MLA-C01 Practice Test 33 — 15 Questions

Question 1

A machine learning engineer is building a time-series forecasting model to predict daily sales for the next 30 days. The dataset spans two years of daily sales data. To evaluate model performance, the engineer needs to simulate a realistic forecasting scenario where the model is trained on past data and tested on future data without leakage. Which data splitting strategy should they use?

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

Accepted Answer

Walk-forward validation with an expanding window. Walk-forward validation with an expanding window respects the temporal order of time-series data by training on all data up to time t and testing on the next period, then expanding the training set forward. This simulates the real forecasting scenario where the model only ever sees past data when predicting the future, and it prevents leakage that random splits would introduce. For a 30-day forecast horizon on two years of daily data, this is the standard approach.

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Hold-out validation using a random 80/20 split

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Stratified sampling based on sales volume

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k-fold cross-validation with random shuffling

Question 2

A machine learning engineer is using AWS Glue ETL to transform a large dataset stored in Amazon S3. The transformation involves joining two tables on a high-cardinality column and aggregating results. The job is running slowly and the engineer needs to improve performance. Which optimization technique should the engineer apply?

Accepted Answer

Use bucketing on the join key for both tables. Bucketing on the join key for both tables co-locates rows with the same key into the same bucket files, which allows Spark to perform a bucketed join that avoids a full shuffle of the large dataset. This dramatically reduces network I/O and speeds up joins on high-cardinality columns.

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Increase the number of workers and memory allocation

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Use Spark SQL instead of the DynamicFrame API

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Convert the job to use Python Shell

Question 3

A data engineer is designing an ETL pipeline using AWS Glue to transform raw data from S3 into a curated set for ML training. The data contains personally identifiable information (PII) that must be masked before being used by data scientists. Which TWO actions should the engineer take? (Choose TWO.)

Accepted Answer

Use AWS Glue DataBrew to define PII masking transformations. Option A is correct because AWS Glue DataBrew provides purpose-built, no-code visual transformations including PII masking recipes (e.g., replacing, hashing, or redacting sensitive columns) that can be applied within the Glue/DataBrew pipeline before data reaches data scientists. Option D is correct because AWS Glue ETL jobs run on Apache Spark, so the engineer can write PySpark code using functions like sha2(), regexp_replace(), or custom UDFs to deterministically mask PII fields during the transform stage. Option B is not appropriate because Kinesis Data Firehose is a streaming ingestion/delivery service, not the batch ETL transformation layer described here, and it does not provide PII masking logic. Option C is incorrect because the Glue Data Catalog is a metadata repository storing table and schema definitions; it does not perform data masking on field values. Option E is incorrect because a Glue Crawler only infers schemas and populates the Data Catalog—it detects formats and structures, not PII, and cannot mask data.

Answer

Use Amazon Kinesis Data Firehose to transform data at ingestion

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Use AWS Glue Data Catalog to automatically mask PII fields

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Use AWS Glue Crawler to detect and mask PII automatically

Question 4

A data scientist is using Amazon SageMaker Ground Truth to create a labeled dataset for an object detection model. The dataset contains 1 million images, and the team wants to reduce labeling cost by labeling only the most informative samples. Which feature of Ground Truth should they use?

Accepted Answer

Active learning. Ground Truth active learning automatically selects the most informative unlabeled samples for human labeling by using a model to score uncertainty, so the team labels far fewer than 1 million images while achieving similar model accuracy. It's designed exactly for cost reduction on large datasets.

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Automated data labeling

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Pre-built annotation worker UI

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Consolidated labeling

Question 5

A company uses SageMaker endpoints for real-time inference. They want to automatically scale the number of instances based on the number of outstanding requests. Which auto-scaling policy type should they choose?

Accepted Answer

Target tracking scaling. Target tracking scaling is the correct choice because it lets you specify a target value for a metric — such as a custom metric for outstanding requests per instance — and SageMaker automatically adjusts instance count to keep that metric at the target. This is the recommended policy for metrics that correlate directly with capacity needs, like request backlog or invocations per instance. It handles both scale-out and scale-in automatically without manual threshold tuning.

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Scheduled scaling

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Step scaling

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Simple scaling

Question 6

A company is using SageMaker to train a neural network for image classification. The training job is taking too long. The team wants to reduce training time without sacrificing model accuracy. Which approach should they recommend?

Accepted Answer

Use a GPU-based instance such as ml.p3.2xlarge. GPU-based instances like ml.p3.2xlarge are specifically designed for parallel processing of matrix operations, which are fundamental to neural network training. By offloading compute-intensive tensor operations to GPU cores, training time can be significantly reduced without altering the model architecture or data, thus preserving accuracy.

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Increase the batch size to the maximum possible

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Use a learning rate scheduler that reduces the learning rate over time

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Add more convolutional layers to the model

Question 7

A company uses SageMaker to deploy a model and wants to perform A/B testing by splitting traffic between two model variants. Which TWO actions should they take? (Select TWO.)

Accepted Answer

Configure two production variants on the endpoint, each with an initial weight. Option A is correct because SageMaker A/B testing is implemented by configuring a single endpoint with two production variants, each assigned an InitialVariantWeight so the endpoint can split inference traffic between them. Option C is correct because after analyzing the variants' performance, you can shift traffic by calling UpdateEndpointWeightsAndCapacity to change each variant's VariantWeight and DesiredInstanceCount without redeploying the endpoint. Option B is not required: Model Registry approval is a governance step and does not itself create or split endpoint traffic. Option D is wrong because deploying separate endpoints with Route53 weighted routing is a DNS-level workaround, not SageMaker's native A/B testing mechanism. Option E is wrong because shadow testing mirrors live traffic to a new variant for evaluation without serving its responses, so it does not split production traffic between two variants.

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Use SageMaker Model Registry to approve both variants

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Deploy each variant to a separate endpoint and use Route53 weighted routing

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Enable shadow testing on the endpoint

Question 8

A machine learning engineer needs to split a time-series dataset for a forecasting model. The data spans 3 years of daily sales. Which splitting strategy should they use to avoid look-ahead bias?

Accepted Answer

Walk-forward validation (time-series split). Walk-forward validation (time-series split) is the correct strategy because it preserves the temporal order of the data, training on past observations and testing on future observations sequentially. This avoids look-ahead bias, where future information leaks into the training set, which would invalidate the forecasting model's performance metrics.

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k-fold cross-validation with shuffling

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Random train-test split with 80/20 ratio

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Stratified sampling based on sales volume

Question 9

A model deployed on SageMaker is returning inaccurate predictions for certain customer segments. The team suspects data drift. Which SageMaker feature should they use to continuously monitor input data distribution?

Accepted Answer

SageMaker Model Monitor. SageMaker Model Monitor is the correct choice because it is specifically designed to continuously monitor the input data distribution of a deployed model and detect data drift over time. It automatically captures and analyzes the statistical properties of incoming inference requests against a baseline, alerting you when significant deviations occur.

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SageMaker Clarify

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SageMaker Debugger

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SageMaker Feature Store

Question 10

A company is using SageMaker Pipelines to orchestrate their ML workflow. They have a Condition step that checks if a model's accuracy exceeds 0.9. If true, they want to register the model in the model registry; otherwise, they want to run a retraining step. Which step type should they use for the decision?

Accepted Answer

Condition step. A Condition step in SageMaker Pipelines evaluates a boolean expression against a property (such as a model accuracy metric from a preceding Evaluation step) and branches the pipeline into a true or false path. This is exactly the construct needed to register the model when accuracy > 0.9 and otherwise trigger retraining.

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Transform step

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Processing step

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Tuning step

Question 11

A data scientist creates a feature group as shown in the exhibit. When ingesting data with an 'age' column of integer values, the ingestion fails. What is the most likely cause?

Accepted Answer

The `age` feature type should be `Integral`, not `String`.. The feature group definition specifies the 'age' column as a `String` type, but the ingested data contains integer values. Amazon SageMaker Feature Store requires that the data types of ingested records match the schema defined in the feature group. When a mismatch occurs, such as providing an integer for a string field, the ingestion fails with a type conversion error.

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The role does not have permissions to write to the feature store.

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The `OnlineStoreConfig` must include a `SecurityConfig`.

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The `EventTimeFeatureName` is incorrectly spelled.

Question 12

A team uses MLflow on SageMaker for experiment tracking. They want to automate the retraining of a model when new training data arrives in an S3 bucket. Which combination of services should they use?

Accepted Answer

EventBridge -> Lambda -> SageMaker Training Job. EventBridge can capture S3 events (via CloudTrail or S3 event notifications routed to EventBridge) and trigger a Lambda function that starts a SageMaker Training Job. This event-driven pattern is the standard AWS-native way to automate retraining when new data lands in S3. It avoids polling and integrates cleanly with MLflow tracking on SageMaker.

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SageMaker Pipelines scheduled trigger every hour

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S3 Event Notifications -> SQS -> SageMaker Training Job

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AWS Step Functions with S3 poller

Question 13

A company uses SageMaker Studio for collaborative ML development. The security team requires that all SageMaker Studio notebooks run within a VPC and cannot access the public internet. Which configuration should the administrator set?

Accepted Answer

Enable VPC-only mode for the SageMaker Studio domain. Enabling VPC-only mode for the SageMaker Studio domain ensures that all Studio notebooks and apps are launched within the specified VPC and cannot access the public internet. This mode enforces that all network traffic, including internet-bound traffic, is routed through the VPC, and it blocks direct internet access by default, meeting the security team's requirement.

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Use SageMaker notebook instances instead of Studio

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Apply an SCP that denies internet access for all IAM users

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Set the SageMaker Studio domain to use a public subnet with a NAT Gateway

Question 14

A company collects sensor data from IoT devices. The data arrives with missing timestamps due to network issues. For anomaly detection, the engineer needs to create features that capture rolling statistics over fixed windows. Which data preprocessing step is essential before feature generation?

Accepted Answer

Resample data to a fixed frequency. Resampling the data to a fixed frequency is essential because rolling window statistics require a consistent time index to compute accurate aggregations over fixed windows. Without a uniform timestamp grid, the window boundaries become ambiguous and the resulting features will be misaligned or incomplete, undermining the anomaly detection model.

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Remove missing timestamps

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Sort data by device ID

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Impute missing values with forward fill

Question 15

A team has a SageMaker Pipeline that trains a model and registers it in the Model Registry. They want to automate the deployment of the approved model to a staging environment. Which event-driven approach should they use?

Accepted Answer

Use Amazon EventBridge to listen for Model Registry approval events and trigger an AWS Lambda function that deploys the model. Amazon EventBridge natively integrates with SageMaker Model Registry and emits events such as 'SageMaker Model Package State Change' when a model package's approval status changes to Approved. An EventBridge rule can match that event pattern and invoke a Lambda function (or Step Functions, CodePipeline, etc.) to deploy the model to staging. This is the canonical event-driven pattern for automating post-approval deployment in SageMaker MLOps workflows.

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Use an SQS queue to store approval messages and have a cron job process them

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Set up a CloudWatch alarm on the Model Registry's ApprovalStatus metric

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Configure an AWS Step Functions state machine to poll the Model Registry every minute