Latest Amazon AIF-C01 Exam Questions in Three Different Formats

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Amazon AIF-C01 Exam Syllabus Topics:

Topic	Details
Topic 1	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 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	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 4	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 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.

>> Exam Topics AIF-C01 Pdf <<

AIF-C01 Test Book - AIF-C01 Exam Dumps Free

VCEDumps offers a complete AWS Certified AI Practitioner (AIF-C01) practice questions in PDF format. This Amazon AIF-C01 test questions pdf file format is simple to use and can be accessed from any device, including a desktop, tablet, laptop, Mac, or smartphone. No matter where you are, you can learn on the go. The PDF version of the AWS Certified AI Practitioner (AIF-C01) exam questions is also readily printable, allowing you to keep tangible copies of the AWS Certified AI Practitioner (AIF-C01) questions with you at all times.

Amazon AWS Certified AI Practitioner Sample Questions (Q42-Q47):

NEW QUESTION # 42
A company wants to use language models to create an application for inference on edge devices. The inference must have the lowest latency possible.
Which solution will meet these requirements?

A. Deploy optimized small language models (SLMs) on edge devices.
B. Incorporate a centralized small language model (SLM) API for asynchronous communication with edge devices.
C. Deploy optimized large language models (LLMs) on edge devices.
D. Incorporate a centralized large language model (LLM) API for asynchronous communication with edge devices.

Answer: A

NEW QUESTION # 43
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 to be classified without any additional context or examples.
B. Provide the new text passage with a few examples of unrelated tasks, such as text summarization or question answering.
C. Provide a detailed explanation of sentiment analysis and how LLMs work in the prompt.
D. Provide examples of text passages with corresponding positive or negative labels in the prompt followed by the new text passage to be classified.

Answer: D

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 # 44
An accounting firm wants to implement a large language model (LLM) to automate document processing.
The firm must proceed responsibly to avoid potential harms.
What should the firm do when developing and deploying the LLM? (Select TWO.)

A. Modify the training data to mitigate bias.
B. Apply prompt engineering techniques.
C. Avoid overfitting on the training data.
D. Include fairness metrics for model evaluation.
E. Adjust the temperature parameter of the model.

Answer: A,D

Explanation:
To implement a large language model (LLM) responsibly, the firm should focus on fairness and mitigating bias, which are critical for ethical AI deployment.
* A. Include Fairness Metrics for Model Evaluation:
* Fairness metrics help ensure that the model's predictions are unbiased and do not unfairly discriminate against any group.
* These metrics can measure disparities in model outcomes across different demographic groups, ensuring responsible AI practices.
* C. Modify the Training Data to Mitigate Bias:
* Adjusting training data to be more representative and balanced can help reduce bias in the model's predictions.
* Mitigating bias at the data level ensures that the model learns from a diverse and fair dataset, reducing potential harms in deployment.
* Why Other Options are Incorrect:
* B. Adjust the temperature parameter of the model: Controls randomness in outputs but does not directly address fairness or bias.
* D. Avoid overfitting on the training data: Important for model generalization but not directly related to responsible AI practices regarding fairness and bias.
* E. Apply prompt engineering techniques: Useful for improving model outputs but not specifically for mitigating bias or ensuring fairness.

NEW QUESTION # 45
A company wants to use a large language model (LLM) to develop a conversational agent. The company needs to prevent the LLM from being manipulated with common prompt engineering techniques to perform undesirable actions or expose sensitive information.
Which action will reduce these risks?

A. Avoid using LLMs that are not listed in Amazon SageMaker.
B. Create a prompt template that teaches the LLM to detect attack patterns.
C. Increase the temperature parameter on invocation requests to the LLM.
D. Decrease the number of input tokens on invocations of the LLM.

Answer: B

Explanation:
Creating a prompt template that teaches the LLM to detect attack patterns is the most effective way to reduce the risk of the model being manipulated through prompt engineering.
* Prompt Templates for Security:
* A well-designed prompt template can guide the LLM to recognize and respond appropriately to potential manipulation attempts.
* This strategy helps prevent the model from performing undesirable actions or exposing sensitive information by embedding security awareness directly into the prompts.
* Why Option A is Correct:
* Teaches Model Security Awareness: Equips the LLM to handle potentially harmful inputs by recognizing suspicious patterns.
* Reduces Manipulation Risk: Helps mitigate risks associated with prompt engineering attacks by proactively preparing the LLM.
* Why Other Options are Incorrect:
* B. Increase the temperature parameter: This increases randomness in responses, potentially making the LLM more unpredictable and less secure.
* C. Avoid LLMs not listed in SageMaker: Does not directly address the risk of prompt manipulation.
* D. Decrease the number of input tokens: Does not mitigate risks related to prompt manipulation.

NEW QUESTION # 46
A digital devices company wants to predict customer demand for memory hardware. The company does not have coding experience or knowledge of ML algorithms and needs to develop a data-driven predictive model.
The company needs to perform analysis on internal data and external data.
Which solution will meet these requirements?

A. Import the data into Amazon SageMaker Data Wrangler. Build ML models and demand forecast predictions by using an Amazon Personalize Trending-Now recipe.
B. Import the data into Amazon SageMaker Canvas. Build ML models and demand forecast predictions by selecting the values in the data from SageMaker Canvas.
C. Store the data in Amazon S3. Create ML models and demand forecast predictions by using Amazon SageMaker built-in algorithms that use the data from Amazon S3.
D. Import the data into Amazon SageMaker Data Wrangler. Create ML models and demand forecast predictions by using SageMaker built-in algorithms.

Answer: B

Explanation:
Amazon SageMaker Canvas is a visual, no-code machine learning interface that allows users to build machine learning models without having any coding experience or knowledge of machine learning algorithms. It enables users to analyze internal and external data, and make predictions using a guided interface.
* Option D (Correct): "Import the data into Amazon SageMaker Canvas. Build ML models and demand forecast predictions by selecting the values in the data from SageMaker Canvas": This is the correct answer because SageMaker Canvas is designed for users without coding experience, providing a visual interface to build predictive models with ease.
* Option A: "Store the data in Amazon S3 and use SageMaker built-in algorithms" is incorrect because it requires coding knowledge to interact with SageMaker's built-in algorithms.
* Option B: "Import the data into Amazon SageMaker Data Wrangler" is incorrect. Data Wrangler is primarily for data preparation and not directly focused on creating ML models without coding.
* Option C: "Use Amazon Personalize Trending-Now recipe" is incorrect as Amazon Personalize is for building recommendation systems, not for general demand forecasting.
AWS AI Practitioner References:
* Amazon SageMaker Canvas Overview: AWS documentation emphasizes Canvas as a no-code solution for building machine learning models, suitable for business analysts and users with no coding experience.

NEW QUESTION # 47
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LATEST AMAZON AIF-C01 EXAM QUESTIONS IN THREE DIFFERENT FORMATS

Latest Amazon AIF-C01 Exam Questions in Three Different Formats