FREE PDF QUIZ 2025 MLA-C01: AWS CERTIFIED MACHINE LEARNING ENGINEER - ASSOCIATE USEFUL EXAM SIMULATOR ONLINE

Free PDF Quiz 2025 MLA-C01: AWS Certified Machine Learning Engineer - Associate Useful Exam Simulator Online

Free PDF Quiz 2025 MLA-C01: AWS Certified Machine Learning Engineer - Associate Useful Exam Simulator Online

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Amazon AWS Certified Machine Learning Engineer - Associate Sample Questions (Q45-Q50):

NEW QUESTION # 45
An ML engineer is evaluating several ML models and must choose one model to use in production. The cost of false negative predictions by the models is much higher than the cost of false positive predictions.
Which metric finding should the ML engineer prioritize the MOST when choosing the model?

  • A. High recall
  • B. Low recall
  • C. High precision
  • D. Low precision

Answer: A

Explanation:
Recall measures the ability of a model to correctly identify all positive cases (true positives) out of all actual positives, minimizing false negatives. Since the cost of false negatives is much higher than falsepositives in this scenario, the ML engineer should prioritize models with high recall to reduce the likelihood of missing positive cases.


NEW QUESTION # 46
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 and core nodes on On-Demand Instances. Run the task nodes on Spot Instances.
  • B. Run the primary node, core nodes, and task nodes on On-Demand Instances.
  • C. Run the primary node, core nodes, and task nodes on Spot Instances.
  • D. Run the primary node on an On-Demand Instance. Run the core nodes and task nodes on Spot Instances.

Answer: A

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 # 47
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. 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.
  • 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. 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: 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 # 48
An ML engineer is using Amazon SageMaker to train a deep learning model that requires distributed training.
After some training attempts, the ML engineer observes that the instances are not performing as expected. The ML engineer identifies communication overhead between the training instances.
What should the ML engineer do to MINIMIZE the communication overhead between the instances?

  • A. Place the instances in the same VPC subnet. Store the data in the same AWS Region and Availability Zone where the instances are deployed.
  • B. Place the instances in the same VPC subnet. Store the data in a different AWS Region from where the instances are deployed.
  • C. Place the instances in the same VPC subnet but in different Availability Zones. Store the data in a different AWS Region from where the instances are deployed.
  • D. Place the instances in the same VPC subnet. Store the data in the same AWS Region but in a different Availability Zone from where the instances are deployed.

Answer: A

Explanation:
To minimize communication overhead during distributed training:
1. Same VPC Subnet: Ensures low-latency communication between training instances by keeping the network traffic within a single subnet.
2. Same AWS Region and Availability Zone: Reduces network latency further because cross-AZ communication incurs additional latency and costs.
3. Data in the Same Region and AZ: Ensures that the training data is accessed with minimal latency, improving performance during training.
This configuration optimizes communication efficiency and minimizes overhead.


NEW QUESTION # 49
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 document attribute boosting in Amazon Q Business to deprioritize the competitor's name.
  • C. Configure an Amazon Kendra retriever for Amazon Q Business to build indexes that exclude 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 # 50
......

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