Practice Free MLA-C01 Exam Online Questions
An ML engineer is tuning an image classification model that shows poor performance on one of two available classes during prediction. Analysis reveals that the images whose class the model performed poorly on represent an extremely small fraction of the whole training dataset.
The ML engineer must improve the model’s performance.
Which solution will meet this requirement?
- A . Optimize for accuracy. Use image augmentation on the less common images to generate new samples.
- B . Optimize for F1 score. Use image augmentation on the less common images to generate new samples.
- C . Optimize for accuracy. Use Synthetic Minority Oversampling Technique (SMOTE) on the less common images to generate new samples.
- D . Optimize for F1 score. Use Synthetic Minority Oversampling Technique (SMOTE) on the less common images to generate new samples.
B
Explanation:
This problem describes severe class imbalance in an image classification task, where the minority class has poor predictive performance. In such cases, accuracy is a misleading metric, because a model can achieve high accuracy by predicting only the majority class. AWS ML best practices recommend using F1 score, which balances precision and recall and is more appropriate for imbalanced classification problems.
To improve performance on the minority image class, image augmentation is the preferred approach. Augmentation techniques―such as rotation, cropping, flipping, and brightness adjustment―create realistic new training examples while preserving semantic meaning. AWS documentation recommends augmentation for computer vision workloads to improve generalization without collecting new data.
SMOTE (Options C and D) is designed for tabular data, not image data, and generating synthetic pixel-level images using SMOTE is not appropriate or supported in typical computer vision pipelines.
Option A is incorrect because optimizing for accuracy does not address minority-class performance.
Option D is incorrect because SMOTE is unsuitable for images.
Therefore, optimizing for F1 score and using image augmentation on the minority class is the correct solution.
A company uses Amazon SageMaker Studio to develop an ML model. The company has a single SageMaker Studio domain. An ML engineer needs to implement a solution that provides an automated alert when SageMaker AI compute costs reach a specific threshold.
Which solution will meet these requirements?
- A . Add resource tagging by editing the SageMaker AI user profile in the SageMaker AI domain.
Configure AWS Cost Explorer to send an alert when the threshold is reached. - B . Add resource tagging by editing the SageMaker AI user profile in the SageMaker AI domain.
Configure AWS Budgets to send an alert when the threshold is reached. - C . Add resource tagging by editing each user’s IAM profile. Configure AWS Cost Explorer to send an alert when the threshold is reached.
- D . Add resource tagging by editing each user’s IAM profile. Configure AWS Budgets to send an alert when the threshold is reached.
B
Explanation:
AWS best practices for cost governance recommend using resource tagging combined with AWS Budgets to track and alert on service-level spending. By adding tags at the SageMaker Studio user profile level, all compute resources launched by users inherit those tags automatically.
AWS Budgets supports threshold-based alerts, unlike AWS Cost Explorer, which is primarily used for historical analysis and visualization. Budgets can trigger notifications via email or Amazon SNS when spending exceeds defined limits.
IAM profiles are unrelated to cost tracking, making options C and D invalid.
Therefore, tagging SageMaker user profiles and using AWS Budgets is the correct solution.
A company uses a batching solution to process daily analytics. The company wants to provide near real-time updates, use open-source technology, and avoid managing or scaling infrastructure.
Which solution will meet these requirements?
- A . Create Amazon Managed Streaming for Apache Kafka (Amazon MSK) Serverless clusters.
- B . Create Amazon MSK Provisioned clusters.
- C . Create Amazon Kinesis Data Streams with Application Auto Scaling.
- D . Create self-hosted Apache Flink applications on Amazon EC2.
A
Explanation:
Amazon MSK Serverless provides a fully managed Apache Kafka-compatible service that automatically handles provisioning, scaling, and capacity management. AWS documentation states that MSK Serverless is designed for customers who want Kafka functionality without managing infrastructure.
Option B requires capacity planning and scaling management.
Option C uses proprietary technology rather than open source.
Option D requires full infrastructure management.
MSK Serverless delivers near real-time streaming with minimal operational overhead while maintaining compatibility with open-source Kafka tooling.
Therefore, Option A is the correct solution.
A company uses Amazon SageMaker Studio to develop an ML model. The company has a single SageMaker Studio domain. An ML engineer needs to implement a solution that provides an automated alert when SageMaker compute costs reach a specific threshold.
Which solution will meet these requirements?
- A . Add resource tagging by editing the SageMaker user profile in the SageMaker domain. Configure AWS Cost Explorer to send an alert when the threshold is reached.
- B . Add resource tagging by editing the SageMaker user profile in the SageMaker domain. Configure AWS Budgets to send an alert when the threshold is reached.
- C . Add resource tagging by editing each user’s IAM profile. Configure AWS Cost Explorer to send an alert when the threshold is reached.
- D . Add resource tagging by editing each user’s IAM profile. Configure AWS Budgets to send an alert when the threshold is reached.
B
Explanation:
Adding resource tagging to the SageMaker user profile enables tracking and monitoring of costs associated with specific SageMaker resources.
AWS Budgets allows setting thresholds and automated alerts for costs and usage, making it the ideal service to notify the ML engineer when compute costs reach a specified limit.
This solution is efficient and integrates seamlessly with SageMaker and AWS cost management tools.
An ML engineer is developing a fraud detection model by using the Amazon SageMaker XGBoost algorithm. The model classifies transactions as either fraudulent or legitimate.
During testing, the model excels at identifying fraud in the training dataset. However, the model is inefficient at identifying fraud in new and unseen transactions.
What should the ML engineer do to improve the fraud detection for new transactions?
- A . Increase the learning rate.
- B . Remove some irrelevant features from the training dataset.
- C . Increase the value of the max_depth hyperparameter.
- D . Decrease the value of the max_depth hyperparameter.
D
Explanation:
A high max_depth value in XGBoost can lead to overfitting, where the model learns the training dataset too well but fails to generalize to new and unseen data. By decreasing the max_depth, the model becomes less complex, reducing overfitting and improving its ability to detect fraud in new transactions. This adjustment helps the model focus on general patterns rather than memorizing specific details in the training data.
A company regularly receives new training data from a vendor of an ML model. The vendor delivers cleaned and prepared data to the company’s Amazon S3 bucket every 3C4 days.
The company has an Amazon SageMaker AI pipeline to retrain the model. An ML engineer needs to run the pipeline automatically when new data is uploaded to the S3 bucket.
Which solution will meet these requirements with the LEAST operational effort?
- A . Create an S3 lifecycle rule to transfer the data to the SageMaker AI training instance and initiate training.
- B . Create an AWS Lambda function that scans the S3 bucket and initiates the pipeline when new data is uploaded.
- C . Create an Amazon EventBridge rule that matches S3 upload events and configures the SageMaker pipeline as the target.
- D . Use Amazon Managed Workflows for Apache Airflow (MWAA) to orchestrate the pipeline when new data is uploaded.
C
Explanation:
AWS best practices recommend event-driven architectures to automate ML workflows with minimal operational overhead. Amazon EventBridge natively integrates with Amazon S3 and Amazon SageMaker Pipelines, making it the most efficient solution for triggering retraining when new data arrives.
Amazon S3 automatically emits object creation events. By creating an EventBridge rule that listens for these events and targets a SageMaker Pipeline execution, the pipeline can start immediately when new training data is uploaded. This solution requires no custom code, no polling, and no infrastructure management.
Option A is incorrect because S3 lifecycle rules manage storage transitions, not workflow execution.
Option B introduces custom code and periodic scanning, which increases operational complexity and cost.
Option D (MWAA) is powerful but requires maintaining an Airflow environment and is unnecessary for a simple event-based trigger.
AWS documentation explicitly highlights EventBridge + SageMaker Pipelines as the recommended pattern for automated retraining workflows triggered by data arrival.
Therefore, Option C is the correct and AWS-verified answer.
A company has an ML model that needs to run one time each night to predict stock values. The model input is 3 MB of data that is collected during the current day. The model produces the predictions for the next day. The prediction process takes less than 1 minute to finish running.
How should the company deploy the model on Amazon SageMaker to meet these requirements?
- A . Use a multi-model serverless endpoint. Enable caching.
- B . Use an asynchronous inference endpoint. Set the Initial Instance Count parameter to 0.
- C . Use a real-time endpoint. Configure an auto scaling policy to scale the model to 0 when the model is not in use.
- D . Use a serverless inference endpoint. Set the MaxConcurrency parameter to 1.
D
Explanation:
A serverless inference endpoint in Amazon SageMaker is ideal for use cases where the model is invoked infrequently, such as running one time each night. It eliminates the cost of idle resources when the model is not in use. Setting the MaxConcurrency parameter to 1 ensures cost-efficiency while supporting the required single nightly invocation. This solution minimizes costs and matches the requirement to process a small amount of data quickly.
An ML engineer needs to deploy a trained model based on a genetic algorithm. Predictions can take several minutes, and requests can include up to 100 MB of data.
Which deployment solution will meet these requirements with the LEAST operational overhead?
- A . Deploy on EC2 Auto Scaling behind an ALB.
- B . Deploy to a SageMaker AI real-time endpoint.
- C . Deploy to a SageMaker AI Asynchronous Inference endpoint.
- D . Deploy to Amazon ECS on EC2.
C
Explanation:
SageMaker Asynchronous Inference is designed for long-running inference workloads and large payloads (up to 1 GB). Requests are queued, processed asynchronously, and results are written to Amazon S3.
Real-time endpoints have payload and timeout limits. EC2 and ECS require infrastructure management, increasing operational overhead.
AWS documentation explicitly recommends asynchronous inference for workloads with large inputs and long execution times.
Therefore, Option C is the correct and most efficient solution.
An ML engineer is developing a classification model. The ML engineer needs to use custom libraries in processing jobs, training jobs, and pipelines in Amazon SageMaker AI.
Which solution will provide this functionality with the LEAST implementation effort?
- A . Manually install the libraries in the SageMaker AI containers.
- B . Build a custom Docker container that includes the required libraries. Host the container in Amazon Elastic Container Registry (Amazon ECR). Use the ECR image in the SageMaker AI jobs and pipelines.
- C . Use a SageMaker AI notebook instance and install libraries at startup.
- D . Run code externally on Amazon EC2 and import results into SageMaker AI.
B
Explanation:
AWS documentation strongly recommends using custom Docker containers when ML workloads require consistent access to custom dependencies across processing jobs, training jobs, and pipelines.
By building a single Docker image that contains all required libraries and hosting it in Amazon ECR, the ML engineer ensures that every SageMaker job uses the same runtime environment. This approach eliminates the need for repetitive installation steps and avoids environment drift.
Manually installing libraries in managed containers is error-prone and not reusable across jobs. Notebook instances are not designed to host production jobs and pipelines. Running code externally breaks the SageMaker workflow and increases operational complexity.
Using a custom container is a one-time setup that provides maximum reuse with minimal ongoing effort, making it the least implementation effort option in the long run.
Therefore, Option B is the correct and AWS-recommended answer.
A company wants to deploy an Amazon SageMaker AI model that can queue requests. The model needs to handle payloads of up to 1 GB that take up to 1 hour to process. The model must return an inference for each request. The model also must scale down when no requests are available to process.
Which inference option will meet these requirements?
- A . Asynchronous inference
- B . Batch transform
- C . Serverless inference
- D . Real-time inference
A
Explanation:
Amazon SageMaker Asynchronous Inference is specifically designed for long-running inference requests and large payloads. It supports payload sizes up to 1 GB and processing times of up to 1 hour, while automatically queuing requests.
Asynchronous inference stores results in Amazon S3 and allows clients to retrieve inference outputs after processing completes. It also supports auto scaling down to zero when there are no incoming requests, reducing cost.
Batch transform is intended for offline, bulk inference and does not return per-request results in an asynchronous requestCresponse pattern. Serverless and real-time inference have strict payload size and timeout limits that do not support 1-hour processing.
Therefore, asynchronous inference is the only SageMaker inference option that meets all stated requirements.