PDF AI1-C01 VCE - AI1-C01 PDF VCE

PDF AI1-C01 VCE - AI1-C01 PDF VCE

PDF AI1-C01 VCE - AI1-C01 PDF VCE

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Amazon AWS Certified AI Practitioner Exam Sample Questions (Q20-Q25):

NEW QUESTION # 20
An AI practitioner trained a custom model on Amazon Bedrock by using a training dataset that contains confidential data. The AI practitioner wants to ensure that the custom model does not generate inference responses based on confidential data.
How should the AI practitioner prevent responses based on confidential data?

  • A. Encrypt the confidential data in the inference responses by using Amazon SageMaker.
  • B. Delete the custom model. Remove the confidential data from the training dataset. Retrain the custom model.
  • C. Mask the confidential data in the inference responses by using dynamic data masking.
  • D. Encrypt the confidential data in the custom model by using AWS Key Management Service (AWS KMS).

Answer: B


NEW QUESTION # 21
Which functionality does Amazon SageMaker Clarify provide?

  • A. Identifies potential bias during data preparation
  • B. Integrates a Retrieval Augmented Generation (RAG) workflow
  • C. Monitors the quality of ML models in production
  • D. Documents critical details about ML models

Answer: A


NEW QUESTION # 22
A company wants to build an ML model by using Amazon SageMaker. The company needs to share and manage variables for model development across multiple teams.
Which SageMaker feature meets these requirements?

  • A. Amazon SageMaker Clarify
  • B. Amazon SageMaker Model Cards
  • C. Amazon SageMaker Feature Store
  • D. Amazon SageMaker Data Wrangler

Answer: C


NEW QUESTION # 23
A company is building an ML model to analyze archived data. The company must perform inference on large datasets that are multiple GBs in size. The company does not need to access the model predictions immediately.
Which Amazon SageMaker inference option will meet these requirements?

  • A. Asynchronous inference
  • B. Real-time inference
  • C. Serverless inference
  • D. Batch transform

Answer: D

Explanation:
I'll continue with more questions shortly. Stay tuned!


NEW QUESTION # 24
A company is using Amazon SageMaker Studio notebooks to build and train ML models. The company stores the data in an Amazon S3 bucket. The company needs to manage the flow of data from Amazon S3 to SageMaker Studio notebooks.
Which solution will meet this requirement?

  • A. Use Amazon Macie to monitor SageMaker Studio.
  • B. Use Amazon Inspector to monitor SageMaker Studio.
  • C. Configure SageMaker to use a VPC with an S3 endpoint.
  • D. Configure SageMaker to use S3 Glacier Deep Archive.

Answer: C


NEW QUESTION # 25
......

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