-series split). Walk-forward validation (time-series split) preserves the temporal order of observations by training on past data and testing on subsequent future periods, which mirrors how a forecasting model is actually used in production. Because daily sales data has trend, seasonality, and autocorrelation, random or stratified splits leak future information into training and produce optimistically biased metrics. Walk-forward validation with a 30-day horizon directly simulates predicting the next 30 days from prior history.

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Leave-one-out cross-validation

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Random 80/20 train-test split

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Stratified k-fold cross-validation

Question 14

Refer to the exhibit. A data scientist tries to deploy a model from an S3 bucket encrypted with SSE-KMS. What should the administrator do to resolve this?

Accepted Answer

Add kms:Decrypt permission to the SageMaker execution role for the KMS key.. When a model artifact is stored in an S3 bucket encrypted with SSE-KMS, the SageMaker execution role must have the kms:Decrypt permission for the specific KMS key to allow SageMaker to decrypt the artifact during model deployment. Without this permission, the deployment fails because SageMaker cannot read the encrypted object. Option B correctly adds the required KMS permission to the execution role.

Answer

Change the model artifact encryption to SSE-S3.

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Re-upload the model artifact without encryption.

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Attach the AWS managed policy 'AmazonSageMakerFullAccess' to the role.

Question 15

A company needs to deploy a model that processes large payloads (up to 1 GB) asynchronously. The results should be written to S3, and the team needs SNS notifications upon completion. Which SageMaker inference option is MOST suitable?

Accepted Answer

Asynchronous Inference. SageMaker Asynchronous Inference is purpose-built for large payloads (up to 1 GB) and long processing times (up to 15 minutes), queuing requests and writing results to S3. It natively supports SNS notifications on completion or failure, matching the requirement exactly. This makes it the correct fit for asynchronous, large-payload processing with S3 output and SNS alerts.

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Batch Transform

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Real-time endpoint

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Serverless Inference

Question 16

A company is using AWS Step Functions to orchestrate their ML retraining pipeline. They want to trigger retraining when new data arrives, but only if the model's performance has degraded below a threshold. Which THREE AWS services should they use together to achieve this? (Choose three.)

Accepted Answer

AWS Step Functions. Amazon EventBridge (C) is the correct event-routing service to detect the arrival of new data (e.g., an S3 PutObject event) and trigger the pipeline, since it can match events and invoke targets such as Step Functions. AWS Step Functions (A) is correct because it orchestrates the multi-step ML retraining workflow, including the conditional logic that checks whether model performance has degraded below the threshold before proceeding. AWS Lambda (B) is correct because it provides the serverless compute to evaluate the performance metric against the threshold and return a decision that Step Functions uses in its Choice state to branch into retraining or stop. Amazon CloudWatch Logs (D) is not correct because it is a logging/monitoring service, not an event trigger or orchestration component for this pipeline. SageMaker Model Registry (E) is not correct because, while it tracks model versions and approval status, it does not itself trigger retraining based on incoming data or performance degradation.

Answer

Amazon CloudWatch Logs

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SageMaker Model Registry

Question 17

A data science team wants to track the lineage of models, including datasets, training jobs, and endpoints, for reproducibility and audit. They need a solution that captures relationships between artifacts automatically during training and deployment. Which service should they use?

Accepted Answer

SageMaker ML Lineage Tracking. SageMaker ML Lineage Tracking automatically captures relationships among datasets, training jobs, model artifacts, and endpoints as the pipeline runs, producing a queryable lineage graph for reproducibility and audit. It records entities and associations without custom instrumentation, which is exactly what the team needs. SageMaker Experiments tracks runs and metrics but does not build the full artifact relationship graph.

Answer

Amazon S3 object versioning

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SageMaker Experiments

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SageMaker Model Registry

Question 18

A team has deployed a real-time inference endpoint and wants to automatically scale based on CPU utilization. Which scaling policy type should they use with Application Auto Scaling for SageMaker endpoints?

Accepted Answer

Target tracking scaling. Target tracking scaling is correct because it adjusts capacity to keep a specified metric, such as CPU utilization, at a target value. Application Auto Scaling for SageMaker endpoints supports target tracking, which automatically creates and manages the necessary CloudWatch alarms and scaling policies. This is the recommended approach for maintaining a utilization target without manually defining step adjustments.

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Step scaling

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Predictive scaling

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Simple scaling

Question 19

An ML engineer wants to be notified when the average inference latency of a SageMaker endpoint exceeds 500 ms for 2 consecutive evaluation periods. Which AWS service combination should they use?

Accepted Answer

CloudWatch Alarm + SNS. CloudWatch Alarms evaluate metrics over specified periods and can trigger SNS notifications when a threshold is breached for a defined number of consecutive periods. SageMaker endpoints automatically publish inference latency metrics to CloudWatch, so an alarm on the latency metric with a 500 ms threshold and 2 evaluation periods, wired to an SNS topic, delivers exactly the required notification.

Answer

SageMaker Model Monitor + SNS

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EventBridge + Lambda

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SageMaker Clarify + SNS

Question 20

A company uses Amazon SageMaker Data Wrangler for data preparation. The data science team wants to automatically detect potential bias in their dataset before training a model. Which feature of Data Wrangler should they use?

Accepted Answer

Built-in bias detection with SageMaker Clarify. Amazon SageMaker Data Wrangler includes built-in bias detection powered by SageMaker Clarify. This feature allows data scientists to analyze datasets for potential bias before model training, directly within the Data Wrangler visual interface. Option D is correct because it is the only option that provides automated bias detection at the data preparation stage.

Answer

Feature importance analysis

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Amazon Rekognition

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Model Monitor