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Free AWS Certified Machine Learning Engineer - Associate MLA-C01 Exam Braindumps
AWS Certified Machine Learning Engineer - Associate MLA-C01 Questions 81–88 (page: 12)

Updated On: 15-Apr-2026
Page 12 of 33
PDF with 270 Questions

A company has a binary classification model in production. An ML engineer needs to develop a new version of the model.

The new model version must maximize correct predictions of positive labels and negative labels. The ML engineer must use a metric to recalibrate the model to meet these requirements.

Which metric should the ML engineer use for the model recalibration?

  1. Accuracy
  2. Precision
  3. Recall
  4. Specificity

Answer(s): A

Explanation:

Accuracy measures the proportion of correctly predicted labels (both positive and negative) out of the total predictions. It is the appropriate metric when the goal is to maximize the correct predictions of both positive and negative labels. However, it assumes that the classes are balanced; if the classes are imbalanced, other metrics like precision, recall, or specificity may be more relevant depending on the specific needs.



A company is using Amazon SageMaker AI to create ML models. The company's data scientists need fine- grained control of the ML workflows that they orchestrate. The data scientists also need the ability to visualize SageMaker AI jobs and workflows as a directed acyclic graph (DAG). The data scientists must keep a running history of model discovery experiments and must establish model governance for auditing and compliance verifications.

Which solution will meet these requirements?

  1. Use AWS CodePipeline and its integration with SageMaker Studio to manage the entire ML workflows. Use SageMaker ML Lineage Tracking for the running history of experiments and for auditing and compliance verifications.
  2. Use AWS CodePipeline and its integration with SageMaker Experiments to manage the entire ML workflows. Use SageMaker Experiments for the running history of experiments and for auditing and compliance verifications.
  3. Use SageMaker Pipelines and its integration with SageMaker Studio to manage the entire ML workflows.
    Use SageMaker ML Lineage Tracking for the running history of experiments and for auditing and compliance verifications.
  4. Use SageMaker Pipelines and its integration with SageMaker Experiments to manage the entire ML workflows. Use SageMaker Experiments for the running history of experiments and for auditing and compliance verifications.

Answer(s): C

Explanation:

SageMaker Pipelines provides a directed acyclic graph (DAG) view for managing and visualizing ML workflows with fine-grained control. It integrates seamlessly with SageMaker Studio, offering an intuitive interface for workflow orchestration.
SageMaker ML Lineage Tracking keeps a running history of experiments and tracks the lineage of datasets, models, and training jobs. This feature supports model governance, auditing, and compliance verification requirements.



A company wants to reduce the cost of its containerized ML applications. The applications use ML models that run on Amazon EC2 instances, AWS Lambda functions, and an Amazon Elastic Container Service (Amazon ECS) cluster. The EC2 workloads and ECS workloads use Amazon Elastic Block Store (Amazon EBS) volumes to save predictions and artifacts.

An ML engineer must identify resources that are being used inefficiently. The ML engineer also must generate recommendations to reduce the cost of these resources.

Which solution will meet these requirements with the LEAST development effort?

  1. Create code to evaluate each instance's memory and compute usage.
  2. Add cost allocation tags to the resources. Activate the tags in AWS Billing and Cost Management.
  3. Check AWS CloudTrail event history for the creation of the resources.
  4. Run AWS Compute Optimizer.

Answer(s): D

Explanation:

AWS Compute Optimizer analyzes the resource usage of Amazon EC2 instances, ECS services, Lambda functions, and Amazon EBS volumes. It provides actionable recommendations to optimize resource utilization and reduce costs, such as resizing instances, moving workloads to Spot Instances, or changing volume types.
This solution requires the least development effort because Compute Optimizer is a managed service that automatically generates insights and recommendations based on historical usage data.



A company needs to create a central catalog for all the company's ML models. The models are in AWS accounts where the company developed the models initially. The models are hosted in Amazon Elastic Container Registry (Amazon ECR) repositories.

Which solution will meet these requirements?

  1. Configure ECR cross-account replication for each existing ECR repository. Ensure that each model is visible in each AWS account.
  2. Create a new AWS account with a new ECR repository as the central catalog. Configure ECR cross- account replication between the initial ECR repositories and the central catalog.
  3. Use the Amazon SageMaker Model Registry to create a model group for models hosted in Amazon ECR.
    Create a new AWS account. In the new account, use the SageMaker Model Registry as the central catalog.
    Attach a cross-account resource policy to each model group in the initial AWS accounts.
  4. Use an AWS Glue Data Catalog to store the models. Run an AWS Glue crawler to migrate the models from the ECR repositories to the Data Catalog. Configure cross-account access to the Data Catalog.

Answer(s): C

Explanation:

The Amazon SageMaker Model Registry is designed to manage and catalog ML models, including those hosted in Amazon ECR. By creating a model group for each model in the SageMaker Model Registry and setting up cross-account resource policies, the company can establish a central catalog in a new AWS account. This allows all models from the initial accounts to be accessible in a unified, centralized manner for better organization, management, and governance. This solution leverages existing AWS services and ensures scalability and minimal operational overhead.



A company has developed a new ML model. The company requires online model validation on 10% of the traffic before the company fully releases the model in production. The company uses an Amazon SageMaker endpoint behind an Application Load Balancer (ALB) to serve the model.

Which solution will set up the required online validation with the LEAST operational overhead?

  1. Use production variants to add the new model to the existing SageMaker endpoint. Set the variant weight to 0.1 for the new model. Monitor the number of invocations by using Amazon CloudWatch.
  2. Use production variants to add the new model to the existing SageMaker endpoint. Set the variant weight to 1 for the new model. Monitor the number of invocations by using Amazon CloudWatch.
  3. Create a new SageMaker endpoint. Use production variants to add the new model to the new endpoint.
    Monitor the number of invocations by using Amazon CloudWatch.
  4. Configure the ALB to route 10% of the traffic to the new model at the existing SageMaker endpoint. Monitor the number of invocations by using AWS CloudTrail.

Answer(s): A

Explanation:

SageMaker production variants allow you to deploy multiple models to a single endpoint and control the traffic distribution among them by assigning weights. By adding the new model as a production variant to the existing SageMaker endpoint and setting its weight to 0.1, 10% of the traffic will be routed to the new model for validation. This solution minimizes operational overhead by reusing the existing endpoint and leverages Amazon CloudWatch to monitor invocations for traffic validation and monitoring.



A company needs to develop an ML model. The model must identify an item in an image and must provide the location of the item.

Which Amazon SageMaker algorithm will meet these requirements?

  1. Image classification
  2. XGBoost
  3. Object detection
  4. K-nearest neighbors (k-NN)

Answer(s): C

Explanation:

The Amazon SageMaker Object Detection algorithm is specifically designed to identify objects in images and provide their locations using bounding boxes. This algorithm is based on deep learning models such as Single Shot MultiBox Detector (SSD) and Faster R-CNN, which are optimized for detecting and localizing multiple objects in an image. Since the requirement is to both identify and locate an item in an image, object detection is the most suitable choice.



A company has an Amazon S3 bucket that contains 1 TB of files from different sources. The S3 bucket contains the following file types in the same S3 folder: CSV, JSON, XLSX, and Apache Parquet.

An ML engineer must implement a solution that uses AWS Glue DataBrew to process the data. The ML engineer also must store the final output in Amazon S3 so that AWS Glue can consume the output in the future.

Which solution will meet these requirements?

  1. Use DataBrew to process the existing S3 folder. Store the output in Apache Parquet format.
  2. Use DataBrew to process the existing S3 folder. Store the output in AWS Glue Parquet format.
  3. Separate the data into a different folder for each file type. Use DataBrew to process each folder individually.
    Store the output in Apache Parquet format.
  4. Separate the data into a different folder for each file type. Use DataBrew to process each folder individually.
    Store the output in AWS Glue Parquet format.

Answer(s): C

Explanation:

AWS Glue DataBrew works best when data is structured consistently. Since the S3 bucket contains multiple file types (CSV, JSON, XLSX, and Parquet) in the same folder, separating them into different folders ensures that DataBrew can properly process each file type. Apache Parquet is the optimal output format because it is a columnar storage format that is highly efficient for querying and is natively supported by AWS Glue. Using this format enhances performance and compatibility for future processing.



A manufacturing company uses an ML model to determine whether products meet a standard for quality. The model produces an output of "Passed" or "Failed." Robots separate the products into the two categories by using the model to analyze photos on the assembly line.

Which metrics should the company use to evaluate the model's performance? (Choose two.)

  1. Precision and recall
  2. Root mean square error (RMSE) and mean absolute percentage error (MAPE)
  3. Accuracy and F1 score
  4. Bilingual Evaluation Understudy (BLEU) score
  5. Perplexity

Answer(s): A,C

Explanation:

The ML model is performing a binary classification task (determining whether products "Passed" or "Failed").
The appropriate metrics for evaluating such a model include:
Precision: Measures how many of the products classified as "Passed" were actually correct.



Recall: Measures how many of the actual "Passed" products were correctly classified.



Accuracy: Measures the overall correctness of the model's predictions.



F1 score: A balanced metric that considers both precision and recall, which is useful when there is an



imbalance between "Passed" and "Failed" categories.



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