MLA-C01 Practice Test 2 — 10 Questions

Question 1

A team uses SageMaker Ground Truth to create labeled datasets. They need to ensure labeling jobs are cost-effective. Which TWO measures should they take? (Select TWO.)

Accepted Answer

Set up a labeling workflow with 'Incremental training'.. Option C is correct because SageMaker Ground Truth supports incremental training, which reuses labels and model artifacts from previous labeling jobs so that the auto-labeling model starts from an already-trained state, reducing the amount of human labeling and therefore cost on subsequent jobs. Option E is correct because Automated data labeling (active learning) uses a machine learning model to label a portion of the dataset automatically and only sends low-confidence items to human workers, which lowers the number of human annotations billed. Option A is not correct because instance type affects throughput and speed, not the fundamental per-label cost, and Ground Truth manages the labeling instances. Option B is not correct because workforce type (public, private, vendor) changes who labels and the price per label, but choosing a 'smaller' workforce type is not a defined cost-optimization measure and does not inherently reduce cost. Option D is not correct because Consolidated billing is an AWS Organizations billing feature for aggregating charges across accounts, not a Ground Truth labeling cost-reduction mechanism.

Answer

Use a smaller instance type for the labeling job.

Answer

Use a smaller workforce type.

Answer

Enable the 'Consolidated billing' for labeling costs.

Question 2

Which SageMaker built-in algorithm should be used for forecasting time series data with seasonal patterns?

Accepted Answer

DeepAR. DeepAR is a SageMaker built-in algorithm specifically designed for time series forecasting using recurrent neural networks (RNNs). It excels at handling complex seasonal patterns and can incorporate additional features, making it ideal for forecasting time series data with seasonality. It is the only built-in algorithm among the options that is purpose-built for time series forecasting.

Answer

IP Insights

Answer

BlazingText

Answer

Factorization Machines

Question 3

A machine learning engineer uses Amazon SageMaker Data Wrangler to preprocess a dataset. After applying a transform, the engineer wants to export the data to a feature group in Amazon SageMaker Feature Store for reuse in training and inference. Which export option should they choose?

Accepted Answer

Export to Amazon SageMaker Feature Store. SageMaker Data Wrangler can export directly to a feature group in Feature Store, making the features available for both training (offline) and inference (online).

Answer

Export to Amazon DynamoDB

Answer

Export to Amazon S3 as CSV

Answer

Export to Amazon SageMaker Pipelines

Question 4

A company is building a fraud detection model on credit card transactions. The dataset contains a column 'merchant_id' with 50,000 unique values, many with low frequency. The team wants to avoid overfitting while preserving predictive signal. Which feature engineering approach is most appropriate?

Accepted Answer

Apply target encoding with smoothing based on global mean. Target encoding with smoothing replaces each category with a blend of the category's mean target value and the global mean, weighted by frequency. This preserves predictive signal from high-frequency merchants while reducing overfitting for low-frequency ones by shrinking their encoded values toward the global mean. It is a standard technique for high-cardinality categorical features.

Answer

Drop the 'merchant_id' column to avoid overfitting

Answer

Apply label encoding and treat it as a numeric feature

Answer

One-hot encode the 'merchant_id' column

Question 5

Which SageMaker built-in algorithm is specifically designed for time series forecasting?

Accepted Answer

DeepAR. DeepAR is a SageMaker built-in algorithm based on recurrent neural networks (RNNs) specifically designed for time series forecasting. It learns from historical time series data and can predict future values, making it the correct choice for forecasting tasks. It supports both univariate and multivariate time series and can incorporate related time series.

Answer

Image Classification

Answer

BlazingText

Answer

XGBoost

Question 6

A healthcare company deploys a model to predict patient readmission risk. The model was trained on historical data and is now showing signs of concept drift. The team needs to implement a monitoring solution that can detect drift and automatically retrain the model when drift is detected. Which THREE steps should the team take to build this solution? (Choose THREE.)

Accepted Answer

Deploy SageMaker Model Monitor to track prediction quality over time. Option A is correct because SageMaker Model Monitor is the AWS service designed to continuously capture endpoint data and evaluate it against baselines, emitting CloudWatch metrics and alarms when data quality, model quality, bias, or drift violations occur, which is exactly what detecting concept drift requires. Option C is correct because Model Monitor's model quality monitoring depends on ground truth labels being joined to captured predictions; for readmission risk, those labels come from actual patient outcomes, so a labeling/feedback pipeline is essential to measure real drift. Option E is correct because the automated retraining requirement is met by triggering a SageMaker training job programmatically — for example, a Lambda function invoked by a CloudWatch alarm on the drift metric — which closes the detect-and-retrain loop. Option B is not appropriate because disabling the endpoint would halt predictions for clinicians and is unnecessary; retraining can occur while the existing endpoint continues serving, with a new model version deployed afterward. Option D is not appropriate because weekly manual comparison against a holdout set is neither automated nor a production drift-detection mechanism, and it would not satisfy the requirement to automatically retrain when drift is detected.

Answer

Disable the existing endpoint to prevent stale predictions during retraining

Answer

Manually compare the model's predictions against a holdout validation set each week

Question 7

A machine learning engineer needs to deploy a new version of a model gradually, initially sending 5% of traffic to the new version and 95% to the current version, while monitoring for errors. Which deployment pattern should they use?

Accepted Answer

Canary deployment. Canary deployment is the correct pattern because it allows the ML engineer to route a small percentage of traffic (e.g., 5%) to the new model version while keeping the majority (95%) on the current version. This enables gradual rollout with real-time monitoring for errors, and if issues are detected, traffic can be instantly shifted back to the stable version.

Answer

Blue/green deployment

Answer

Shadow testing

Answer

Rolling deployment

Question 8

A company wants to track the lineage of their ML models, including the training dataset, hyperparameters, and training job used to produce each model version. Which AWS service should they use?

Accepted Answer

SageMaker ML Lineage Tracking. SageMaker ML Lineage Tracking is the correct choice because it is purpose-built to record and query the provenance of ML models, capturing relationships between datasets, training jobs, hyperparameters, and model versions. It creates a directed acyclic graph (DAG) of entities (e.g., artifacts, actions, contexts) that allows you to trace how a specific model version was produced, which directly meets the requirement for lineage tracking.

Answer

Amazon DynamoDB

Answer

AWS Glue Data Catalog

Answer

Amazon S3 object tagging

Question 9

A data science team uses SageMaker Pipelines to orchestrate their ML workflow. They noticed that even when source data hasn't changed, the pipeline re-runs all steps, wasting compute time. What should they enable to avoid redundant runs?

Accepted Answer

Enable pipeline caching by setting the CacheConfig property for each step. SageMaker Pipelines supports step caching via the `CacheConfig` property. When enabled, the pipeline checks if the step's inputs (including source data, parameters, and code) have changed since the last successful run. If no changes are detected, the step is skipped and the previous output is reused, eliminating redundant compute.

Answer

Configure the pipeline to run on a schedule instead of on-demand

Answer

Use the Parameter step to pass previous execution ID

Answer

Use Lambda step to check data changes before running

Question 10

An organization needs to ensure that all data transmitted between containers in a SageMaker training job is encrypted. In the training job configuration, which setting should they enable?

Accepted Answer

Enable inter-container traffic encryption. SageMaker training jobs support inter-container traffic encryption, which ensures that data transmitted between containers (e.g., distributed training workers) is encrypted in transit. This setting uses TLS to protect the communication channel, meeting the organization's requirement for encrypted data transmission between containers.

Answer

Use a KMS key for data encryption

Answer

Configure the training job in VPC-only mode

Answer

Enable network isolation mode