AIF-C01 Test Answers - AIF-C01 Latest Practice Questions

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Amazon AIF-C01 Exam Syllabus Topics:

Topic	Details
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 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	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 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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AIF-C01 Latest Practice Questions, AIF-C01 Exam Cram

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Amazon AWS Certified AI Practitioner Sample Questions (Q150-Q155):

NEW QUESTION # 150
Which AW5 service makes foundation models (FMs) available to help users build and scale generative AI applications?

A. Amazon Q Developer
B. Amazon Bedrock
C. Amazon Comprehend
D. Amazon Kendra

Answer: B

Explanation:
Amazon Bedrock is a fully managed service that provides access to foundation models (FMs) from various providers, enabling users to build and scale generative AI applications. It simplifies the process of integrating FMs into applications for tasks like text generation, chatbots, and more.
Exact Extract from AWS AI Documents:
From the AWS Bedrock User Guide:
"Amazon Bedrock is a fully managed service that makes foundation models (FMs) from leading AI providers available through a single API, enabling developers to build and scale generative AI applications with ease." (Source: AWS Bedrock User Guide, Introduction to Amazon Bedrock) Detailed Explanation:
* Option A: Amazon Q DeveloperAmazon Q Developer is an AI-powered assistant for coding and AWS service guidance, not a service for hosting or providing foundation models.
* Option B: Amazon BedrockThis is the correct answer. Amazon Bedrock provides access to foundation models, making it the primary service for building and scaling generative AI applications.
* Option C: Amazon KendraAmazon Kendra is an intelligent search service powered by machine learning, not a service for providing foundation models or building generative AI applications.
* Option D: Amazon ComprehendAmazon Comprehend is an NLP service for text analysis tasks like sentiment analysis, not for providing foundation models or supporting generative AI.
References:
AWS Bedrock User Guide: Introduction to Amazon Bedrock (https://docs.aws.amazon.com/bedrock/latest
/userguide/what-is-bedrock.html)
AWS AI Practitioner Learning Path: Module on Generative AI Services
AWS Documentation: Generative AI on AWS (https://aws.amazon.com/generative-ai/)

NEW QUESTION # 151
A company wants to use generative AI to increase developer productivity and software development. The company wants to use Amazon Q Developer.
What can Amazon Q Developer do to help the company meet these requirements?

A. Run an application without provisioning or managing servers.
B. Create software snippets, reference tracking, and open-source license tracking.
C. Enable voice commands for coding and providing natural language search.
D. Convert audio files to text documents by using ML models.

Answer: B

Explanation:
Amazon Q Developer is a tool designed to assist developers in increasing productivity by generating code snippets, managing reference tracking, and handling open-source license tracking. These features help developers by automating parts of the software development process.
* Option A (Correct): "Create software snippets, reference tracking, and open-source license tracking": This is the correct answer because these are key features that help developers streamline and automate tasks, thus improving productivity.
* Option B: "Run an application without provisioning or managing servers" is incorrect as it refers to AWS Lambda or AWS Fargate, not Amazon Q Developer.
* Option C: "Enable voice commands for coding and providing natural language search" is incorrect because this is not a function of Amazon Q Developer.
* Option D: "Convert audio files to text documents by using ML models" is incorrect as this refers to Amazon Transcribe, not Amazon Q Developer.
AWS AI Practitioner References:
* Amazon Q Developer Features: AWS documentation outlines how Amazon Q Developer supports developers by offering features that reduce manual effort and improve efficiency.

NEW QUESTION # 152
A company is using a large language model (LLM) on Amazon Bedrock to build a chatbot. The chatbot processes customer support requests. To resolve a request, the customer and the chatbot must interact a few times.
Which solution gives the LLM the ability to use content from previous customer messages?

A. Turn on model invocation logging to collect messages.
B. Add messages to the model prompt.
C. Use Provisioned Throughput for the LLM.
D. Use Amazon Personalize to save conversation history.

Answer: B

Explanation:
The company is building a chatbot using an LLM on Amazon Bedrock, and the chatbot needs to use content from previous customer messages to resolve requests. Adding previous messages to the model prompt (also known as providing conversation history) enables the LLM to maintain context across interactions, allowing it to respond coherently based on the ongoing conversation.
Exact Extract from AWS AI Documents:
From the AWS Bedrock User Guide:
"To enable a large language model (LLM) to maintain context in a conversation, you can include previous messages in the model prompt. This approach, often referred to as providing conversation history, allows the LLM to generate responses that are contextually relevant toprior interactions." (Source: AWS Bedrock User Guide, Building Conversational Applications) Detailed Option A: Turn on model invocation logging to collect messages.Model invocation logging records interactions for auditing or debugging but does not provide the LLM with access to previous messages during inference to maintain conversation context.
Option B: Add messages to the model prompt.This is the correct answer. Including previous messages in the prompt gives the LLM the conversation history it needs to respond appropriately, a common practice for chatbots on Amazon Bedrock.
Option C: Use Amazon Personalize to save conversation history.Amazon Personalize is for building recommendation systems, not for managing conversation history in a chatbot. This option is irrelevant.
Option D: Use Provisioned Throughput for the LLM.Provisioned Throughput in Amazon Bedrock ensures consistent performance for model inference but does not address the need to use previous messages in the conversation.
Reference:
AWS Bedrock User Guide: Building Conversational Applications (https://docs.aws.amazon.com/bedrock/latest/userguide/conversational-apps.html) AWS AI Practitioner Learning Path: Module on Generative AI and Chatbots Amazon Bedrock Developer Guide: Managing Conversation Context (https://aws.amazon.com/bedrock/)

NEW QUESTION # 153
A company wants to improve the accuracy of the responses from a generative AI application. The application uses a foundation model (FM) on Amazon Bedrock.
Which solution meets these requirements MOST cost-effectively?

A. Fine-tune the FM.
B. Train a new FM.
C. Retrain the FM.
D. Use prompt engineering.

Answer: D

Explanation:
The company wants to improve the accuracy of a generative AI application using a foundation model (FM) on Amazon Bedrock in the most cost-effective way. Prompt engineering involves optimizing the input prompts to guide the FM to produce more accurate responses without modifying the model itself. This approach is cost- effective because it does not require additional computational resources or training, unlike fine-tuning or retraining.
Exact Extract from AWS AI Documents:
From the AWS Bedrock User Guide:
"Prompt engineering is a cost-effective technique to improve the performance of foundation models. By crafting precise and context-rich prompts, users can guide the model to generate more accurate and relevant responses without the need for fine-tuning or retraining." (Source: AWS Bedrock User Guide, Prompt Engineering for Foundation Models) Detailed Explanation:
* Option A: Fine-tune the FM.Fine-tuning involves retraining the FM on a custom dataset, which requirescomputational resources, time, and cost (e.g., for Amazon Bedrock fine-tuning jobs). It is not the most cost-effective solution.
* Option B: Retrain the FM.Retraining an FM from scratch is highly resource-intensive and expensive, as it requires large datasets and significant compute power. This is not cost-effective.
* Option C: Train a new FM.Training a new FM is the most expensive option, as it involves building a model from the ground up, requiring extensive data, compute resources, and expertise. This is not cost- effective.
* Option D: Use prompt engineering.This is the correct answer. Prompt engineering adjusts the input prompts to improve the FM's responses without incurring additional compute costs, making it the most cost-effective solution for improving accuracy on Amazon Bedrock.
References:
AWS Bedrock User Guide: Prompt Engineering for Foundation Models (https://docs.aws.amazon.com
/bedrock/latest/userguide/prompt-engineering.html)
AWS AI Practitioner Learning Path: Module on Generative AI Optimization Amazon Bedrock Developer Guide: Cost Optimization for Generative AI (https://aws.amazon.com/bedrock/)

NEW QUESTION # 154
A company uses Amazon SageMaker for its ML pipeline in a production environment. The company has large input data sizes up to 1 GB and processing times up to 1 hour. The company needs near real-time latency.
Which SageMaker inference option meets these requirements?

A. Real-time inference
B. Serverless inference
C. Batch transform
D. Asynchronous inference

Answer: A

Explanation:
Real-time inference is designed to provide immediate, low-latency predictions, which is necessary when the company requires near real-time latency for its ML models. This option is optimal when there is a need for fast responses, even with large input data sizes and substantial processing times.
Option A (Correct): "Real-time inference": This is the correct answer because it supports low-latency requirements, which are essential for real-time applications where quick response times are needed.
Option B: "Serverless inference" is incorrect because it is more suited for intermittent, small-scale inference workloads, not for continuous, large-scale, low-latency needs.
Option C: "Asynchronous inference" is incorrect because it is used for workloads that do not require immediate responses.
Option D: "Batch transform" is incorrect as it is intended for offline, large-batch processing where immediate response is not necessary.
AWS AI Practitioner Reference:
Amazon SageMaker Inference Options: AWS documentation describes real-time inference as the best solution for applications that require immediate prediction results with low latency.

NEW QUESTION # 155
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AIF-C01 TEST ANSWERS - AIF-C01 LATEST PRACTICE QUESTIONS

AIF-C01 Test Answers - AIF-C01 Latest Practice Questions