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Amazon AIF-C01 Exam Syllabus Topics:

Topic	Details
Topic 1	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 2	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 3	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 4	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.
Topic 5	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.

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Amazon AWS Certified AI Practitioner Sample Questions (Q70-Q75):

NEW QUESTION # 70
A company wants to use Amazon Q Business for its dat
a. The company needs to ensure the security and privacy of the data. Which combination of steps will meet these requirements? (Select TWO.)

A. Configure AWS Identity and Access Management (IAM) for authentication.
B. Allow public access to the Amazon Q index.
C. Set up cross-account access to the Amazon Q index.
D. Configure Amazon Inspector for authentication.
E. Enable AWS Key Management Service (AWS KMS) keys for the Amazon Q Business Enterprise index.

Answer: A,E

Explanation:
The correct answers are A and E because both directly align with AWS best practices for securing generative AI services and data privacy in enterprise applications.
From the AWS Amazon Q Business documentation:
"AWS Key Management Service (KMS) integrates with Amazon Q Business to encrypt sensitive data at rest. You can use customer-managed KMS keys to meet compliance requirements." And:
"You must configure IAM access controls to manage which users and applications can access Amazon Q Business indexes, ensuring that only authorized users can retrieve information." Explanation of other options:
B . Cross-account access is not a common requirement for internal enterprise use of Amazon Q Business unless explicitly sharing data across organizations. It's not a requirement for securing access.
C . Amazon Inspector is a vulnerability management tool for EC2 and containers. It is unrelated to Amazon Q authentication or security.
D . Allowing public access would violate security and privacy principles and directly contradict the stated requirement.
Referenced AWS AI/ML Documents and Study Guides:
Amazon Q Business Developer Guide - Security and Identity Management
AWS KMS Documentation - Integration with Bedrock and Amazon Q
AWS Certified Machine Learning Specialty Guide - Responsible AI and Governance Section

NEW QUESTION # 71
A company wants to assess the costs that are associated with using a large language model (LLM) to generate inferences. The company wants to use Amazon Bedrock to build generative AI applications.
Which factor will drive the inference costs?

A. Temperature value
B. Total training time
C. Number of tokens consumed
D. Amount of data used to train the LLM

Answer: C

NEW QUESTION # 72
A company is creating a model to label credit card transactions. The company has a large volume of sample transaction data to train the model. Most of the transaction data is unlabeled. The data does not contain confidential information. The company needs to obtain labeled sample data to fine-tune the model.

A. Run an Amazon SageMaker AI training job that uses the PyTorch Distributed library to label data
B. Use an Amazon SageMaker Ground Truth labeling job with Amazon Mechanical Turk workers
C. Run batch inference jobs on the unlabeled data
D. Run an Amazon SageMaker AI labeling job
E. Use an optical character recognition model trained on labeled samples to label unlabeled samples

Answer: B

Explanation:
Comprehensive and Detailed
Amazon SageMaker Ground Truth lets you create data labeling jobs and can integrate with Amazon Mechanical Turk (a distributed human workforce) to label large unlabeled datasets.
A (batch inference) applies models to already-trained data, not labeling.
B (PyTorch Distributed) is for distributed training, not labeling.
D (OCR) applies only to text extraction from images, not transactions.
E is incorrect; Ground Truth is the service for labeling, not "AI labeling job." Reference:
AWS Documentation - SageMaker Ground Truth

NEW QUESTION # 73
An AI practitioner is using an Amazon Bedrock base model to summarize session chats from the customer service department. The AI practitioner wants to store invocation logs to monitor model input and output data.
Which strategy should the AI practitioner use?

A. Configure model invocation logging in Amazon EventBridge.
B. Configure AWS Audit Manager as the logs destination for the model.
C. Configure AWS CloudTrail as the logs destination for the model.
D. Enable invocation logging in Amazon Bedrock.

Answer: D

Explanation:
Amazon Bedrock provides an option to enable invocation logging to capture and store the input and output data of the models used. This is essential for monitoring and auditing purposes, particularly when handling customer data.
Option B (Correct): "Enable invocation logging in Amazon Bedrock": This is the correct answer as it directly enables the logging of all model invocations, ensuring transparency and traceability.
Option A: "Configure AWS CloudTrail" is incorrect because CloudTrail logs API calls but does not provide specific logging for model inputs and outputs.
Option C: "Configure AWS Audit Manager" is incorrect as Audit Manager is used for compliance reporting, not specific invocation logging for AI models.
Option D: "Configure model invocation logging in Amazon EventBridge" is incorrect as EventBridge is for event-driven architectures, not specifically designed for logging AI model inputs and outputs.
AWS AI Practitioner Reference:
Amazon Bedrock Logging Capabilities: AWS emphasizes using built-in logging features in Bedrock to maintain data integrity and transparency in model operations.

NEW QUESTION # 74
Which option is a benefit of using Amazon SageMaker Model Cards to document AI models?

A. Providing a visually appealing summary of a model's capabilities.
B. Reducing the overall computational requirements of a model.
C. Physically storing models for archival purposes.
D. Standardizing information about a model's purpose, performance, and limitations.

Answer: D

Explanation:
Amazon SageMaker Model Cards provide a standardized way to document important details about an AI model, such as its purpose, performance, intended usage, and known limitations. This enables transparency and compliance while fostering better communication between stakeholders. It does not store models physically or optimize computational requirements. References: AWS SageMaker Model Cards Documentation.

NEW QUESTION # 75
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