FREE PDF QUIZ 2025 EFFICIENT AMAZON AIF-C01 PASS GUARANTEED

Free PDF Quiz 2025 Efficient Amazon AIF-C01 Pass Guaranteed

Free PDF Quiz 2025 Efficient Amazon AIF-C01 Pass Guaranteed

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Amazon AWS Certified AI Practitioner Sample Questions (Q21-Q26):

NEW QUESTION # 21
What are tokens in the context of generative AI models?

  • A. Tokens are the specific prompts or instructions given to a generative AI model to generate output.
  • B. Tokens are the pre-trained weights of a generative AI model that are fine-tuned for specific tasks.
  • C. Tokens are the mathematical representations of words or concepts used in generative AI models.
  • D. Tokens are the basic units of input and output that a generative AI model operates on, representing words, subwords, or other linguistic units.

Answer: D

Explanation:
Tokens in generative AI models are the smallest units that the model processes, typically representing words, subwords, or characters. They are essential for the model to understand and generate language, breaking down text into manageable parts for processing.
* Option A (Correct): "Tokens are the basic units of input and output that a generative AI model operates on, representing words, subwords, or other linguistic units": This is the correct definition of tokens in the context of generative AI models.
* Option B: "Mathematical representations of words" describes embeddings, not tokens.
* Option C: "Pre-trained weights of a model" refers to the parameters of a model, not tokens.
* Option D: "Prompts or instructions given to a model" refers to the queries or commands provided to a model, not tokens.
AWS AI Practitioner References:
* Understanding Tokens in NLP: AWS provides detailed explanations of how tokens are used in natural language processing tasks by AI models, such as in Amazon Comprehend and other AWS AI services.


NEW QUESTION # 22
A company wants to create a chatbot by using a foundation model (FM) on Amazon Bedrock. The FM needs to access encrypted data that is stored in an Amazon S3 bucket.
The data is encrypted with Amazon S3 managed keys (SSE-S3).
The FM encounters a failure when attempting to access the S3 bucket data.
Which solution will meet these requirements?

  • A. Set the access permissions for the S3 buckets to allow public access to enable access over the internet.
  • B. Ensure that the role that Amazon Bedrock assumes has permission to decrypt data with the correct encryption key.
  • C. Ensure that the S3 data does not contain sensitive information.
  • D. Use prompt engineering techniques to tell the model to look for information in Amazon S3.

Answer: B

Explanation:
Amazon Bedrock needs the appropriate IAM role with permission to access and decrypt data stored in Amazon S3. If the data is encrypted with Amazon S3 managed keys (SSE-S3), the role that Amazon Bedrock assumes must have the required permissions to access and decrypt the encrypted data.
* Option A (Correct): "Ensure that the role that Amazon Bedrock assumes has permission to decrypt data with the correct encryption key": This is the correct solution as it ensures that the AI model can access the encrypted data securely without changing the encryption settings or compromising data security.
* Option B: "Set the access permissions for the S3 buckets to allow public access" is incorrect because it violates security best practices by exposing sensitive data to the public.
* Option C: "Use prompt engineering techniques to tell the model to look for information in Amazon S3" is incorrect as it does not address the encryption and permission issue.
* Option D: "Ensure that the S3 data does not contain sensitive information" is incorrect because it does not solve the access problem related to encryption.
AWS AI Practitioner References:
* Managing Access to Encrypted Data in AWS: AWS recommends using proper IAM roles and policies to control access to encrypted data stored in S3.


NEW QUESTION # 23
A company is using an Amazon Bedrock base model to summarize documents for an internal use case. The company trained a custom model to improve the summarization quality.
Which action must the company take to use the custom model through Amazon Bedrock?

  • A. Deploy the custom model in an Amazon SageMaker endpoint for real-time inference.
  • B. Grant access to the custom model in Amazon Bedrock.
  • C. Purchase Provisioned Throughput for the custom model.
  • D. Register the model with the Amazon SageMaker Model Registry.

Answer: A

Explanation:
To use a custom model that has been trained to improve summarization quality, the company must deploy the model on an Amazon SageMaker endpoint. This allows the model to be used for real-time inference through Amazon Bedrock or other AWS services. By deploying the model in SageMaker, the custom model can be accessed programmatically via API calls, enabling integration with Amazon Bedrock.
* Option B (Correct): "Deploy the custom model in an Amazon SageMaker endpoint for real-time inference": This is the correct answer because deploying the model on SageMaker enables it to serve real-time predictions and be integrated with Amazon Bedrock.
* Option A: "Purchase Provisioned Throughput for the custom model" is incorrect because provisioned throughput is related to database or storage services, not model deployment.
* Option C: "Register the model with the Amazon SageMaker Model Registry" is incorrect because while the model registry helps with model management, it does not make the model accessible for real- time inference.
* Option D: "Grant access to the custom model in Amazon Bedrock" is incorrect because Bedrock does not directly manage custom model access; it relies on deployed endpoints like those in SageMaker.
AWS AI Practitioner References:
* Amazon SageMaker Endpoints: AWS recommends deploying models to SageMaker endpoints to use them for real-time inference in various applications.


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 Inspector to monitor SageMaker Studio.
  • B. Use Amazon Macie 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
A company wants to use generative AI to increase developer productivity and software development. The company wants to use Amazon Q Developer.
What can Amazon Q Developer do to help the company meet these requirements?

  • A. Run an application without provisioning or managing servers.
  • B. Convert audio files to text documents by using ML models.
  • C. Enable voice commands for coding and providing natural language search.
  • D. Create software snippets, reference tracking, and open-source license tracking.

Answer: D

Explanation:
Amazon Q Developer is a tool designed to assist developers in increasing productivity by generating code snippets, managing reference tracking, and handling open-source license tracking. These features help developers by automating parts of the software development process.
* Option A (Correct): "Create software snippets, reference tracking, and open-source license tracking": This is the correct answer because these are key features that help developers streamline and automate tasks, thus improving productivity.
* Option B: "Run an application without provisioning or managing servers" is incorrect as it refers to AWS Lambda or AWS Fargate, not Amazon Q Developer.
* Option C: "Enable voice commands for coding and providing natural language search" is incorrect because this is not a function of Amazon Q Developer.
* Option D: "Convert audio files to text documents by using ML models" is incorrect as this refers to Amazon Transcribe, not Amazon Q Developer.
AWS AI Practitioner References:
* Amazon Q Developer Features: AWS documentation outlines how Amazon Q Developer supports developers by offering features that reduce manual effort and improve efficiency.


NEW QUESTION # 26
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