LATEST MLA-C01 DUMPS FILES & NEW MLA-C01 DUMPS QUESTIONS

Latest MLA-C01 Dumps Files & New MLA-C01 Dumps Questions

Latest MLA-C01 Dumps Files & New MLA-C01 Dumps Questions

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Tags: Latest MLA-C01 Dumps Files, New MLA-C01 Dumps Questions, Latest MLA-C01 Test Question, Test MLA-C01 Engine Version, Exam MLA-C01 Review

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Amazon AWS Certified Machine Learning Engineer - Associate Sample Questions (Q10-Q15):

NEW QUESTION # 10
A company has used Amazon SageMaker to deploy a predictive ML model in production. The company is using SageMaker Model Monitor on the model. After a model update, an ML engineer notices data quality issues in the Model Monitor checks.
What should the ML engineer do to mitigate the data quality issues that Model Monitor has identified?

  • A. Adjust the model's parameters and hyperparameters.
  • B. Include additional data in the existing training set for the model. Retrain and redeploy the model.
  • C. Initiate a manual Model Monitor job that uses the most recent production data.
  • D. Create a new baseline from the latest dataset. Update Model Monitor to use the new baseline for evaluations.

Answer: D

Explanation:
When Model Monitor identifies data quality issues, it might be due to a shift in the data distribution compared to the original baseline. By creating a new baseline using the most recent production data and updating Model Monitor to evaluate against this baseline, the ML engineer ensures that the monitoring is aligned with the current data patterns. This approach mitigates false positives and reflects the updated data characteristics without immediately retraining the model.


NEW QUESTION # 11
A company has AWS Glue data processing jobs that are orchestrated by an AWS Glue workflow. The AWS Glue jobs can run on a schedule or can be launched manually.
The company is developing pipelines in Amazon SageMaker Pipelines for ML model development. The pipelines will use the output of the AWS Glue jobs during the data processing phase of model development.
An ML engineer needs to implement a solution that integrates the AWS Glue jobs with the pipelines.
Which solution will meet these requirements with the LEAST operational overhead?

  • A. Use processing steps in SageMaker Pipelines. Configure inputs that point to the Amazon Resource Names (ARNs) of the AWS Glue jobs.
  • B. Use Callback steps in SageMaker Pipelines to start the AWS Glue workflow and to stop the pipelines until the AWS Glue jobs finish running.
  • C. Use AWS Step Functions for orchestration of the pipelines and the AWS Glue jobs.
  • D. Use Amazon EventBridge to invoke the pipelines and the AWS Glue jobs in the desired order.

Answer: B

Explanation:
Callback steps in Amazon SageMaker Pipelines allow you to integrate external processes, such as AWS Glue jobs, into the pipeline workflow. By using a Callback step, the SageMaker pipeline can trigger the AWS Glue workflow and pause execution until the Glue jobs complete. This approach provides seamless integration with minimal operational overhead, as it directly ties the pipeline's execution flow to the completion of the AWS Glue jobs without requiring additional orchestration tools or complex setups.


NEW QUESTION # 12
An ML engineer has an Amazon Comprehend custom model in Account A in the us-east-1 Region. The ML engineer needs to copy the model to Account # in the same Region.
Which solution will meet this requirement with the LEAST development effort?

  • A. Use AWS DataSync to replicate the model from Account A to Account B.
  • B. Use Amazon S3 to make a copy of the model. Transfer the copy to Account B.
  • C. Create an AWS Site-to-Site VPN connection between Account A and Account # to transfer the model.
  • D. Create a resource-based IAM policy. Use the Amazon Comprehend ImportModel API operation to copy the model to Account B.

Answer: D

Explanation:
Amazon Comprehend provides the ImportModel API operation, which allows you to copy a custom model between AWS accounts. By creating a resource-based IAM policy on the model in Account A, you can grant Account B the necessary permissions to access and import the model. This approach requires minimal development effort and is the AWS-recommended method for sharing custom models across accounts.


NEW QUESTION # 13
A company is planning to use Amazon SageMaker to make classification ratings that are based on images.
The company has 6 ## of training data that is stored on an Amazon FSx for NetApp ONTAP system virtual machine (SVM). The SVM is in the same VPC as SageMaker.
An ML engineer must make the training data accessible for ML models that are in the SageMaker environment.
Which solution will meet these requirements?

  • A. Create a catalog connection from SageMaker Data Wrangler to the FSx for ONTAP file system.
  • B. Create a direct connection from SageMaker Data Wrangler to the FSx for ONTAP file system.
  • C. Create an Amazon S3 bucket. Use Mountpoint for Amazon S3 to link the S3 bucket to the FSx for ONTAP file system.
  • D. Mount the FSx for ONTAP file system as a volume to the SageMaker Instance.

Answer: D

Explanation:
Amazon FSx for NetApp ONTAP allows mounting the file system as a network-attached storage (NAS) volume. Since the FSx for ONTAP file system and SageMaker instance are in the same VPC, you can directly mount the file system to the SageMaker instance. This approach ensures efficient access to the 6 TB of training data without the need to duplicate or transfer the data, meeting the requirements with minimal complexity and operational overhead.


NEW QUESTION # 14
A company is planning to create several ML prediction models. The training data is stored in Amazon S3. The entire dataset is more than 5 ## in size and consists of CSV, JSON, Apache Parquet, and simple text files.
The data must be processed in several consecutive steps. The steps include complex manipulations that can take hours to finish running. Some of the processing involves natural language processing (NLP) transformations. The entire process must be automated.
Which solution will meet these requirements?

  • A. Use Amazon SageMaker notebooks for each data processing step. Automate the process by using Amazon EventBridge.
  • B. Use Amazon SageMaker Pipelines to create a pipeline of data processing steps. Automate the pipeline by using Amazon EventBridge.
  • C. Process data at each step by using Amazon SageMaker Data Wrangler. Automate the process by using Data Wrangler jobs.
  • D. Process data at each step by using AWS Lambda functions. Automate the process by using AWS Step Functions and Amazon EventBridge.

Answer: B

Explanation:
Amazon SageMaker Pipelines is designed for creating, automating, and managing end-to-end ML workflows, including complex data preprocessing tasks. It supports handling large datasets and can integrate with custom steps, such as NLP transformations. By combining SageMaker Pipelines with Amazon EventBridge, the entire workflow can be triggered and automated efficiently, meeting the requirements for scalability, automation, and processing complexity.


NEW QUESTION # 15
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