3 formats of updated BraindumpsVCE Amazon AIF-C01 Exam Questions

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Amazon AIF-C01 Exam Syllabus Topics:

Topic	Details
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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Amazon AWS Certified AI Practitioner Sample Questions (Q34-Q39):

NEW QUESTION # 34
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. AWS PrivateLink
C. Amazon CloudFront
D. Amazon Macie

Answer: B

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 # 35
An AI practitioner is building a model to generate images of humans in various professions. The AI practitioner discovered that the input data is biased and that specific attributes affect the image generation and create bias in the model.
Which technique will solve the problem?

A. Watermark detection for images
B. Model monitoring for class distribution
C. Data augmentation for imbalanced classes
D. Retrieval Augmented Generation (RAG)

Answer: C

Explanation:
Data augmentation for imbalanced classes is the correct technique to address bias in input data affecting image generation.
* Data Augmentation for Imbalanced Classes:
* Involves generating new data samples by modifying existing ones, such as flipping, rotating, or cropping images, to balance the representation of different classes.
* Helps mitigate bias by ensuring that the training data is more representative of diverse characteristics and scenarios.
* Why Option A is Correct:
* Balances Data Distribution: Addresses class imbalance by augmenting underrepresented classes, which reduces bias in the model.
* Improves Model Fairness: Ensures that the model is exposed to a more diverse set of training examples, promoting fairness in image generation.
* Why Other Options are Incorrect:
* B. Model monitoring for class distribution: Helps identify bias but does not actively correct it.
* C. Retrieval Augmented Generation (RAG): Involves combining retrieval and generation but is unrelated to mitigating bias in image generation.
* D. Watermark detection for images: Detects watermarks in images, not a technique for addressing bias.

NEW QUESTION # 36
A company is using domain-specific models. The company wants to avoid creating new models from the beginning. The company instead wants to adapt pre-trained models to create models for new, related tasks.
Which ML strategy meets these requirements?

A. Use transfer learning.
B. Use unsupervised learning.
C. Increase the number of epochs.
D. Decrease the number of epochs.

Answer: A

NEW QUESTION # 37
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 Developer
B. Amazon Q in Amazon EC2
C. Amazon Q in AWS Chatbot
D. Amazon Q in Amazon QuickSight

Answer: D

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 References:
* 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 # 38
A company wants to build an ML model by using Amazon SageMaker. The company needs to share and manage variables for model development across multiple teams.
Which SageMaker feature meets these requirements?

A. Amazon SageMaker Clarify
B. Amazon SageMaker Model Cards
C. Amazon SageMaker Data Wrangler
D. Amazon SageMaker Feature Store

Answer: D

Explanation:
Amazon SageMaker Feature Store is the correct solution for sharing and managing variables (features) across multiple teams during model development.
* Amazon SageMaker Feature Store:
* A fully managed repository for storing, sharing, and managing machine learning features across different teams and models.
* It enables collaboration and reuse of features, ensuring consistent data usage and reducing redundancy.
* Why Option A is Correct:
* Centralized Feature Management: Provides a central repository for managing features, making it easier to share them across teams.
* Collaboration and Reusability: Improves efficiency by allowing teams to reuse existing features instead of creating them from scratch.
* Why Other Options are Incorrect:
* B. SageMaker Data Wrangler: Helps with data preparation and analysis but does not provide a centralized feature store.
* C. SageMaker Clarify: Used for bias detection and explainability, not for managing variables across teams.
* D. SageMaker Model Cards: Provide model documentation, not feature management.

NEW QUESTION # 39
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3 formats of updated BraindumpsVCE Amazon AIF-C01 Exam Questions

Amazon AIF-C01 Exam Syllabus Topics:

Pass Guaranteed Amazon - Unparalleled AIF-C01 - Exam AWS Certified AI Practitioner Material

Amazon AWS Certified AI Practitioner Sample Questions (Q34-Q39):

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