100% PASS MLA-C01 FREE BRAIN DUMPS - REALISTIC AWS CERTIFIED MACHINE LEARNING ENGINEER - ASSOCIATE CERTIFICATE EXAM

100% Pass MLA-C01 Free Brain Dumps - Realistic AWS Certified Machine Learning Engineer - Associate Certificate Exam

100% Pass MLA-C01 Free Brain Dumps - Realistic AWS Certified Machine Learning Engineer - Associate Certificate Exam

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Amazon AWS Certified Machine Learning Engineer - Associate Sample Questions (Q55-Q60):

NEW QUESTION # 55
An ML engineer needs to use an Amazon EMR cluster to process large volumes of data in batches. Any data loss is unacceptable.
Which instance purchasing option will meet these requirements MOST cost-effectively?

  • A. Run the primary node, core nodes, and task nodes on On-Demand Instances.
  • B. Run the primary node on an On-Demand Instance. Run the core nodes and task nodes on Spot Instances.
  • C. Run the primary node, core nodes, and task nodes on Spot Instances.
  • D. Run the primary node and core nodes on On-Demand Instances. Run the task nodes on Spot Instances.

Answer: D

Explanation:
For Amazon EMR, the primary node and core nodes handle the critical functions of the cluster, including data storage (HDFS) and processing. Running them on On-Demand Instances ensures high availability and prevents data loss, as Spot Instances can be interrupted. The task nodes, which handle additionalprocessing but do not store data, can use Spot Instances to reduce costs without compromising the cluster's resilience or data integrity. This configuration balances cost-effectiveness and reliability.


NEW QUESTION # 56
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?

  • A. 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.
  • B. 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.
  • C. 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.
  • D. 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.

Answer: B

Explanation:
Scenario:The company wants to perform online validation of a new ML model on 10% of the traffic before fully deploying the model in production. The setup must have minimal operational overhead.
Why Use SageMaker Production Variants?
* Built-In Traffic Splitting:Amazon SageMaker endpoints support production variants, allowing multiple models to run on a single endpoint. You can direct a percentage of incoming traffic to each variant by adjusting the variant weights.
* Ease of Management:Using production variants eliminates the need for additional infrastructure like separate endpoints or custom ALB configurations.
* Monitoring with CloudWatch:SageMaker automatically integrates with CloudWatch, enabling real- time monitoring of model performance and invocation metrics.
Steps to Implement:
* Deploy the New Model as a Production Variant:
* Update the existing SageMaker endpoint to include the new model as a production variant. This can be done via the SageMaker console, CLI, or SDK.
Example SDK Code:
import boto3
sm_client = boto3.client('sagemaker')
response = sm_client.update_endpoint_weights_and_capacities(
EndpointName='existing-endpoint-name',
DesiredWeightsAndCapacities=[
{'VariantName': 'current-model', 'DesiredWeight': 0.9},
{'VariantName': 'new-model', 'DesiredWeight': 0.1}
]
)
* Set the Variant Weight:
* Assign a weight of 0.1 to the new model and 0.9 to the existing model. This ensures 10% of traffic goes to the new model while the remaining 90% continues to use the current model.
* Monitor the Performance:
* Use Amazon CloudWatch metrics, such as InvocationCount and ModelLatency, to monitor the traffic and performance of each variant.
* Validate the Results:
* Analyze the performance of the new model based on metrics like accuracy, latency, and failure rates.
Why Not the Other Options?
* Option B:Setting the weight to 1 directs all traffic to the new model, which does not meet the requirement of splitting traffic for validation.
* Option C:Creating a new endpoint introduces additional operational overhead for traffic routing and monitoring, which is unnecessary given SageMaker's built-in production variant capability.
* Option D:Configuring the ALB to route traffic requires manual setup and lacks SageMaker's seamless variant monitoring and traffic splitting features.
Conclusion:Using production variants with a weight of 0.1 for the new model on the existing SageMaker endpoint provides the required traffic split for online validation with minimal operational overhead.
References:
* Amazon SageMaker Endpoints
* SageMaker Production Variants
* Monitoring SageMaker Endpoints with CloudWatch


NEW QUESTION # 57
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 ML Lineage Tracking on the central model registry. Create tracking entities for the approval process.
  • B. Use SageMaker Pipelines. When a model version is registered, use the AWS SDK to change the approval status to "Approved."
  • C. Use SageMaker Model Monitor to evaluate the performance of the model and to manage the approval.
  • 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 # 58
A company that has hundreds of data scientists is using Amazon SageMaker to create ML models. The models are in model groups in the SageMaker Model Registry.
The data scientists are grouped into three categories: computer vision, natural language processing (NLP), and speech recognition. An ML engineer needs to implement a solution to organize the existing models into these groups to improve model discoverability at scale. The solution must not affect the integrity of the model artifacts and their existing groupings.
Which solution will meet these requirements?

  • A. Create a Model Registry collection for each of the three categories. Move the existing model groups into the collections.
  • B. Create a model group for each category. Move the existing models into these category model groups.
  • C. Use SageMaker ML Lineage Tracking to automatically identify and tag which model groups should contain the models.
  • D. Create a custom tag for each of the three categories. Add the tags to the model packages in the SageMaker Model Registry.

Answer: D

Explanation:
Using custom tags allows you to organize and categorize models in the SageMaker Model Registry without altering their existing groupings or affecting the integrity of the model artifacts. Tags are a lightweight and scalable way to improve model discoverability at scale, enabling the data scientists to filter and identify models by category (e.g., computer vision, NLP, speech recognition). This approach meets the requirements efficiently without introducing structural changes to the existing model registry setup.


NEW QUESTION # 59
A company is gathering audio, video, and text data in various languages. The company needs to use a large language model (LLM) to summarize the gathered data that is in Spanish.
Which solution will meet these requirements in the LEAST amount of time?

  • A. Use Amazon Comprehend and Amazon Translate to convert the data into English text. Use Amazon Bedrock with the Stable Diffusion model to summarize the text.
  • B. Train and deploy a model in Amazon SageMaker to convert the data into English text. Train and deploy an LLM in SageMaker to summarize the text.
  • C. Use Amazon Transcribe and Amazon Translate to convert the data into English text. Use Amazon Bedrock with the Jurassic model to summarize the text.
  • D. Use Amazon Rekognition and Amazon Translate to convert the data into English text. Use Amazon Bedrock with the Anthropic Claude model to summarize the text.

Answer: C

Explanation:
Amazon Transcribeis well-suited for converting audio data into text, including Spanish.
Amazon Translatecan efficiently translate Spanish text into English if needed.
Amazon Bedrock, with theJurassic model, is designed for tasks like text summarization and can handle large language models (LLMs) seamlessly. This combination provides a low-code, managed solution to process audio, video, and text data with minimal time and effort.


NEW QUESTION # 60
......

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