AIF-C01 Reliable Test Blueprint, AIF-C01 Valid Exam Pattern

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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	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 3	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 4	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 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 Valid Exam Pattern & AIF-C01 Discount Code

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Amazon AWS Certified AI Practitioner Sample Questions (Q106-Q111):

NEW QUESTION # 106
A company wants to build an interactive application for children that generates new stories based on classic stories. The company wants to use Amazon Bedrock and needs to ensure that the results and topics are appropriate for children.
Which AWS service or feature will meet these requirements?

A. Amazon Rekognition
B. Guardrails for Amazon Bedrock
C. Agents for Amazon Bedrock
D. Amazon Bedrock playgrounds

Answer: B

NEW QUESTION # 107
Which AWS feature records details about ML instance data for governance and reporting?

A. Amazon SageMaker Model Monitor
B. Amazon SageMaker Model Cards
C. Amazon SageMaker JumpStart
D. Amazon SageMaker Debugger

Answer: B

Explanation:
Amazon SageMaker Model Cards provide a centralized and standardized repository for documenting machine learning models. They capture key details such as the model's intended use, training and evaluation datasets, performance metrics, ethical considerations, and other relevant information. This documentation facilitates governance and reporting by ensuring that all stakeholders have access to consistent and comprehensive information about each model. While Amazon SageMaker Debugger is used for real-time debugging and monitoring during training, and Amazon SageMaker Model Monitor tracks deployed models for data and predictionquality, neither offers the comprehensive documentation capabilities of Model Cards. Amazon SageMaker JumpStart provides pre-built models and solutions but does not focus on governance documentation.
Reference: Amazon SageMaker Model Cards

NEW QUESTION # 108
What does an F1 score measure in the context of foundation model (FM) performance?

A. Financial cost of operating the model
B. Model speed in generating responses
C. Energy efficiency of the model's computations
D. Model precision and recall

Answer: D

Explanation:
The F1 score is a metric used to evaluate the performance of a classification model by considering both precision and recall. Precision measures the accuracy of positive predictions (i.e., the proportion of true positive predictions among all positive predictions made by the model), while recall measures the model's ability to identify all relevant positive instances (i.e., the proportion of true positive predictions among all actual positive instances). The F1 score is the harmonic mean of precision and recall, providing a single metric that balances both concerns. This is particularly useful when dealing with imbalanced datasets or when the cost of false positives and false negatives is significant. Options B, C, and D pertain to other aspects of model performance but are not related to the F1 score.

NEW QUESTION # 109
A company has a database of petabytes of unstructured data from internal sources. The company wants to transform this data into a structured format so that its data scientists can perform machine learning (ML) tasks.
Which service will meet these requirements?

A. Amazon Lex
B. Amazon Rekognition
C. AWS Glue
D. Amazon Kinesis Data Streams

Answer: C

NEW QUESTION # 110
A company is building a contact center application and wants to gain insights from customer conversations.
The company wants to analyze and extract key information from the audio of the customer calls.
Which solution meets these requirements?

A. Create classification labels by using Amazon Comprehend.
B. Build a conversational chatbot by using Amazon Lex.
C. Extract information from call recordings by using Amazon SageMaker Model Monitor.
D. Transcribe call recordings by using Amazon Transcribe.

Answer: D

Explanation:
Amazon Transcribe is the correct solution for converting audio from customer calls into text, allowing the company to analyze and extract key information from the conversations.
* Amazon Transcribe:
* It is a fully managed automatic speech recognition (ASR) service that converts speech into text, making it easier to perform text-based analysis on audio data.
* After transcribing the audio, further analysis can be performed using other AWS services like Amazon Comprehend to extract insights such as sentiment, key phrases, or entities.
* Why Option B is Correct:
* Conversion to Text: Transcribing audio recordings is the first step in gaining insights from spoken conversations, allowing for further processing.
* Enables Further Analysis: Once the audio is transcribed into text, other tools and services can be used to analyze the content more deeply.
* Why Other Options are Incorrect:
* A. Amazon Lex: Is used for building conversational interfaces, not for transcribing or analyzing audio from customer calls.
* C. Amazon SageMaker Model Monitor: Monitors ML models for bias and data drift, not for audio analysis.
* D. Amazon Comprehend: Can analyze text but cannot transcribe audio; it would be used after transcription is completed.

NEW QUESTION # 111
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AIF-C01 RELIABLE TEST BLUEPRINT, AIF-C01 VALID EXAM PATTERN

AIF-C01 Reliable Test Blueprint, AIF-C01 Valid Exam Pattern