Free AWS MLA-C01 practice questions

10 free AWS MLA-C01 practice questions with the correct answer and a full explanation for each, taken from the CertStash pack of 271 questions. Work through them, then open each answer to check your reasoning.

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Question 1

CASE STUDY

A company is building a web-based AI application by using Amazon SageMaker. The application will provide the following capabilities and features: ML experimentation, training, a central model registry, model deployment, and model monitoring.

The application must ensure secure and isolated use of training data during the ML lifecycle. The training data is stored in Amazon S3.

The company needs to use the central model registry to manage different versions of models in the application.

Which action will meet this requirement with the LEAST operational overhead?

  1. Create a separate Amazon Elastic Container Registry (Amazon ECR) repository for each model.
  2. Use Amazon Elastic Container Registry (Amazon ECR) and unique tags for each model version.
  3. Use the SageMaker Model Registry and model groups to catalog the models.
  4. Use the SageMaker Model Registry and unique tags for each model version.
Show answer and explanation

Correct answer: C. Use the SageMaker Model Registry and model groups to catalog the models.

SageMaker Model Registry with model groups is the purpose-built AWS service for managing model versions and metadata with minimal operational overhead. It provides native integration with SageMaker's ML lifecycle, automatic versioning, and approval workflows. ECR repositories are container registries not designed for model metadata management, and tagging alone (options B and D) lacks the governance and cataloging features of the Model Registry.

Why the other options are wrong

  • A. ECR repositories are for container images, not optimized for ML model versioning and governance.
  • B. ECR with tags requires manual management and lacks SageMaker's model-specific features like approval status and lineage tracking.
  • D. Generic tagging in Model Registry lacks the organizational structure that model groups provide for managing related model versions.

Question 2

CASE STUDY

A company is building a web-based AI application by using Amazon SageMaker. The application will provide the following capabilities and features: ML experimentation, training, a central model registry, model deployment, and model monitoring.

The application must ensure secure and isolated use of training data during the ML lifecycle. The training data is stored in Amazon S3.

The company is experimenting with consecutive training jobs.

How can the company MINIMIZE infrastructure startup times for these jobs?

  1. Use Managed Spot Training.
  2. Use SageMaker managed warm pools.
  3. Use SageMaker Training Compiler.
  4. Use the SageMaker distributed data parallelism (SMDDP) library.
Show answer and explanation

Correct answer: B. Use SageMaker managed warm pools.

SageMaker managed warm pools maintain pre-initialized compute resources between training jobs, eliminating the startup overhead of provisioning and initializing instances. This directly addresses minimizing infrastructure startup times for consecutive jobs. Managed Spot Training reduces costs but not startup time. Training Compiler optimizes code execution speed, not infrastructure provisioning. SMDDP is for distributed training parallelism, not startup optimization.

Why the other options are wrong

  • A. Managed Spot Training reduces costs through interruption-prone instances but does not minimize startup times.
  • C. Training Compiler optimizes training job execution speed, not the infrastructure provisioning startup phase.
  • D. Distributed data parallelism is for scaling training across multiple instances, not reducing startup times.

Question 3

CASE STUDY

A company is building a web-based AI application by using Amazon SageMaker. The application will provide the following capabilities and features: ML experimentation, training, a central model registry, model deployment, and model monitoring.

The application must ensure secure and isolated use of training data during the ML lifecycle. The training data is stored in Amazon S3.

The company must implement a manual approval-based workflow to ensure that only approved models can be deployed to production endpoints.

Which solution will meet this requirement?

  1. Use SageMaker Experiments to facilitate the approval process during model registration.
  2. Use SageMaker ML Lineage Tracking on the central model registry. Create tracking entities for the approval process.
  3. Use SageMaker Model Monitor to evaluate the performance of the model and to manage the approval.
  4. Use SageMaker Pipelines. When a model version is registered, use the AWS SDK to change the approval status to "Approved."
Show answer and explanation

Correct answer: D. Use SageMaker Pipelines. When a model version is registered, use the AWS SDK to change the approval status to "Approved."

A model version registered in the SageMaker Model Registry starts with a PendingManualApproval status, so it cannot be deployed until a reviewer acts. With SageMaker Pipelines, the workflow registers the version, and the reviewer calls the AWS SDK UpdateModelPackage API to set the status to Approved. Deployment steps are gated on that status, so only models a person has approved reach production endpoints. Experiments, ML Lineage Tracking, and Model Monitor do not control this status.

Why the other options are wrong

  • A. SageMaker Experiments organizes and compares training runs; it does not gate model deployment.
  • B. ML Lineage Tracking records model relationships and data provenance; it does not gate deployment on approval.
  • C. Model Monitor detects data drift and quality issues on endpoints; it does not manage registry approval status.

Question 4

CASE STUDY

A company is building a web-based AI application by using Amazon SageMaker. The application will provide the following capabilities and features: ML experimentation, training, a central model registry, model deployment, and model monitoring.

The application must ensure secure and isolated use of training data during the ML lifecycle. The training data is stored in Amazon S3.

The company needs to run an on-demand workflow to monitor bias drift for models that are deployed to real-time endpoints from the application.

Which action will meet this requirement?

  1. Configure the application to invoke an AWS Lambda function that runs a SageMaker Clarify job.
  2. Invoke an AWS Lambda function to pull the sagemaker-model-monitor-analyzer built-in SageMaker image.
  3. Use AWS Glue Data Quality to monitor bias.
  4. Use SageMaker notebooks to compare the bias.
Show answer and explanation

Correct answer: A. Configure the application to invoke an AWS Lambda function that runs a SageMaker Clarify job.

SageMaker Clarify is the dedicated service for bias detection and monitoring, and invoking it through AWS Lambda enables on-demand workflow execution for monitoring bias drift in deployed endpoints. This provides the required monitoring capability without continuous execution overhead. Pulling the monitor-analyzer image manually (option B) bypasses SageMaker's integrated monitoring. AWS Glue Data Quality monitors data quality but not model bias. Notebooks require manual execution and don't provide on-demand automation.

Why the other options are wrong

  • B. Manually pulling the monitor-analyzer image requires custom scripting and lacks the integration that Clarify provides.
  • C. AWS Glue Data Quality monitors data quality issues, not model-specific bias drift in predictions.
  • D. SageMaker notebooks require manual execution and comparison, not automated o-emand workflows.

Question 5

CASE STUDY

An ML engineer is developing a fraud detection model on AWS. The training dataset includes transaction logs, customer profiles, and tables from an on-premises MySQL database. The transaction logs and customer profiles are stored in Amazon S3.

The dataset has a class imbalance that affects the learning of the model's algorithm. Additionally, many of the features have interdependencies.

The algorithm is not capturing all the desired underlying patterns in the data.

Which AWS service or feature can aggregate the data from the various data sources?

  1. Amazon EMR Spark jobs
  2. Amazon Kinesis Data Streams
  3. Amazon DynamoDB
  4. AWS Lake Formation
Show answer and explanation

Correct answer: D. AWS Lake Formation

AWS Lake Formation is built to bring data from many sources into one governed data lake on Amazon S3. Its blueprints use AWS Glue JDBC connections to ingest tables from databases such as the on-premises MySQL instance, catalog them alongside the existing S3 transaction logs and customer profiles, and apply central permissions. That gives the ML engineer one cataloged, governed dataset to train on without building ingestion pipelines by hand.

Why the other options are wrong

  • A. EMR Spark can read S3 and JDBC sources, but it requires cluster management and custom code rather than managed, governed aggregation.
  • B. Kinesis Data Streams ingests real-time event streams; it does not aggregate existing batch tables and S3 files.
  • C. DynamoDB is a NoSQL key-value database used to store application data, not a service that aggregates and governs multiple sources.

Question 6

CASE STUDY

An ML engineer is developing a fraud detection model on AWS. The training dataset includes transaction logs, customer profiles, and tables from an on-premises MySQL database. The transaction logs and customer profiles are stored in Amazon S3.

The dataset has a class imbalance that affects the learning of the model's algorithm. Additionally, many of the features have interdependencies.

The algorithm is not capturing all the desired underlying patterns in the data.

After the data is aggregated, the ML engineer must implement a solution to automatically detect anomalies in the data and to visualize the result.

Which solution will meet these requirements?

  1. Use Amazon Athena to automatically detect the anomalies and to visualize the result.
  2. Use Amazon Redshift Spectrum to automatically detect the anomalies. Use Amazon QuickSight to visualize the result.
  3. Use Amazon SageMaker Data Wrangler to automatically detect the anomalies and to visualize the result.
  4. Use AWS Batch to automatically detect the anomalies. Use Amazon QuickSight to visualize the result.
Show answer and explanation

Correct answer: C. Use Amazon SageMaker Data Wrangler to automatically detect the anomalies and to visualize the result.

SageMaker Data Wrangler includes built-in data quality and anomaly detection capabilities with integrated visualization features, enabling both anomaly detection and result visualization in a single tool. This meets both requirements with minimal additional components. Athena is a query service without anomaly detection. Redshift Spectrum querying data doesn't automatically detect anomalies; QuickSight visualization requires separate anomaly logic. AWS Batch requires custom anomaly detection code.

Why the other options are wrong

  • A. Amazon Athena is a query engine without built-in anomaly detection capabilities.
  • B. Redshift Spectrum requires custom anomaly detection logic; it doesn't automatically detect anomalies without additional implementation.
  • D. AWS Batch requires custom container code to detect anomalies and doesn't provide automatic anomaly detection.

Question 7

CASE STUDY

An ML engineer is developing a fraud detection model on AWS. The training dataset includes transaction logs, customer profiles, and tables from an on-premises MySQL database. The transaction logs and customer profiles are stored in Amazon S3.

The dataset has a class imbalance that affects the learning of the model's algorithm. Additionally, many of the features have interdependencies.

The algorithm is not capturing all the desired underlying patterns in the data.

The training dataset includes categorical data and numerical data. The ML engineer must prepare the training dataset to maximize the accuracy of the model.

Which action will meet this requirement with the LEAST operational overhead?

  1. Use AWS Glue to transform the categorical data into numerical data.
  2. Use AWS Glue to transform the numerical data into categorical data.
  3. Use Amazon SageMaker Data Wrangler to transform the categorical data into numerical data.
  4. Use Amazon SageMaker Data Wrangler to transform the numerical data into categorical data.
Show answer and explanation

Correct answer: C. Use Amazon SageMaker Data Wrangler to transform the categorical data into numerical data.

SageMaker Data Wrangler provides visual, low-code data transformation with built-in recipes for encoding categorical variables into numerical representations. It requires minimal coding effort and provides immediate preview of transformations. AWS Glue requires writing custom transformation code (Spark/Python), increasing operational overhead. Data Wrangler's visual interface and pre-built transformations make it the least operationally intensive option.

Why the other options are wrong

  • A. AWS Glue requires custom ETL code or mapping logic, which increases operational complexity compared to Data Wrangler's visual interface.
  • B. Converting numerical to categorical data is the inverse of what's needed for a classification model; it reduces rather than maximizes accuracy.
  • D. Converting numerical to categorical data reverses the needed transformation and would degrade model performance.

Question 8

CASE STUDY

An ML engineer is developing a fraud detection model on AWS. The training dataset includes transaction logs, customer profiles, and tables from an on-premises MySQL database. The transaction logs and customer profiles are stored in Amazon S3.

The dataset has a class imbalance that affects the learning of the model's algorithm. Additionally, many of the features have interdependencies.

The algorithm is not capturing all the desired underlying patterns in the data.

Before the ML engineer trains the model, the ML engineer must resolve the issue of the imbalanced data.

Which solution will meet this requirement with the LEAST operational effort?

  1. Use Amazon Athena to identify patterns that contribute to the imbalance. Adjust the dataset accordingly.
  2. Use Amazon SageMaker Studio Classic built-in algorithms to process the imbalanced dataset.
  3. Use AWS Glue DataBrew built-in features to oversample the minority class.
  4. Use the Amazon SageMaker Data Wrangler balance data operation to oversample the minority class.
Show answer and explanation

Correct answer: D. Use the Amazon SageMaker Data Wrangler balance data operation to oversample the minority class.

SageMaker Data Wrangler includes a built-in balance data operation specifically designed to handle class imbalance through oversampling techniques. This requires no custom code and is the least operationally intensive solution. Athena is for querying, not data balancing. Studio Classic built-in algorithms may handle imbalance but require model training iteration rather than preprocessing. AWS Glue DataBrew is primarily for data quality and profiling, not specialized imbalance handling.

Why the other options are wrong

  • A. Athena identifies patterns through queries but doesn't provide automated balancing operations.
  • B. SageMaker algorithms can handle imbalanced data but require training iterations rather than resolving the issue in preprocessing.
  • C. AWS Glue DataBrew is designed for data quality and profiling, not providing built-in class balancing operations.

Question 9

CASE STUDY

An ML engineer is developing a fraud detection model on AWS. The training dataset includes transaction logs, customer profiles, and tables from an on-premises MySQL database. The transaction logs and customer profiles are stored in Amazon S3.

The dataset has a class imbalance that affects the learning of the model's algorithm. Additionally, many of the features have interdependencies.

The algorithm is not capturing all the desired underlying patterns in the data.

The ML engineer needs to use an Amazon SageMaker built-in algorithm to train the model.

Which algorithm should the ML engineer use to meet this requirement?

  1. LightGBM
  2. Linear learner
  3. K-means clustering
  4. Neural Topic Model (NTM) ✅Correct Answer: A, LightGBM LightGBM is a SageMaker built-in gradient boosting algorithm that builds ensembles of decision trees, so it models the nonlinear interactions among interdependent features that the current algorithm is missing. For the class imbalance, it exposes parameters such as scale_pos_weight and is_unbalance to weight the minority fraud class, and its GOSS and exclusive feature bundling techniques keep training fast on large tabular data. This makes it the right fit for tabular fraud classification.
Show answer and explanation

Answer and explanation for question 9

Question 10

A company has deployed an XGBoost prediction model in production to predict if a customer is likely to cancel a subscription. The company uses Amazon SageMaker Model Monitor to detect deviations in the F1 score.

During a baseline analysis of model quality, the company recorded a threshold for the F1 score. After several months of no change, the model's F1 score decreases significantly.

What could be the reason for the reduced F1 score?

  1. Concept drift occurred in the underlying customer data that was used for predictions.
  2. The model was not sufficiently complex to capture all the patterns in the original baseline data.
  3. The original baseline data had a data quality issue of missing values.
  4. Incorrect ground truth labels were provided to Model Monitor during the calculation of the baseline.
Show answer and explanation

Correct answer: A. Concept drift occurred in the underlying customer data that was used for predictions.

Concept drift occurs when the statistical properties of the target variable change over time in production data. After months of stable performance, a sudden significant decrease in F1 score indicates the underlying customer behavior patterns have shifted, customers likely differ in their cancellation patterns from the baseline period. This is the most common reason for model performance degradation in production. The other options describe static issues that would have affected performance from the beginning, not caused degradation after months of stability.

Why the other options are wrong

  • B. If the model was insufficiently complex for baseline data, performance would have been poor from the start, not degrade after stable months.
  • C. Missing values in baseline data would have impacted initial model training and baseline performance, not cause degradation months later.
  • D. Incorrect ground truth labels during baseline calculation would affect the threshold itself, not explain actual performance changes in production.

That was 10 of 271.

The full AWS MLA-C01 pack has all 271 questions, each with the answer, the explanation and why the other options are wrong, plus a questions-only copy for timed runs. US$39, paid once, with free monthly updates and a pass-or-your-money-back guarantee.

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