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The AWS Certified AI Practitioner (AIF-C01) practice questions (desktop and web-based) are customizable, meaning users can set the questions and time according to their needs to improve their discipline and feel the real-based exam scenario to pass the Amazon AIF-C01 Certification. Customizable mock tests comprehensively and accurately represent the actual AWS Certified AI Practitioner (AIF-C01) certification exam scenario.

Amazon AWS Certified AI Practitioner Sample Questions (Q55-Q60):

NEW QUESTION # 55
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. Enable voice commands for coding and providing natural language search.
B. Convert audio files to text documents by using ML models.
C. Create software snippets, reference tracking, and open-source license tracking.
D. Run an application without provisioning or managing servers.

Answer: C

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 # 56
A manufacturing company uses AI to inspect products and find any damages or defects.
Which type of AI application is the company using?

A. Natural language processing (NLP)
B. Recommendation system
C. Computer vision
D. Image processing

Answer: C

Explanation:
The manufacturing company uses AI to inspect products for damages or defects, which involves analyzing visual data (e.g., images or videos of products). This task falls under computer vision, a type of AI application that enables machines to interpret and understand visual information, such as identifying defects in manufacturing.
Exact Extract from AWS AI Documents:
From the AWS AI Practitioner Learning Path:
"Computer vision enables machines to interpret and understand visual data from the world, such as images or videos. Common applications include defect detection in manufacturing, where AI models analyze product images to identify damages or anomalies." (Source: AWS AI Practitioner Learning Path, Module on AI Concepts) Detailed Explanation:
* Option A: Recommendation systemRecommendation systems suggest items or actions based on user preferences (e.g., product recommendations). They are not relevant for inspecting products for defects.
* Option B: Natural language processing (NLP)NLP focuses on processing and understanding text or speech, not visual data like product images. This option is incorrect.
* Option C: Computer visionThis is the correct answer. Computer vision is used for tasks like defect detection in manufacturing by analyzing visual data to identify damages or defects.
* Option D: Image processingWhile image processing involves manipulating images (e.g., filtering, resizing), it is a lower-level technique, not an AI application. Computer vision, which often uses image processing as a component, is the broader AI application here.
References:
AWS AI Practitioner Learning Path: Module on AI Concepts
Amazon Rekognition Developer Guide: Image Analysis (https://docs.aws.amazon.com/rekognition/latest/dg
/what-is.html)
AWS Documentation: Introduction to Computer Vision (https://aws.amazon.com/computer-vision/)

NEW QUESTION # 57
An education provider is building a question and answer application that uses a generative AI model to explain complex concepts. The education provider wants to automatically change the style of the model response depending on who is asking the question. The education provider will give the model the age range of the user who has asked the question.
Which solution meets these requirements with the LEAST implementation effort?

A. Add a role description to the prompt context that instructs the model of the age range that the response should target.
B. Summarize the response text depending on the age of the user so that younger users receive shorter responses.
C. Use chain-of-thought reasoning to deduce the correct style and complexity for a response suitable for that user.
D. Fine-tune the model by using additional training data that is representative of the various age ranges that the application will support.

Answer: A

Explanation:
Adding a role description to the prompt context is a straightforward way to instruct the generative AI model to adjust its response style based on the user's age range. This method requires minimal implementation effort as it does not involve additional training or complex logic.
Option B (Correct): "Add a role description to the prompt context that instructs the model of the age range that the response should target": This is the correct answer because it involves the least implementation effort while effectively guiding the model to tailor responses according to the age range.
Option A: "Fine-tune the model by using additional training data" is incorrect because it requires significant effort in gathering data and retraining the model.
Option C: "Use chain-of-thought reasoning" is incorrect as it involves complex reasoning that may not directly address the need to adjust response style based on age.
Option D: "Summarize the response text depending on the age of the user" is incorrect because it involves additional processing steps after generating the initial response, increasing complexity.
AWS AI Practitioner Reference:
Prompt Engineering Techniques on AWS: AWS recommends using prompt context effectively to guide generative models in providing tailored responses based on specific user attributes.

NEW QUESTION # 58
A company wants to fine-tune an ML model that is hosted on Amazon Bedrock. The company wants to use its own sensitive data that is stored in private databases in a VPC. The data needs to stay within the company's private network.
Which solution will meet these requirements?

A. Use AWS PrivateLink to connect the VPC and Amazon Bedrock.
B. Restrict access to Amazon Bedrock by using an AWS Identity and Access Management (IAM) resource policy.
C. Restrict access to Amazon Bedrock by using an AWS Identity and Access Management (IAM) service role.
D. Use AWS Key Management Service (AWS KMS) keys to encrypt the data.

Answer: A

Explanation:
The company wants to fine-tune an ML model on Amazon Bedrock using sensitive data stored in private databases within a VPC, ensuring the data remains within its private network. AWS PrivateLink provides a secure, private connection between a VPC and AWS services like Amazon Bedrock, allowing data to stay within the company's network without traversing the public internet. This meets the requirement for maintaining data privacy during fine-tuning.
Exact Extract from AWS AI Documents:
From the AWS Bedrock User Guide:
"AWS PrivateLink enables you to securely connect your VPC to Amazon Bedrock without exposing data to the public internet. This is particularly useful for fine-tuning models with sensitive data, as it ensures that data remains within your private network." (Source: AWS Bedrock User Guide, Security and Networking) Detailed Option A: Restrict access to Amazon Bedrock by using an AWS Identity and Access Management (IAM) service role.While IAM service roles control access to Amazon Bedrock, they do not address the requirement of keeping data within the private network during data transfer. This option is insufficient.
Option B: Restrict access to Amazon Bedrock by using an AWS Identity and Access Management (IAM) resource policy.IAM resource policies define permissions for Bedrock resources but do not ensure that data stays within the private network. This option is incorrect.
Option C: Use AWS PrivateLink to connect the VPC and Amazon Bedrock.This is the correct answer. AWS PrivateLink creates a secure, private connection between the VPC and Amazon Bedrock, ensuring that sensitive data does not leave the private network during fine-tuning, as required.
Option D: Use AWS Key Management Service (AWS KMS) keys to encrypt the data.While AWS KMS can encrypt data, encryption alone does not guarantee that data remains within the private network during transfer. This option does not fully meet the requirement.
Reference:
AWS Bedrock User Guide: Security and Networking (https://docs.aws.amazon.com/bedrock/latest/userguide/security.html) AWS Documentation: AWS PrivateLink (https://aws.amazon.com/privatelink/) AWS AI Practitioner Learning Path: Module on Security and Networking for AI/ML Services

NEW QUESTION # 59
What are tokens in the context of generative AI models?

A. Tokens are the pre-trained weights of a generative AI model that are fine-tuned for specific tasks.
B. Tokens are the mathematical representations of words or concepts used in generative AI models.
C. Tokens are the specific prompts or instructions given to a generative AI model to generate output.
D. Tokens are the basic units of input and output that a generative AI model operates on, representing words, subwords, or other linguistic units.

Answer: D

NEW QUESTION # 60
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