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Quiz 2025 AIF-C01: AWS Certified AI Practitioner Pass-Sure Free Braindumps

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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 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 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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Amazon AWS Certified AI Practitioner Sample Questions (Q129-Q134):

NEW QUESTION # 129
A bank is building a chatbot to answer customer questions about opening a bank account. The chatbot will use public bank documents to generate responses. The company will use Amazon Bedrock and prompt engineering to improve the chatbot's responses.
Which prompt engineering technique meets these requirements?

A. Directional stimulus prompting
B. Zero-shot prompting
C. Complexity-based prompting
D. Few-shot prompting

Answer: A

Explanation:
Directional stimulus prompting guides the foundation model to produce outputs aligned with business context. It's particularly effective for aligning responses with public documents and improving coherence. From Bedrock Prompt Engineering Techniques documentation:
Explanation:
Directional stimulus prompting guides the foundation model to produce outputs aligned with business context. It's particularly effective for aligning responses with public documents and improving coherence. From Bedrock Prompt Engineering Techniques documentation:
"Directional stimulus prompting provides structured prompts to steer the model output towards desired formats or behaviors using specific linguistic cues."

NEW QUESTION # 130
A company has installed a security camera. The company uses an ML model to evaluate the security camera footage for potential thefts. The company has discovered that the model disproportionately flags people who are members of a specific ethnic group.
Which type of bias is affecting the model output?

A. Measurement bias
B. Sampling bias
C. Confirmation bias
D. Observer bias

Answer: B

Explanation:
Sampling bias is the correct type of bias affecting the model output when it disproportionately flags people from a specific ethnic group.
* Sampling Bias:
* Occurs when the training data is not representative of the broader population, leading to skewed model outputs.
* In this case, if the model disproportionately flags people from a specific ethnic group, it likely indicates that the training data was not adequately balanced or representative.
* Why Option B is Correct:
* Reflects Data Imbalance: A biased sample in the training data could result in unfair outcomes, such as disproportionately flagging a particular group.
* Common Issue in ML Models: Sampling bias is a known problem that can lead to unfair or inaccurate model predictions.
* Why Other Options are Incorrect:
* A. Measurement bias: Involves errors in data collection or measurement, not sampling.
* C. Observer bias: Refers to bias introduced by researchers or data collectors, not the model's output.
* D. Confirmation bias: Involves favoring information that confirms existing beliefs, not relevant to model output bias.

NEW QUESTION # 131
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 # 132
A company needs to use Amazon SageMaker AI for model training and inference. The company must comply with regulatory requirements to run SageMaker jobs in an isolated environment without internet access.
Which solution will meet these requirements?

A. Associate appropriate AWS Identity and Access Management (IAM) roles with the SageMaker jobs.
B. Encrypt the data at rest by using encryption for SageMaker geospatial capabilities.
C. Run SageMaker training and inference by using SageMaker Experiments.
D. Run SageMaker training and inference by using network isolation.

Answer: D

NEW QUESTION # 133
A company needs to train an ML model to classify images of different types of animals. The company has a large dataset of labeled images and will not label more data. Which type of learning should the company use to train the model?

A. Unsupervised learning.
B. Supervised learning.
C. Reinforcement learning.
D. Active learning.

Answer: B

Explanation:
Supervised learning is appropriate when the dataset is labeled. The model uses this data to learn patterns and classify images. Unsupervised learning, reinforcement learning, and active learning are not suitable since they either require unlabeled data or different problem settings. References: AWS Machine Learning Best Practices.

NEW QUESTION # 134
......

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