100% PASS QUIZ 2025 AMAZON AIF-C01 UPDATED TEST SAMPLE ONLINE

100% Pass Quiz 2025 Amazon AIF-C01 Updated Test Sample Online

100% Pass Quiz 2025 Amazon AIF-C01 Updated Test Sample Online

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Amazon AWS Certified AI Practitioner Sample Questions (Q11-Q16):

NEW QUESTION # 11
A company is implementing the Amazon Titan foundation model (FM) by using Amazon Bedrock. The company needs to supplement the model by using relevant data from the company's private data sources.
Which solution will meet this requirement?

  • A. Enable model invocation logging
  • B. Use a different FM
  • C. Choose a lower temperature value
  • D. Create an Amazon Bedrock knowledge base

Answer: D

Explanation:
Creating an Amazon Bedrock knowledge base allows the integration of external or private data sources with a foundation model (FM) like Amazon Titan. This integration helps supplement the model with relevant data from the company's private data sources to enhance its responses.
* Option C (Correct): "Create an Amazon Bedrock knowledge base": This is the correct answer as it enables the company to incorporate private data into the FM to improve its effectiveness.
* Option A: "Use a different FM" is incorrect because it does not address the need to supplement the current model with private data.
* Option B: "Choose a lower temperature value" is incorrect as it affects output randomness, not the integration of private data.
* Option D: "Enable model invocation logging" is incorrect because logging does not help in supplementing the model with additional data.
AWS AI Practitioner References:
* Amazon Bedrock and Knowledge Integration: AWS explains how creating a knowledge base allows Amazon Bedrock to use external data sources to improve the FM's relevance and accuracy.


NEW QUESTION # 12
A company wants to develop a large language model (LLM) application by using Amazon Bedrock and customer data that is uploaded to Amazon S3. The company's security policy states that each team can access data for only the team's own customers.
Which solution will meet these requirements?

  • A. Create an Amazon Bedrock custom service role for each team that has access to only the team's customer data.
  • B. Create a custom service role that has Amazon S3 access. Ask teams to specify the customer name on each Amazon Bedrock request.
  • C. Redact personal data in Amazon S3. Update the S3 bucket policy to allow team access to customer data.
  • D. Create one Amazon Bedrock role that has full Amazon S3 access. Create IAM roles for each team that have access to only each team's customer folders.

Answer: A

Explanation:
To comply with the company's security policy, which restricts each team to access data for only their own customers, creating an Amazon Bedrock custom service role for each team is the correct solution.
* Custom Service Role Per Team:
* A custom service role for each team ensures that the access control is granular, allowing only specific teams to access their own customer data in Amazon S3.
* This setup aligns with the principle of least privilege, ensuring teams can only interact with data they are authorized to access.
* Why Option A is Correct:
* Access Control: Allows precise access permissions for each team's data.
* Security Compliance: Directly meets the company's security policy requirements by ensuring data segregation.
* Why Other Options are Incorrect:
* B. Custom service role with customer name specification: This approach is impractical as it relies on manual input, which is prone to errors and does not inherently enforce data access controls.
* C. Redacting personal data and updating S3 bucket policy: Redaction does not solve the requirement for team-specific access, and updating bucket policies is less granular than creating roles.
* D. One Bedrock role with full S3 access and IAM roles for teams: This setup does not meet the least privilege principle, as having a single role with full access is contrary to the company's security policy.
Thus, A is the correct answer to meet the company's security requirements.


NEW QUESTION # 13
An AI practitioner has a database of animal photos. The AI practitioner wants to automatically identify and categorize the animals in the photos without manual human effort.
Which strategy meets these requirements?

  • A. Named entity recognition
  • B. Object detection
  • C. Inpainting
  • D. Anomaly detection

Answer: B

Explanation:
Object detection is the correct strategy for automatically identifying and categorizing animals in photos.
* Object Detection:
* A computer vision technique that identifies and locates objects within an image and assigns them to predefined categories.
* Ideal for tasks such as identifying animals in photos, where the goal is to detect specific objects (animals) and categorize them accordingly.
* Why Option A is Correct:
* Automatic Identification: Object detection models can automatically identify different types of animals in the images without manual intervention.
* Categorization Capability: Assigns labels to detected objects, fulfilling the requirement for categorizing animals.
* Why Other Options are Incorrect:
* B. Anomaly detection: Identifies outliers or unusual patterns, not specific objects in images.
* C. Named entity recognition: Used in NLP to identify entities in text, not for image processing.
* D. Inpainting: Used for filling in missing parts of images, not for detecting or categorizing objects.


NEW QUESTION # 14
An AI practitioner is using a large language model (LLM) to create content for marketing campaigns. The generated content sounds plausible and factual but is incorrect.
Which problem is the LLM having?

  • A. Data leakage
  • B. Overfitting
  • C. Hallucination
  • D. Underfitting

Answer: C


NEW QUESTION # 15
Which option is a benefit of using Amazon SageMaker Model Cards to document AI models?

  • A. Physically storing models for archival purposes.
  • B. Reducing the overall computational requirements of a model.
  • C. Providing a visually appealing summary of a model's capabilities.
  • D. Standardizing information about a model's purpose, performance, and limitations.

Answer: D

Explanation:
Amazon SageMaker Model Cards provide a standardized way to document important details about an AI model, such as its purpose, performance, intended usage, and known limitations. This enables transparency and compliance while fostering better communication between stakeholders. It does not store models physically or optimize computational requirements. References: AWS SageMaker Model Cards Documentation.


NEW QUESTION # 16
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