MLA-C01 Practice Questions 1 — 50 Questions

Question 1

A team built a SageMaker Pipeline that includes a training step and a model evaluation step. They want to automatically register a model in SageMaker Model Registry only if the evaluation metric (accuracy) exceeds 0.9. Which pipeline step should be used to implement this conditional logic?

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

Accepted Answer

Condition step. The Condition step in SageMaker Pipelines allows you to add conditional branching logic, such as evaluating a metric and proceeding only if a condition is met. In this scenario, you would use a ConditionStep to check if the accuracy metric from the evaluation step exceeds 0.9, and then conditionally execute a RegisterModel step to register the model in SageMaker Model Registry. A common misconception is that the RegisterModel step itself can conditionally register a model based on metrics, but in SageMaker Pipelines, conditional logic must be implemented explicitly with a ConditionStep.

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RegisterModel step

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Processing step

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Transform step

Question 2

A company uses Amazon SageMaker to train and deploy a machine learning model. After deployment, they notice that the model's accuracy drops significantly over time due to changes in the underlying data distribution. Which monitoring solution should they implement to detect this issue automatically?

Accepted Answer

Set up Amazon SageMaker Model Monitor with data quality monitoring.. Amazon SageMaker Model Monitor with data quality monitoring is the correct solution because it automatically detects deviations in the input data distribution compared to a baseline, which directly addresses the problem of model accuracy degradation due to data drift. It continuously monitors the statistical properties of inference requests and alerts when drift is detected, enabling proactive retraining.

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Configure AWS Config rules to check the model accuracy metric.

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Use AWS CloudTrail to monitor changes to the model's S3 bucket.

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Enable Amazon CloudWatch Logs on the endpoint and set alarms on inference latency.

Question 3

An ML team is preparing time-series data for a demand forecasting model. They want to evaluate model performance over time without leaking future information into past training windows. Which data splitting strategy is MOST appropriate?

Accepted Answer

Walk-forward validation with an expanding window. Walk-forward validation with an expanding window is the most appropriate strategy because it respects the temporal order of the data, ensuring that each training window contains only past observations and each validation window contains only future observations. This prevents data leakage and provides a realistic evaluation of how the model will perform on unseen future time steps, which is critical for demand forecasting.

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Random k-fold cross-validation

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Single hold-out set with random selection

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Stratified sampling based on the target variable

Question 4

A company is training a deep learning model on Amazon SageMaker. The training job started but has been stuck in 'InProgress' state for an unusually long time with low CPU utilization. The data scientist suspects a bottleneck. What should be the first troubleshooting step?

Accepted Answer

Review CloudWatch Logs for the training container to identify errors or warnings.. When a SageMaker training job is stuck in 'InProgress' with low CPU utilization, the most common cause is a bottleneck in data loading or preprocessing within the training container. Reviewing CloudWatch Logs for the training container is the first troubleshooting step because it provides direct visibility into container-level errors, warnings, or stalls (e.g., hanging on a file read, waiting for a dependency, or a misconfigured data channel) that would not be visible from instance-level metrics alone.

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Switch the training job to use Spot instances to reduce cost and potentially improve throughput.

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Increase the number of training instances to parallelize data loading.

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Stop and restart the training job with a different instance type.

Question 5

A retail company has a SageMaker model that predicts customer churn. The model was trained on data that included a 'customer_zipcode' feature. After deployment, the data science team notices that the model's predictions for certain zip codes have become less accurate over time. They suspect that the relationship between zip code and churn has changed due to a recent relocation of a major employer. Which SageMaker monitoring capability should they use to detect this type of drift?

Accepted Answer

SageMaker Model Monitor model quality monitoring. Model quality monitoring is designed to monitor the performance of a model by comparing predictions to actual labels. When the relationship between a feature and the target changes, the model's accuracy drops, and model quality monitoring will detect this drift. Data quality monitoring only looks at input distributions, bias drift focuses on fairness, and feature attribution drift looks at feature importance, none of which directly measure predictive performance.

Answer

SageMaker Model Monitor bias drift monitoring

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SageMaker Model Monitor feature attribution drift monitoring

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SageMaker Model Monitor data quality monitoring

Question 6

A data scientist observes that a linear regression model has many irrelevant features. They want to perform feature selection to improve generalization. Which method combines feature selection with model training using a penalty that can shrink coefficients to zero?

Accepted Answer

Lasso regression. Lasso regression (L1 regularization) adds a penalty equal to the absolute value of the coefficients, which can shrink some coefficients exactly to zero — effectively performing feature selection during training. This makes it the method that combines feature selection with model fitting in a single step.

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Ridge regression

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Recursive Feature Elimination (RFE)

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Principal Component Analysis (PCA)

Question 7

A data engineer needs to prepare a large dataset for machine learning. The data is stored in an Amazon RDS MySQL database and needs to be transformed and moved to an S3 bucket in Parquet format for use with SageMaker. Which AWS service is most suitable for this extraction, transformation, and loading (ETL) task?

Accepted Answer

Use AWS Glue ETL jobs with PySpark to read from RDS, apply transformations, and write to S3 as Parquet.. AWS Glue ETL jobs with PySpark are the most suitable service for this task because Glue is a fully managed, serverless ETL service that can natively connect to Amazon RDS MySQL via JDBC, apply transformations using PySpark, and write the output directly to S3 in Parquet format. This aligns perfectly with the requirement to extract, transform, and load a large dataset into a machine-learning-ready format without managing infrastructure.

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Use Amazon Athena CTAS statements to copy data from RDS to S3.

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Use SageMaker Data Wrangler to connect to RDS and export transformed data to S3.

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Use Amazon EMR with Spark to read from RDS, transform, and write to S3.

Question 8

Refer to the exhibit. The data scientist wants to update the endpoint to use a new model version without downtime. Which approach should they use?

Accepted Answer

Create a new endpoint configuration with a second variant and update the endpoint. It uses SageMaker's blue/green deployment pattern: creating a new endpoint configuration with a second variant (the new model version) and updating the endpoint shifts traffic gradually or instantly without downtime. This approach leverages the endpoint's ability to host multiple variants and route traffic between them, ensuring zero interruption during the update.

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Delete the existing endpoint and create a new one

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Update the endpoint's model name directly

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Use SageMaker Model Monitor to automatically switch

Question 9

A company wants to use SageMaker Autopilot to automatically build a binary classification model. Which output does Autopilot provide to help understand model decisions?

Accepted Answer

An explainability report with feature importance. SageMaker Autopilot automatically generates an explainability report that includes feature importance, which helps users understand which features influenced the model's predictions. This report is produced for the best model and provides insights into model decisions, aligning with the requirement to understand model decisions. Autopilot's explainability report is a built-in feature that leverages SHAP values to compute feature attributions, but it is presented as a report, not just a raw plot.

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A leaderboard of models with only accuracy metrics

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A confusion matrix for each candidate model

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A SHAP values summary plot for each trial

Question 10

A company wants to deploy a foundation model from SageMaker JumpStart with the lowest possible inference cost, given that latency requirements are flexible. They have a mix of traffic volumes. Which approach should they take?

Accepted Answer

Select the smallest instance type that meets throughput requirements and enable automatic scaling. SageMaker JumpStart provides pre-built models; for cost optimization, choosing the smallest suitable instance type and enabling auto-scaling based on demand reduces cost while handling varying traffic.

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Use SageMaker Savings Plans to get a discount on on-demand instances

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Deploy the model on the largest GPU instance to handle peak load

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Deploy the model on a serverless inference endpoint

Question 11

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.

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Use a smaller workforce type.

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Enable the 'Consolidated billing' for labeling costs.

Question 12

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.

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IP Insights

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BlazingText

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Factorization Machines

Question 13

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).

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Export to Amazon DynamoDB

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Export to Amazon S3 as CSV

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Export to Amazon SageMaker Pipelines

Question 14

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.

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Drop the 'merchant_id' column to avoid overfitting

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Apply label encoding and treat it as a numeric feature

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One-hot encode the 'merchant_id' column

Question 15

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.

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Image Classification

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BlazingText

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XGBoost

Question 16

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

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

Question 17

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.

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Blue/green deployment

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Shadow testing

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Rolling deployment

Question 18

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.

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Amazon DynamoDB

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AWS Glue Data Catalog

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Amazon S3 object tagging

Question 19

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.

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

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Use the Parameter step to pass previous execution ID

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Use Lambda step to check data changes before running

Question 20

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.

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Use a KMS key for data encryption

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Configure the training job in VPC-only mode

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Enable network isolation mode