FREE PDF QUIZ 2025 UPDATED AIF-C01: AWS CERTIFIED AI PRACTITIONER REAL EXAM ANSWERS

Free PDF Quiz 2025 Updated AIF-C01: AWS Certified AI Practitioner Real Exam Answers

Free PDF Quiz 2025 Updated AIF-C01: AWS Certified AI Practitioner Real Exam Answers

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Amazon AIF-C01 Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 2
  • 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 3
  • 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 4
  • 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 5
  • 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.

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The top personal and professional Amazon AIF-C01 certification exam benefits are recognition of skills, updated knowledge, more career opportunities, instant promotion, and increase in salary, etc. If your answer is yes first of all you have to enroll in the AWS Certified AI Practitioner (AIF-C01) certification exam and put all your efforts to pass this career advancement certification exam. Are you looking for the right and recommended way to pass the Amazon AIF-C01 exam?

Amazon AWS Certified AI Practitioner Sample Questions (Q36-Q41):

NEW QUESTION # 36
A pharmaceutical company wants to analyze user reviews of new medications and provide a concise overview for each medication. Which solution meets these requirements?

  • A. Create a classification model that categorizes medications into different groups by using Amazon SageMaker.
  • B. Create medication review summaries by using Amazon Bedrock large language models (LLMs).
  • C. Create medication review summaries by using Amazon Rekognition.
  • D. Create a time-series forecasting model to analyze the medication reviews by using Amazon Personalize.

Answer: B

Explanation:
Amazon Bedrock provides large language models (LLMs) that are optimized for natural language understanding and text summarization tasks, making it the best choice for creating concise summaries of user reviews. Time-series forecasting, classification, and image analysis (Rekognition) are not suitable for summarizing textual data. References: AWS Bedrock Documentation.


NEW QUESTION # 37
Which functionality does Amazon SageMaker Clarify provide?

  • A. Identifies potential bias during data preparation
  • B. Integrates a Retrieval Augmented Generation (RAG) workflow
  • C. Documents critical details about ML models
  • D. Monitors the quality of ML models in production

Answer: A

Explanation:
Exploratory data analysis (EDA) involves understanding the data by visualizing it, calculating statistics, and creating correlation matrices. This stage helps identify patterns, relationships, and anomalies in the data, which can guide further steps in the ML pipeline.
Option C (Correct): "Exploratory data analysis": This is the correct answer as the tasks described (correlation matrix, calculating statistics, visualizing data) are all part of the EDA process.
Option A: "Data pre-processing" is incorrect because it involves cleaning and transforming data, not initial analysis.
Option B: "Feature engineering" is incorrect because it involves creating new features from raw data, not analyzing the data's existing structure.
Option D: "Hyperparameter tuning" is incorrect because it refers to optimizing model parameters, not analyzing the data.
AWS AI Practitioner Reference:
Stages of the Machine Learning Pipeline: AWS outlines EDA as the initial phase of understanding and exploring data before moving to more specific preprocessing, feature engineering, and model training stages.


NEW QUESTION # 38
A company wants to enhance response quality for a large language model (LLM) for complex problem- solving tasks. The tasks require detailed reasoning and a step-by-step explanation process.
Which prompt engineering technique meets these requirements?

  • A. Zero-shot prompting
  • B. Chain-of-thought prompting
  • C. Few-shot prompting
  • D. Directional stimulus prompting

Answer: B

Explanation:
The company wants to enhance the response quality of an LLM for complex problem-solving tasks requiring detailed reasoning and step-by-step explanations. Chain-of-thought prompting encourages the LLM to break down the problem into intermediate steps, providing a clear reasoning process before arriving at the final answer, which is ideal for this requirement.
Exact Extract from AWS AI Documents:
From the AWS Bedrock User Guide:
"Chain-of-thought prompting improves the reasoning capabilities of large language models by encouraging them to break down complex tasks into intermediate steps, providing a step-by-step explanation that leads to the final answer. This technique is particularly effective for problem-solving tasks requiring detailed reasoning." (Source: AWS Bedrock User Guide, Prompt Engineering Techniques) Detailed Explanation:
* Option A: Few-shot promptingFew-shot prompting provides a few examples to guide the LLM but does not explicitly encourage step-by-step reasoning or detailed explanations.
* Option B: Zero-shot promptingZero-shot prompting relies on the LLM's pre-trained knowledge without examples, making it less effective for complex tasks requiring detailed reasoning.
* Option C: Directional stimulus promptingDirectional stimulus prompting is not a standard technique in AWS documentation, likely a distractor, and does not address step-by-step reasoning.
* Option D: Chain-of-thought promptingThis is the correct answer. Chain-of-thought prompting enhances response quality for complex tasks by guiding the LLM to reason step-by-step, providing detailed explanations.
References:
AWS Bedrock User Guide: Prompt Engineering Techniques (https://docs.aws.amazon.com/bedrock/latest
/userguide/prompt-engineering.html)
AWS AI Practitioner Learning Path: Module on Generative AI Prompting
Amazon Bedrock Developer Guide: Advanced Prompting Strategies (https://aws.amazon.com/bedrock/)


NEW QUESTION # 39
A company has a foundation model (FM) that was customized by using Amazon Bedrock to answer customer queries about products. The company wants to validate the model's responses to new types of queries. The company needs to upload a new dataset that Amazon Bedrock can use for validation.
Which AWS service meets these requirements?

  • A. Amazon Elastic Block Store (Amazon EBS)
  • B. Amazon S3
  • C. AWS Snowcone
  • D. Amazon Elastic File System (Amazon EFS)

Answer: B

Explanation:
I'll continue to format the remaining questions in the same format. Stay tuned!


NEW QUESTION # 40
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. Use prompt engineering to add all the PDF files as context to the user prompt when the prompt is submitted to Amazon Bedrock.
  • B. Use prompt engineering to add one PDF file as context to the user prompt when the prompt is submitted to Amazon Bedrock.
  • C. Upload PDF documents to an Amazon Bedrock knowledge base. Use the knowledge base to provide context when users submit prompts 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: B

Explanation:
Using Amazon Bedrock with large language models (LLMs) allows for efficient utilization of AI to answer queries based on context provided in product manuals. To achieve this cost-effectively, the company should avoid unnecessary use of resources.
Option A (Correct): "Use prompt engineering to add one PDF file as context to the user prompt when the prompt is submitted to Amazon Bedrock": This is the most cost-effective solution. By using prompt engineering, only the relevant content from one PDF file is added as context to each query. This approach minimizes the amount of data processed, which helps in reducing costs associated with LLMs' computational requirements.
Option B: "Use prompt engineering to add all the PDF files as context to the user prompt when the prompt is submitted to Amazon Bedrock" is incorrect. Including all PDF files would increase costs significantly due to the large context size processed by the model.
Option C: "Use all the PDF documents to fine-tune a model with Amazon Bedrock" is incorrect. Fine-tuning a model is more expensive than using prompt engineering, especially if done for multiple documents.
Option D: "Upload PDF documents to an Amazon Bedrock knowledge base" is incorrect because Amazon Bedrock does not have a built-in knowledge base feature for directly managing and querying PDF documents.
AWS AI Practitioner Reference:
Prompt Engineering for Cost-Effective AI: AWS emphasizes the importance of using prompt engineering to minimize costs when interacting with LLMs. By carefully selecting relevant context, users can reduce the amount of data processed and save on expenses.


NEW QUESTION # 41
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