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【Hardware】 Pass Guaranteed Amazon - MLA-C01 Authoritative Real Question

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Amazon AWS Certified Machine Learning Engineer - Associate Sample Questions (Q83-Q88):NEW QUESTION # 83
An ML engineer needs to use data with Amazon SageMaker Canvas to train an ML model. The data is stored in Amazon S3 and is complex in structure. The ML engineer must use a file format that minimizes processing time for the data.
Which file format will meet these requirements?
  • A. JSON files compressed with gzip
  • B. JSON objects in JSONL format
  • C. CSV files compressed with Snappy
  • D. Apache Parquet files
Answer: D

NEW QUESTION # 84
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. 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.
  • B. 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.
  • C. 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.
  • D. 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: C
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 # 85
A company has several teams that have developed separate prediction models on their own laptops. The teams developed the models by using Python with scikit-learn and TensorFlow frameworks.
The company must rebuild the models and must integrate the models into an ML infrastructure that the company manages by using Amazon SageMaker. The company also must incorporate the models into a model registry.
Which solution will meet these requirements with the LEAST operational overhead?
  • A. Export the models from the laptops to an Amazon S3 bucket. Use an Amazon API Gateway REST API and AWS Lambda functions with SageMaker endpoints to access the models. Register the models in the SageMaker Model Registry.
  • B. Use code from the laptops to create containers for the models. Use the bring your own container (BYOC) functionality of SageMaker to import and use the models. Register the models in the SageMaker Model Registry.
  • C. Import the models into the SageMaker Model Registry. Use SageMaker to run the imported models.
  • D. Import the Python-based models into SageMaker. Rebuild the scikit-learn and TensorFlow models in SageMaker. Register all the models in the SageMaker Model Registry.
Answer: D
Explanation:
The least operational overhead comes from directly importing the scikit-learn and TensorFlow models into SageMaker, rebuilding them using the respective prebuilt SageMaker frameworks, and then registering them in the SageMaker Model Registry. This leverages managed framework containers provided by SageMaker, avoids custom container management, and integrates seamlessly with the registry.

NEW QUESTION # 86
A company is creating an application that will recommend products for customers to purchase. The application will make API calls to Amazon Q Business. The company must ensure that responses from Amazon Q Business do not include the name of the company's main competitor.
Which solution will meet this requirement?
  • A. Configure an Amazon Q Business retriever to exclude the competitor's name.
  • B. Configure an Amazon Kendra retriever for Amazon Q Business to build indexes that exclude the competitor's name.
  • C. Configure document attribute boosting in Amazon Q Business to deprioritize the competitor's name.
  • D. Configure the competitor's name as a blocked phrase in Amazon Q Business.
Answer: D
Explanation:
Amazon Q Business allows configuring blocked phrases to exclude specific terms or phrases from the responses. By adding the competitor's name as a blocked phrase, the company can ensure that it will not appear in the API responses, meeting the requirement efficiently with minimal configuration.

NEW QUESTION # 87
A company wants to use Amazon SageMaker AI to host an ML model that runs on CPU for real-time predictions. The model has intermittent traffic during business hours and periods of no traffic after business hours.
Which hosting option will serve inference requests in the MOST cost-effective manner?
  • A. Deploy the model to a real-time endpoint with scheduled auto scaling.
  • B. Deploy the model to a SageMaker AI Serverless Inference endpoint with provisioned concurrency during business hours.
  • C. Deploy the model to an asynchronous inference endpoint with auto scaling to zero.
  • D. Deploy the model to a real-time endpoint and activate it only during business hours using AWS Lambda.
Answer: B
Explanation:
AWS recommends SageMaker Serverless Inference for workloads with intermittent or unpredictable traffic.
Serverless inference automatically scales compute resources to zero when idle, eliminating costs during periods with no traffic.
For business-hour traffic spikes, provisioned concurrency ensures low-latency responses while still avoiding the cost of continuously running instances. This model is especially cost-effective for CPU-based inference workloads.
Real-time endpoints incur costs even when idle, and asynchronous inference is designed for long-running jobs rather than low-latency predictions.
AWS documentation explicitly states that Serverless Inference is the most cost-effective option for intermittent real-time workloads.
Therefore, Option B is the correct choice.

NEW QUESTION # 88
......
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