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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
  • 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.
Topic 3
  • 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 4
  • 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 5
  • 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.

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Amazon AWS Certified AI Practitioner Sample Questions (Q50-Q55):

NEW QUESTION # 50
A company is using Amazon Bedrock Agents to build an application to automate business workflows.

Answer: B

Explanation:
The correct answer is D. Amazon Bedrock Agents are used to orchestrate and execute complex workflows by connecting foundation models with APIs, databases, and tools. According to AWS documentation, agents interpret user inputs, plan the necessary steps, call external APIs or systems, and return structured results. This allows the model to go beyond text generation into full automation workflows-such as booking tasks, querying internal systems, or summarizing reports. Option A describes multi-modal models, B refers to prompt tuning, and C misstates control delegation; agents act autonomously based on model reasoning. Thus, Bedrock Agents function as intelligent orchestrators, handling multi-step task execution through integrated tool use.
Referenced AWS AI/ML Documents and Study Guides:
Amazon Bedrock Developer Guide - Agents Overview
AWS Generative AI Best Practices - Workflow Orchestration


NEW QUESTION # 51
A company has multiple datasets that contain historical data. The company wants to use ML technologies to process each dataset.
Select the correct ML technology from the following list for each dataset. Select each ML technology one time or not at all. (Select THREE.) Computer vision Natural language processing (NLP) Reinforcement learning Time series forecasting

Answer:

Explanation:
Dataset 1: A dataset that contains text-based customer reviews # Natural language processing (NLP) NLP is designed for analyzing text (sentiment analysis, text classification, etc.).
Dataset 2: A dataset that contains images of animals labeled with their species names # Computer vision Computer vision models classify or detect objects in images.
Dataset 3: A dataset that contains daily sales volumes for products # Time series forecasting Time series forecasting predicts future values based on historical sequential data (like sales, demand, stock prices).


NEW QUESTION # 52
A company wants to create an application by using Amazon Bedrock. The company has a limited budget and prefers flexibility without long-term commitment.
Which Amazon Bedrock pricing model meets these requirements?

Answer: C


NEW QUESTION # 53
A social media company wants to use a large language model (LLM) to summarize messages. The company has chosen a few LLMs that are available on Amazon SageMaker JumpStart. The company wants to compare the generated output toxicity of these models.
Which strategy gives the company the ability to evaluate the LLMs with the LEAST operational overhead?

Answer: A

Explanation:
The least operational overhead comes from automated tools that can scan and evaluate LLM outputs for toxicity. AWS and SageMaker JumpStart support integrations with automatic evaluation tools and APIs (such as Amazon Comprehend or third-party toxicity classifiers).
B is correct: Automated evaluation provides quick, scalable, and repeatable analysis, requiring minimal human intervention.
A and C require manual effort, increasing operational overhead.
D (RLHF) is resource-intensive and not designed for rapid, automated model comparison.
"Automated evaluation can quickly assess generated text for specific attributes like toxicity, sentiment, or compliance using pre-trained classifiers, reducing human involvement and operational complexity." (Reference: AWS SageMaker JumpStart Evaluation, AWS AI Practitioner Guide)
"Automated evaluation can quickly assess generated text for specific attributes like toxicity, sentiment, or compliance using pre-trained classifiers, reducing human involvement and operational complexity." (Reference: AWS SageMaker JumpStart Evaluation, AWS AI Practitioner Guide)


NEW QUESTION # 54
A company has guidelines for data storage and deletion.
Which data governance strategy does this describe?

Answer: A

Explanation:
Comprehensive and Detailed Explanation From Exact AWS AI documents:
Data retention policies define:
* How long data is stored
* When data must be deleted
* How data lifecycle requirements are enforced
AWS data governance guidance identifies data retention as a core compliance and governance strategy.
Why the other options are incorrect:
* De-identification (A) removes personal identifiers.
* Data quality (B) focuses on accuracy and completeness.
* Log storage (D) is a technical logging mechanism.
AWS AI document references:
* AWS Data Governance Best Practices
* Managing Data Lifecycles
* Compliance and Retention Policies


NEW QUESTION # 55
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