AIF-C01 Practice Exam Fee | AIF-C01 Valid Study Plan

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Once you have practiced on our AWS Certified AI Practitioner test questions, the system will automatically memorize and analyze all your practice. You must finish the model test in limited time. There have a timer on the right of the interface. Once you begin to do the exercises of the AIF-C01 test guide, the timer will start to work and count down. If you don’t finish doing the exercises, all your exercises of the AIF-C01 Exam Questions will be delivered automatically. Then the system will generate a report according to your performance. You will clearly know where you are good at or not. Then you can make your own learning plans based on the report of the AIF-C01 test guide. Also, you will do more practices that you are not good at until you completely have no problem.

Amazon AIF-C01 Exam Syllabus Topics:

Topic	Details
Topic 1	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 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	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.

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The Amazon AIF-C01 certification exam is one of the best certification exams that offer a unique opportunity to advance beginners or experience a professional career. With the AWS Certified AI Practitioner AIF-C01 exam everyone can validate their skills and knowledge easily and quickly. There are other several benefits that you can gain with the AWS Certified AI Practitioner AIF-C01 Certification test. The prominent advantages of the AIF-C01 certification exam are more career opportunities, proven skills, chances of instant promotion, more job roles, and becoming a member of the AIF-C01 certification community.

Amazon AWS Certified AI Practitioner Sample Questions (Q143-Q148):

NEW QUESTION # 143
A company has terabytes of data in a database that the company can use for business analysis. The company wants to build an AI-based application that can build a SQL query from input text that employees provide.
The employees have minimal experience with technology.
Which solution meets these requirements?

A. Residual neural network
B. WaveNet
C. Generative pre-trained transformers (GPT)
D. Support vector machine

Answer: C

Explanation:
Generative Pre-trained Transformers (GPT) are suitable for building an AI-based application that can generate SQL queries from natural language input provided by employees.
* GPT for Natural Language Processing:
* GPT models are designed for understanding and generating human-like text based on natural language input.
* They can be fine-tuned to interpret specific tasks, such as converting natural language queries into SQL queries.
* Why Option A is Correct:
* Natural Language Understanding: GPT is highly effective for tasks that require understanding of human language and generating structured outputs like SQL.
* User-Friendly: Requires minimal technology experience from employees, as they provide simple text input.
* Why Other Options are Incorrect:
* B. Residual neural network: Typically used in computer vision tasks, not for natural language- to-SQL conversion.
* C. Support vector machine: Used for classification tasks, not for generating structured queries from text.
* D. WaveNet: A deep generative model for audio data, unrelated to text-to-SQL tasks.

NEW QUESTION # 144
Which AWS feature records details about ML instance data for governance and reporting?

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

Answer: D

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 prediction quality, 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.

NEW QUESTION # 145
A medical company is customizing a foundation model (FM) for diagnostic purposes. The company needs the model to be transparent and explainable to meet regulatory requirements.
Which solution will meet these requirements?

A. Generate simple metrics, reports, and examples by using Amazon SageMaker Clarify.
B. Gather more data. Use Amazon Rekognition to add custom labels to the data.
C. Configure the security and compliance by using Amazon Inspector.
D. Encrypt and secure training data by using Amazon Macie.

Answer: A

Explanation:
Amazon SageMaker Clarify provides transparency and explainability for machine learning models by generating metrics, reports, and examples that help to understand model predictions. For a medical company that needs a foundation model to be transparent and explainable to meet regulatory requirements, SageMaker Clarify is the most suitable solution.
* Amazon SageMaker Clarify:
* It helps in identifying potential bias in the data and model, and also explains model behavior by generating feature attributions, providing insights into which features are most influential in the model's predictions.
* These capabilities are critical in medical applications where regulatory compliance often mandates transparency and explainability to ensure that decisions made by the model can be trusted and audited.
* Why Option B is Correct:
* Transparency and Explainability: SageMaker Clarify is explicitly designed to provide insights into machine learning models' decision-making processes, helping meet regulatory requirements by explaining why a model made a particular prediction.
* Compliance with Regulations: The tool is suitable for use in sensitive domains, such as healthcare, where there is a need for explainable AI.
* Why Other Options are Incorrect:
* A. Amazon Inspector: Focuses on security assessments, not on explainability or model transparency.
* C. Amazon Macie: Provides data security by identifying and protecting sensitive data, but does not help in making models explainable.
* D. Amazon Rekognition: Used for image and video analysis, not relevant to making models explainable.
Thus, B is the correct answer for meeting transparency and explainability requirements for the foundation model

NEW QUESTION # 146
A company wants to create an application to summarize meetings by using meeting audio recordings.
Select and order the correct steps from the following list to create the application. Each step should be selected one time or not at all. (Select and order THREE.)
* Convert meeting audio recordings to meeting text files by using Amazon Polly.
* Convert meeting audio recordings to meeting text files by using Amazon Transcribe.
* Store meeting audio recordings in an Amazon S3 bucket.
* Store meeting audio recordings in an Amazon Elastic Block Store (Amazon EBS) volume.
* Summarize meeting text files by using Amazon Bedrock.
* Summarize meeting text files by using Amazon Lex.

Answer:

Explanation:

Explanation:
Step 1: Store meeting audio recordings in an Amazon S3 bucket.
Step 2: Convert meeting audio recordings to meeting text files by using Amazon Transcribe.
Step 3: Summarize meeting text files by using Amazon Bedrock.
The company wants to create an application to summarize meeting audio recordings, which requires a sequence of steps involving storage, speech-to-text conversion, and text summarization. Amazon S3 is the recommended storage service for audio files, Amazon Transcribe converts audio to text, and Amazon Bedrock provides generative AI capabilities for summarization. These three steps, in this order, create an efficient workflow for the application.
Exact Extract from AWS AI Documents:
From the Amazon Transcribe Developer Guide:
"Amazon Transcribe uses deep learning to convert audio files into text, supporting applications such as meeting transcription. Audio files can be stored in Amazon S3, and Transcribe can process them directly from an S3 bucket." From the AWS Bedrock User Guide:
"Amazon Bedrock provides foundation models that can perform text summarization, enabling developers to build applications that generate concise summaries from text data, such as meeting transcripts." (Source: Amazon Transcribe Developer Guide, Introduction to Amazon Transcribe; AWS Bedrock User Guide, Text Generation and Summarization) Detailed Explanation:
* Step 1: Store meeting audio recordings in an Amazon S3 bucket.Amazon S3 is the standard storage service for audio files in AWS workflows, especially for integration with services like Amazon Transcribe. Storing the recordings in S3 allows Transcribe to access and process them efficiently. This is the first logical step.
* Step 2: Convert meeting audio recordings to meeting text files by using Amazon Transcribe.
Amazon Transcribe is designed for automatic speech recognition (ASR), converting audio files (stored in S3) into text. This step is necessary to transform the meeting recordings into a format that can be summarized.
* Step 3: Summarize meeting text files by using Amazon Bedrock.Amazon Bedrock provides foundation models capable of generative AI tasks like text summarization. Once the audio is converted to text, Bedrock can summarize the meeting transcripts, completing the application's requirements.
Unused Options Analysis:
* Convert meeting audio recordings to meeting text files by using Amazon Polly.Amazon Polly is a text-to-speech service, not for converting audio to text. This option is incorrect and not used.
* Store meeting audio recordings in an Amazon Elastic Block Store (Amazon EBS) volume.Amazon EBS is for block storage, typically used for compute instances, not for storing files for processing by services like Transcribe. S3 is the better choice, so this option is not used.
* Summarize meeting text files by using Amazon Lex.Amazon Lex is for building conversational interfaces (chatbots), not for text summarization. Bedrock is the appropriate service for summarization, so this option is not used.
Hotspot Selection Analysis:
The task requires selecting and ordering three steps from the list, with each step used exactly once or not at all. The selected steps-storing in S3, converting with Transcribe, and summarizing with Bedrock-form a complete and logical workflow for the application.
References:
Amazon Transcribe Developer Guide: Introduction to Amazon Transcribe (https://docs.aws.amazon.com
/transcribe/latest/dg/what-is.html)
AWS Bedrock User Guide: Text Generation and Summarization (https://docs.aws.amazon.com/bedrock/latest
/userguide/what-is-bedrock.html)
AWS AI Practitioner Learning Path: Module on Speech-to-Text and Generative AI Amazon S3 User Guide: Storing Data for Processing (https://docs.aws.amazon.com/AmazonS3/latest
/userguide/Welcome.html)

NEW QUESTION # 147
A company wants to use a large language model (LLM) on Amazon Bedrock for sentiment analysis. The company needs the LLM to produce more consistent responses to the same input prompt.
Which adjustment to an inference parameter should the company make to meet these requirements?

A. Decrease the temperature value
B. Increase the temperature value
C. Increase the maximum generation length
D. Decrease the length of output tokens

Answer: A

NEW QUESTION # 148
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AIF-C01 PRACTICE EXAM FEE | AIF-C01 VALID STUDY PLAN

AIF-C01 Practice Exam Fee | AIF-C01 Valid Study Plan