MLA-C01 RELIABLE TEST TESTKING - AUTHORIZED MLA-C01 EXAM DUMPS

MLA-C01 Reliable Test Testking - Authorized MLA-C01 Exam Dumps

MLA-C01 Reliable Test Testking - Authorized MLA-C01 Exam Dumps

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Amazon AWS Certified Machine Learning Engineer - Associate Sample Questions (Q66-Q71):

NEW QUESTION # 66
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?

  • A. Use SageMaker Model Monitor to evaluate the performance of the model and to manage the approval.
  • B. Use SageMaker Pipelines. When a model version is registered, use the AWS SDK to change the approval status to "Approved."
  • C. Use SageMaker ML Lineage Tracking on the central model registry. Create tracking entities for the approval process.
  • D. Use SageMaker Experiments to facilitate the approval process during model registration.

Answer: B

Explanation:
To implement a manual approval-based workflow ensuring that only approved models are deployed to production endpoints, Amazon SageMaker provides integrated tools such asSageMaker Pipelinesand the SageMaker Model Registry.
SageMaker Pipelinesis a robust service for building, automating, and managing end-to-end machine learning workflows. It facilitates the orchestration of various steps in the ML lifecycle, including data preprocessing, model training, evaluation, and deployment. By integrating with theSageMaker Model Registry, it enables seamless tracking and management of model versions and their approval statuses.
Implementation Steps:
* Define the Pipeline:
* Create a SageMaker Pipeline encompassing steps for data preprocessing, model training, evaluation, and registration of the model in the Model Registry.
* Incorporate aCondition Stepto assess model performance metrics. If the model meets predefined criteria, proceed to the next step; otherwise, halt the process.
* Register the Model:
* Utilize theRegisterModelstep to add the trained model to the Model Registry.
* Set the ModelApprovalStatus parameter to PendingManualApproval during registration. This status indicates that the model awaits manual review before deployment.
* Manual Approval Process:
* Notify the designated approver upon model registration. This can be achieved by integrating Amazon EventBridge to monitor registration events and trigger notifications via AWS Lambda functions.
* The approver reviews the model's performance and, if satisfactory, updates the model's status to Approved using the AWS SDK or through the SageMaker Studio interface.
* Deploy the Approved Model:
* Configure the pipeline to automatically deploy models with an Approved status to the production endpoint. This can be managed by adding deployment steps conditioned on the model's approval status.
Advantages of This Approach:
* Automated Workflow:SageMaker Pipelines streamline the ML workflow, reducing manual interventions and potential errors.
* Governance and Compliance:The manual approval step ensures that only thoroughly evaluated models are deployed, aligning with organizational standards.
* Scalability:The solution supports complex ML workflows, making it adaptable to various project requirements.
By implementing this solution, the company can establish a controlled and efficient process for deploying models, ensuring that only approved versions reach production environments.
References:
* Automate the machine learning model approval process with Amazon SageMaker Model Registry and Amazon SageMaker Pipelines
* Update the Approval Status of a Model - Amazon SageMaker


NEW QUESTION # 67
A company has a large collection of chat recordings from customer interactions after a product release. An ML engineer needs to create an ML model to analyze the chat data. The ML engineer needs to determine the success of the product by reviewing customer sentiments about the product.
Which action should the ML engineer take to complete the evaluation in the LEAST amount of time?

  • A. Use random forests to classify sentiments of the chat conversations.
  • B. Use Amazon Comprehend to analyze sentiments of the chat conversations.
  • C. Use Amazon Rekognition to analyze sentiments of the chat conversations.
  • D. Train a Naive Bayes classifier to analyze sentiments of the chat conversations.

Answer: B

Explanation:
Amazon Comprehend is a fully managed natural language processing (NLP) service that includes a built-in sentiment analysis feature. It can quickly and efficiently analyze text data to determine whether the sentiment is positive, negative, neutral, or mixed. Using Amazon Comprehend requires minimal setup and provides accurate results without the need to train and deploy custom models, making it the fastest and most efficient solution for this task.


NEW QUESTION # 68
An ML engineer receives datasets that contain missing values, duplicates, and extreme outliers. The ML engineer must consolidate these datasets into a single data frame and must prepare the data for ML.
Which solution will meet these requirements?

  • A. Manually import and merge the datasets. Consolidate the datasets into a single data frame. Use Amazon SageMaker data labeling to prepare the data.
  • B. Use Amazon SageMaker Ground Truth to import the datasets and to consolidate them into a single data frame. Use the human-in-the-loop capability to prepare the data.
  • C. Manually import and merge the datasets. Consolidate the datasets into a single data frame. Use Amazon Q Developer to generate code snippets that will prepare the data.
  • D. Use Amazon SageMaker Data Wrangler to import the datasets and to consolidate them into a single data frame. Use the cleansing and enrichment functionalities to prepare the data.

Answer: D

Explanation:
Amazon SageMakerData Wranglerprovides a comprehensive solution for importing, consolidating, and preparing datasets for ML. It offers tools to handle missing values, duplicates, and outliers through its built- incleansingandenrichmentfunctionalities, allowing the ML engineer to efficiently prepare the data in a single environment with minimal manual effort.


NEW QUESTION # 69
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?

  • A. Configure the application to invoke an AWS Lambda function that runs a SageMaker Clarify job.
  • B. Use AWS Glue Data Quality to monitor bias.
  • C. Invoke an AWS Lambda function to pull the sagemaker-model-monitor-analyzer built-in SageMaker image.
  • D. Use SageMaker notebooks to compare the bias.

Answer: A

Explanation:
Monitoring bias drift in deployed machine learning models is crucial to ensure fairness and accuracy over time. Amazon SageMaker Clarify provides tools to detect bias in ML models, both during training and after deployment. To monitor bias drift for models deployed to real-time endpoints, an effective approach involves orchestrating SageMaker Clarify jobs using AWS Lambda functions.
Implementation Steps:
* Set Up Data Capture:
* Enable data capture on the SageMaker endpoint to record input data and model predictions. This captured data serves as the basis for bias analysis.
* Develop a Lambda Function:
* Create an AWS Lambda function configured to initiate a SageMaker Clarify job. This function will process the captured data to assess bias metrics.
* Schedule or Trigger the Lambda Function:
* Configure the Lambda function to run on-demand or at scheduled intervals using Amazon CloudWatch Events or EventBridge. This setup allows for regular bias monitoring as per the application's requirements.
* Analyze and Respond to Results:
* After each Clarify job completes, review the generated bias reports. If bias drift is detected, take appropriate actions, such as retraining the model or adjusting data preprocessing steps.
Advantages of This Approach:
* Automation:Utilizing AWS Lambda for orchestrating Clarify jobs enables automated and scalable bias monitoring without manual intervention.
* Cost-Effectiveness:AWS Lambda's serverless nature ensures that you only pay for the compute time consumed during the execution of the function, optimizing resource usage.
* Flexibility:The solution can be tailored to specific monitoring needs, allowing for adjustments in monitoring frequency and analysis parameters.
By implementing this solution, the company can effectively monitor bias drift in real-time, ensuring that the AI application maintains fairness and accuracy throughout its lifecycle.
References:
* Bias drift for models in production - Amazon SageMaker
* Schedule Bias Drift Monitoring Jobs - Amazon SageMaker


NEW QUESTION # 70
A company needs to give its ML engineers appropriate access to training data. The ML engineers must access training data from only their own business group. The ML engineers must not be allowed to access training data from other business groups.
The company uses a single AWS account and stores all the training data in Amazon S3 buckets. All ML model training occurs in Amazon SageMaker.
Which solution will provide the ML engineers with the appropriate access?

  • A. Configure S3 Object Lock settings for each user.
  • B. Create IAM policies. Attach the policies to IAM users or IAM roles.
  • C. Add cross-origin resource sharing (CORS) policies to the S3 buckets.
  • D. Enable S3 bucket versioning.

Answer: B

Explanation:
By creating IAM policies with specific permissions, you can restrict access to Amazon S3 buckets or objects based on the user's business group. These policies can be attached to IAM users or IAM roles associated with the ML engineers, ensuring that each engineer can only access training data belonging to their group. This approach is secure, scalable, and aligns with AWS best practices for access control.


NEW QUESTION # 71
......

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