AMAZON AIF-C01 EXAM DUMPS IN PDF FORMAT

Amazon AIF-C01 Exam Dumps in PDF Format

Amazon AIF-C01 Exam Dumps in PDF Format

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Amazon AIF-C01 Exam Syllabus Topics:

TopicDetails
Topic 1
  • Guidelines for Responsible AI: This domain highlights the ethical considerations and best practices for deploying AI solutions responsibly, including ensuring fairness and transparency. It is aimed at AI practitioners, including data scientists and compliance officers, who are involved in the development and deployment of AI systems and need to adhere to ethical standards.
Topic 2
  • Applications of Foundation Models: This domain examines how foundation models, like large language models, are used in practical applications. It is designed for those who need to understand the real-world implementation of these models, including solution architects and data engineers who work with AI technologies to solve complex problems.
Topic 3
  • Fundamentals of Generative AI: This domain explores the basics of generative AI, focusing on techniques for creating new content from learned patterns, including text and image generation. It targets professionals interested in understanding generative models, such as developers and researchers in AI.
Topic 4
  • Security, Compliance, and Governance for AI Solutions: This domain covers the security measures, compliance requirements, and governance practices essential for managing AI solutions. It targets security professionals, compliance officers, and IT managers responsible for safeguarding AI systems, ensuring regulatory compliance, and implementing effective governance frameworks.
Topic 5
  • Fundamentals of AI and ML: This domain covers the fundamental concepts of artificial intelligence (AI) and machine learning (ML), including core algorithms and principles. It is aimed at individuals new to AI and ML, such as entry-level data scientists and IT professionals.

Amazon AWS Certified AI Practitioner Sample Questions (Q65-Q70):

NEW QUESTION # 65
A security company is using Amazon Bedrock to run foundation models (FMs). The company wants to ensure that only authorized users invoke the models. The company needs to identify any unauthorized access attempts to set appropriate AWS Identity and Access Management (IAM) policies and roles for future iterations of the FMs.
Which AWS service should the company use to identify unauthorized users that are trying to access Amazon Bedrock?

  • A. AWS Trusted Advisor
  • B. Amazon Fraud Detector
  • C. AWS CloudTrail
  • D. AWS Audit Manager

Answer: C

Explanation:
AWS CloudTrail is a service that enables governance, compliance, and operational and risk auditing of your AWS account. It tracks API calls and identifies unauthorized access attempts to AWS resources, including Amazon Bedrock.
* AWS CloudTrail:
* Provides detailed logs of all API calls made within an AWS account, including those to Amazon Bedrock.
* Can identify unauthorized access attempts by logging and monitoring the API calls, which helps in setting appropriate IAM policies and roles.
* Why Option B is Correct:
* Monitoring and Security: CloudTrail logs all access requests and helps detect unauthorized access attempts.
* Auditing and Compliance: The logs can be used to audit user activity and enforce security measures.
* Why Other Options are Incorrect:
* A. AWS Audit Manager: Used for automating audit preparation, not for tracking real-time unauthorized access attempts.
* C. Amazon Fraud Detector: Designed to detect fraudulent online activities, not unauthorized access to AWS services.
* D. AWS Trusted Advisor: Provides best practice recommendations for AWS resources, not access monitoring.
Thus, B is the correct answer for identifying unauthorized users attempting to access Amazon Bedrock.


NEW QUESTION # 66
A company needs to choose a model from Amazon Bedrock to use internally. The company must identify a model that generates responses in a style that the company's employees prefer.
What should the company do to meet these requirements?

  • A. Use public model leaderboards to identify the model.
  • B. Evaluate the models by using built-in prompt datasets.
  • C. Use the model InvocationLatency runtime metrics in Amazon CloudWatch when trying models.
  • D. Evaluate the models by using a human workforce and custom prompt datasets.

Answer: D

Explanation:
To determine which model generates responses in a style that the company's employees prefer, the best approach is to use a human workforce to evaluate the models with custom prompt datasets. This method allows for subjective evaluation based on the specific stylistic preferences of the company's employees, which cannot be effectively assessed through automated methods or pre-built datasets.
* Option B (Correct): "Evaluate the models by using a human workforce and custom prompt datasets": This is the correct answer as it directly involves human judgment to evaluate the style and quality of the responses, aligning with employee preferences.
* Option A: "Evaluate the models by using built-in prompt datasets" is incorrect because built-in datasets may not capture the company's specific stylistic requirements.
* Option C: "Use public model leaderboards to identify the model" is incorrect as leaderboards typically measure model performance on standard benchmarks, not on stylistic preferences.
* Option D: "Use the model InvocationLatency runtime metrics in Amazon CloudWatch" is incorrect because latency metrics do not provide any information about the style of the model's responses.
AWS AI Practitioner References:
* Model Evaluation Techniques on AWS: AWS suggests using human evaluators to assess qualitative aspects of model outputs, such as style and tone, to ensure alignment with organizational preferences


NEW QUESTION # 67
A company wants to use large language models (LLMs) with Amazon Bedrock to develop a chat interface for the company's product manuals. The manuals are stored as PDF files.
Which solution meets these requirements MOST cost-effectively?

  • A. Upload PDF documents to an Amazon Bedrock knowledge base. Use the knowledge base to provide context when users submit prompts to Amazon Bedrock.
  • B. Use prompt engineering to add all the PDF files as context to the user prompt when the prompt is submitted to Amazon Bedrock.
  • C. Use prompt engineering to add one PDF file as context to the user prompt when the prompt is submitted to Amazon Bedrock.
  • D. Use all the PDF documents to fine-tune a model with Amazon Bedrock. Use the fine-tuned model to process user prompts.

Answer: A


NEW QUESTION # 68
How can companies use large language models (LLMs) securely on Amazon Bedrock?

  • A. Enable AWS Audit Manager for automatic model evaluation jobs.
  • B. Design clear and specific prompts. Configure AWS Identity and Access Management (IAM) roles and policies by using least privilege access.
  • C. Use Amazon CloudWatch Logs to make models explainable and to monitor for bias.
  • D. Enable Amazon Bedrock automatic model evaluation jobs.

Answer: B


NEW QUESTION # 69
A digital devices company wants to predict customer demand for memory hardware. The company does not have coding experience or knowledge of ML algorithms and needs to develop a data-driven predictive model. The company needs to perform analysis on internal data and external data.
Which solution will meet these requirements?

  • A. Import the data into Amazon SageMaker Data Wrangler. Build ML models and demand forecast predictions by using an Amazon Personalize Trending-Now recipe.
  • B. Import the data into Amazon SageMaker Data Wrangler. Create ML models and demand forecast predictions by using SageMaker built-in algorithms.
  • C. Import the data into Amazon SageMaker Canvas. Build ML models and demand forecast predictions by selecting the values in the data from SageMaker Canvas.
  • D. Store the data in Amazon S3. Create ML models and demand forecast predictions by using Amazon SageMaker built-in algorithms that use the data from Amazon S3.

Answer: C

Explanation:
I'll continue to format the rest. Let me know if you would like me to provide them all in one go or in parts.


NEW QUESTION # 70
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