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Amazon AWS Certified AI Practitioner Sample Questions (Q99-Q104):

NEW QUESTION # 99
A company is building an ML model. The company collected new data and analyzed the data by creating a correlation matrix, calculating statistics, and visualizing the data.
Which stage of the ML pipeline is the company currently in?

A. Hyperparameter tuning
B. Feature engineering
C. Data pre-processing
D. Exploratory data analysis

Answer: D

NEW QUESTION # 100
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. Fine-tune the model by using additional training data that is representative of the various age ranges that the application will support.
B. Use chain-of-thought reasoning to deduce the correct style and complexity for a response suitable for that user.
C. Add a role description to the prompt context that instructs the model of the age range that the response should target.
D. Summarize the response text depending on the age of the user so that younger users receive shorter responses.

Answer: C

NEW QUESTION # 101
A financial institution is using Amazon Bedrock to develop an AI application. The application is hosted in a VPC. To meet regulatory compliance standards, the VPC is not allowed access to any internet traffic.
Which AWS service or feature will meet these requirements?

A. Internet gateway
B. Amazon CloudFront
C. AWS PrivateLink
D. Amazon Macie

Answer: C

Explanation:
AWS PrivateLink enables private connectivity between VPCs and AWS services without exposing traffic to the public internet. This feature is critical for meeting regulatory compliance standards that require isolation from public internet traffic.
* Option A (Correct): "AWS PrivateLink": This is the correct answer because it allows secure access to Amazon Bedrock and other AWS services from a VPC without internet access, ensuring compliance with regulatory standards.
* Option B: "Amazon Macie" is incorrect because it is a security service for data classification and protection, not for managing private network traffic.
* Option C: "Amazon CloudFront" is incorrect because it is a content delivery network service and does not provide private network connectivity.
* Option D: "Internet gateway" is incorrect as it enables internet access, which violates the VPC's no- internet-traffic policy.
AWS AI Practitioner References:
* AWS PrivateLink Documentation: AWS highlights PrivateLink as a solution for connecting VPCs to AWS services privately, which is essential for organizations with strict regulatory requirements.

NEW QUESTION # 102
A company wants to display the total sales for its top-selling products across various retail locations in the past 12 months.
Which AWS solution should the company use to automate the generation of graphs?

A. Amazon Q in Amazon QuickSight
B. Amazon Q in Amazon EC2
C. Amazon Q Developer
D. Amazon Q in AWS Chatbot

Answer: A

Explanation:
Amazon QuickSight is a fully managed business intelligence (BI) service that allows users to create and publish interactive dashboards that include visualizations like graphs, charts, and tables. "Amazon Q" is the natural language query feature within Amazon QuickSight. It enables users to ask questions about their data in natural language and receive visual responses such as graphs.
Option C (Correct): "Amazon Q in Amazon QuickSight": This is the correct answer because Amazon QuickSight Q is specifically designed to allow users to explore their data through natural language queries, and it can automatically generate graphs to display sales data and other metrics. This makes it an ideal choice for the company to automate the generation of graphs showing total sales for its top-selling products across various retail locations.
Option A, B, and D: These options are incorrect:
A . Amazon Q in Amazon EC2: Amazon EC2 is a compute service that provides virtual servers, but it is not directly related to generating graphs or providing natural language querying features.
B . Amazon Q Developer: This is not an existing AWS service or feature.
D . Amazon Q in AWS Chatbot: AWS Chatbot is a service that integrates with Amazon Chime and Slack for monitoring and managing AWS resources, but it is not used for generating graphs based on sales data.
AWS AI Practitioner Reference:
Amazon QuickSight Q is designed to provide insights from data by using natural language queries, making it a powerful tool for generating automated graphs and visualizations directly from queried data.
Business Intelligence (BI) on AWS: AWS services such as Amazon QuickSight provide business intelligence capabilities, including automated reporting and visualization features, which are ideal for companies seeking to visualize data like sales trends over time.

NEW QUESTION # 103
A company is using a pre-trained large language model (LLM) to extract information from documents. The company noticed that a newer LLM from a different provider is available on Amazon Bedrock. The company wants to transition to the new LLM on Amazon Bedrock.
What does the company need to do to transition to the new LLM?

A. Create a new labeled dataset
B. Adjust the prompt template.
C. Fine-tune the LLM.
D. Perform feature engineering.

Answer: B

Explanation:
Transitioning to a new large language model (LLM) on Amazon Bedrock typically involves minimal changes when the new model is pre-trained and available as a foundation model. Since the company is moving from one pre-trained LLM to another, the primary task is to ensure compatibility between the new model's input requirements and the existing application. Adjusting the prompt template is often necessary because different LLMs may have varying prompt formats, tokenization methods, or response behaviors, even for similar tasks like document extraction.
Exact Extract from AWS AI Documents:
From the AWS Bedrock User Guide:
"When switching between foundation models in Amazon Bedrock, you may need to adjust the prompt template to align with the new model's expected input format and optimize its performance for your use case.
Prompt engineering is critical to ensure the model understands the task and generates accurate outputs." (Source: AWS Bedrock User Guide, Prompt Engineering for Foundation Models) Detailed Explanation:
Option A: Create a new labeled dataset.Creating a new labeled dataset is unnecessary when transitioning to a new pre-trained LLM, as pre-trained models are already trained on large datasets. This option would only be relevant if the company were training a custom model from scratch, which is not the case here.
Option B: Perform feature engineering.Feature engineering is typically associated with traditional machine learning models, not pre-trained LLMs. LLMs process raw text inputs, and transitioning to a new LLM does not require restructuring input features. This option is incorrect.
Option C: Adjust the prompt template.This is the correct approach. Different LLMs may interpret prompts differently due to variations in training data, tokenization, or model architecture. Adjusting the prompt template ensures the new LLM understands the task (e.g., document extraction) and produces the desired output format. AWS documentation emphasizes prompt engineering as a key step when adopting a new foundation model.
Option D: Fine-tune the LLM.Fine-tuning is not required for transitioning to a new pre-trained LLM unless the company needs to customize the model for a highly specific task. Since the question does not indicate a need for customization beyond document extraction (a common LLM capability), fine-tuning is unnecessary.
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 Working with Foundation Models in Amazon Bedrock Amazon Bedrock Developer Guide: Transitioning Between Models (https://docs.aws.amazon.com/bedrock
/latest/devguide/)

NEW QUESTION # 104
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100% PASS QUIZ 2025 RELIABLE AMAZON VALID TEST AIF-C01 BRAINDUMPS

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