RELIABLE AIF-C01 REAL EXAM EXAM PASS FOR SURE | VALID AIF-C01 TEST SYLLABUS

Reliable AIF-C01 Real Exam Exam Pass For Sure | Valid AIF-C01 Test Syllabus

Reliable AIF-C01 Real Exam Exam Pass For Sure | Valid AIF-C01 Test Syllabus

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Amazon AWS Certified AI Practitioner Sample Questions (Q20-Q25):

NEW QUESTION # 20
Which option describes embeddings in the context of AI?

  • A. A numerical method for data representation in a reduced dimensionality space
  • B. A method for visualizing high-dimensional data
  • C. An encryption method for securing sensitive data
  • D. A method for compressing large datasets

Answer: A

Explanation:
Embeddings in AI refer to numerical representations of data (e.g., text, images) in a lower-dimensional space, capturing semantic or contextual relationships. They are widely used in NLP and other AI tasks to represent complex data in a format that models can process efficiently.
Exact Extract from AWS AI Documents:
From the AWS AI Practitioner Learning Path:
"Embeddings are numerical representations of data in a reduced dimensionality space. In natural language processing, for example, word or sentence embeddings capture semantic relationships, enabling models to process text efficiently for tasks like classification or similarity search." (Source: AWS AI Practitioner Learning Path, Module on AI Concepts) Detailed Explanation:
Option A: A method for compressing large datasetsWhile embeddings reduce dimensionality, their primary purpose is not data compression but rather to represent data in a way that preserves meaningful relationships.
This option is incorrect.
Option B: An encryption method for securing sensitive dataEmbeddings are not related to encryption or data security. They are used for data representation, making this option incorrect.
Option C: A method for visualizing high-dimensional dataWhile embeddings can sometimes be used in visualization (e.g., t-SNE), their primary role is data representation for model processing, not visualization.
This option is misleading.
Option D: A numerical method for data representation in a reduced dimensionality spaceThis is the correct answer. Embeddings transform complex data into lower-dimensional numerical vectors, preserving semantic or contextual information for use in AI models.
References:
AWS AI Practitioner Learning Path: Module on AI Concepts
Amazon Comprehend Developer Guide: Embeddings for Text Analysis (https://docs.aws.amazon.com
/comprehend/latest/dg/embeddings.html)
AWS Documentation: What are Embeddings? (https://aws.amazon.com/what-is/embeddings/)


NEW QUESTION # 21
An ML research team develops custom ML models. The model artifacts are shared with other teams for integration into products and services. The ML team retains the model training code and dat a. The ML team wants to builk a mechanism that the ML team can use to audit models.
Which solution should the ML team use when publishing the custom ML models?

  • A. Create model training scripts. Commit the model training scripts to a Git repository.
  • B. Create documents with the relevant information. Store the documents in Amazon S3.
  • C. Use AWS A] Service Cards for transparency and understanding models.
  • D. Create Amazon SageMaker Model Cards with Intended uses and training and inference details.

Answer: D

Explanation:
The ML research team needs a mechanism to audit custom ML models while sharing model artifacts with other teams. Amazon SageMaker Model Cards provide a structured way todocument model details, including intended uses, training data, and inference performance, making them ideal for auditing and ensuring transparency when publishing models.
Exact Extract from AWS AI Documents:
From the Amazon SageMaker Developer Guide:
"Amazon SageMaker Model Cards enable you to document critical details about your machine learning models, such as intended uses, training data, evaluation metrics, and inference details. Model Cards support auditing by providing a centralized record that can be reviewed by teams to understand model behavior and limitations." (Source: Amazon SageMaker Developer Guide, SageMaker Model Cards) Detailed Option A: Create documents with the relevant information. Store the documents in Amazon S3.While storing documents in S3 is feasible, it lacks the structured format and integration with SageMaker that Model Cards provide, making it less suitable for auditing purposes.
Option B: Use AWS AI Service Cards for transparency and understanding models.AWS AI Service Cards are not a standard feature in AWS documentation. This option appears to be a distractor and is not a valid solution.
Option C: Create Amazon SageMaker Model Cards with Intended uses and training and inference details.This is the correct answer. SageMaker Model Cards are specifically designed to document model details for auditing, transparency, and collaboration, meeting the team's requirements.
Option D: Create model training scripts. Commit the model training scripts to a Git repository.Sharing training scripts in a Git repository provides access to code but does not offer a structured auditing mechanism for model details like intended uses or inference performance.
Reference:
Amazon SageMaker Developer Guide: SageMaker Model Cards (https://docs.aws.amazon.com/sagemaker/latest/dg/model-cards.html) AWS AI Practitioner Learning Path: Module on Model Governance and Auditing AWS Documentation: Responsible AI with SageMaker (https://aws.amazon.com/sagemaker/)


NEW QUESTION # 22
What are tokens in the context of generative AI models?

  • A. Tokens are the pre-trained weights of a generative AI model that are fine-tuned for specific tasks.
  • B. Tokens are the specific prompts or instructions given to a generative AI model to generate output.
  • C. Tokens are the basic units of input and output that a generative AI model operates on, representing words, subwords, or other linguistic units.
  • D. Tokens are the mathematical representations of words or concepts used in generative AI models.

Answer: C

Explanation:
Tokens in generative AI models are the smallest units that the model processes, typically representing words, subwords, or characters. They are essential for the model to understand and generate language, breaking down text into manageable parts for processing.
* Option A (Correct): "Tokens are the basic units of input and output that a generative AI model operates on, representing words, subwords, or other linguistic units": This is the correct definition of tokens in the context of generative AI models.
* Option B: "Mathematical representations of words" describes embeddings, not tokens.
* Option C: "Pre-trained weights of a model" refers to the parameters of a model, not tokens.
* Option D: "Prompts or instructions given to a model" refers to the queries or commands provided to a model, not tokens.
AWS AI Practitioner References:
* Understanding Tokens in NLP: AWS provides detailed explanations of how tokens are used in natural language processing tasks by AI models, such as in Amazon Comprehend and other AWS AI services.


NEW QUESTION # 23
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. Amazon CloudFront
  • B. Internet gateway
  • C. Amazon Macie
  • D. AWS PrivateLink

Answer: D


NEW QUESTION # 24
A company wants to use a large language model (LLM) on Amazon Bedrock for sentiment analysis. The company wants to classify the sentiment of text passages as positive or negative.
Which prompt engineering strategy meets these requirements?

  • A. Provide the new text passage with a few examples of unrelated tasks, such as text summarization or question answering.
  • B. Provide the new text passage to be classified without any additional context or examples.
  • C. Provide examples of text passages with corresponding positive or negative labels in the prompt followed by the new text passage to be classified.
  • D. Provide a detailed explanation of sentiment analysis and how LLMs work in the prompt.

Answer: C

Explanation:
Providing examples of text passages with corresponding positive or negative labels in the prompt followed by the new text passage to be classified is the correct prompt engineering strategy for using a large language model (LLM) on Amazon Bedrock for sentiment analysis.
* Example-Driven Prompts:
* This strategy, known as few-shot learning, involves giving the model examples of input-output pairs (e.g., text passages with their sentiment labels) to help it understand the task context.
* It allows the model to learn from these examples and apply the learned pattern to classify new text passages correctly.
* Why Option A is Correct:
* Guides the Model: Providing labeled examples teaches the model how to perform sentiment analysis effectively, increasing accuracy.
* Contextual Relevance: Aligns the model's responses to the specific task of classifying sentiment.
* Why Other Options are Incorrect:
* B. Detailed explanation of sentiment analysis: Unnecessary for the model's operation; it requires examples, not explanations.
* C. New text passage without context: Provides no guidance or learning context for the model.
* D. Unrelated task examples: Would confuse the model and lead to inaccurate results.


NEW QUESTION # 25
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