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[General] Efficient AIF-C01 Exam Guide Materials & The Best Materials to help you pass

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【General】 Efficient AIF-C01 Exam Guide Materials & The Best Materials to help you pass

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Amazon AWS Certified AI Practitioner Sample Questions (Q131-Q136):NEW QUESTION # 131
A company trains image and text generation models on Amazon SageMaker AI. The company releases the models by using Amazon Bedrock. The company must retain a tamper-proof, queryable record of every API call from SageMaker AI, Amazon Bedrock, and AWS Identity and Access Management (IAM).
Which AWS service will meet these requirements?
  • A. AWS CloudTrail Lake
  • B. Amazon Inspector
  • C. Amazon Macie
  • D. AWS Trusted Advisor
Answer: A
Explanation:
Comprehensive and Detailed Explanation From Exact AWS AI documents:
AWS CloudTrail Lake provides:
* Immutable (tamper-proof) storage of API activity
* Advanced querying across multiple AWS services
* Long-term retention for audit and compliance
AWS governance guidance recommends CloudTrail Lake for centralized, queryable audit logs.
Why the other options are incorrect:
* Trusted Advisor (A) gives recommendations.
* Macie (B) discovers sensitive data.
* Inspector (D) performs vulnerability scans.
AWS AI document references:
* AWS CloudTrail Lake Overview
* Auditing AI Workloads on AWS
* Centralized Compliance Logging

NEW QUESTION # 132
HOTSPOT
Select the correct AI term from the following list for each statement. Each AI term should be selected one time. (Select THREE.)
* AI
* Deep learning
* ML

Answer:
Explanation:

Explanation:

Artificial Intelligence (AI) is the broad field focused on simulating human problem-solving and cognitive abilities, including reasoning, perception, and decision-making.
(Reference: AWS Certified AI Practitioner Official Study Guide)
Machine Learning (ML) is a subset of AI that uses data-driven algorithms to identify patterns and make predictions without explicit programming for each specific task.
(Reference: AWS Machine Learning Overview)
Deep learning is a subset of ML that uses neural networks with many layers (deep neural networks) to process complex data and extract high-level features.
(Reference: AWS Deep Learning on AWS)

NEW QUESTION # 133
An online learning company with large volumes of educational materials wants to use enterprise search.
Which AWS service meets these requirements?
  • A. Amazon Kendra
  • B. Amazon Comprehend
  • C. Amazon Personalize
  • D. Amazon Textract
Answer: A
Explanation:
Amazon Kendra is an enterprise search service that uses machine learning to index and search unstructured data such as documents, manuals, and course materials.
Amazon Comprehend performs NLP tasks like sentiment analysis and entity recognition, not enterprise search.
Amazon Textract extracts structured text from scanned docs and PDFs.
Amazon Personalize builds recommendation systems, not search engines.
# Reference:
AWS Documentation - Amazon Kendra

NEW QUESTION # 134
A company is building a large language model (LLM) question answering chatbot. The company wants to decrease the number of actions call center employees need to take to respond to customer questions.
Which business objective should the company use to evaluate the effect of the LLM chatbot?
  • A. Average call duration
  • B. Regulatory compliance
  • C. Website engagement rate
  • D. Corporate social responsibility
Answer: A

NEW QUESTION # 135
An AI practitioner is developing a prompt for large language models (LLMs) in Amazon Bedrock. The AI practitioner must ensure that the prompt works across all Amazon Bedrock LLMs.
Which characteristic can differ across the LLMs?
  • A. Maximum token count
  • B. Compatibility with Amazon Bedrock Guardrails
  • C. On-demand inference parameter support
  • D. The ability to control model output randomness
Answer: A
Explanation:
The correct answer is A because each foundation model on Amazon Bedrock (e.g., Claude, Titan, Mistral, Meta Llama) has a different maximum token limit, which defines the maximum number of tokens the model can accept in the prompt and generate in the response.
From AWS documentation:
"Each model in Amazon Bedrock has its own maximum token limit. Prompts exceeding the limit must be truncated or adjusted depending on the selected model." Explanation of other options:
B). On-demand inference support is a platform feature that is uniformly supported across models on Bedrock.
C). All Bedrock LLMs support randomness control through temperature and top-p parameters.
D). Amazon Bedrock Guardrails are designed to work across supported models, though specific behaviors may vary slightly.
Referenced AWS AI/ML Documents and Study Guides:
* Amazon Bedrock Model Comparison Guide
* AWS Prompt Engineering and LLM Deployment Documentation
* AWS ML Specialty Study Guide - Bedrock Model Capabilities

NEW QUESTION # 136
......
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