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When you take Amazon AIF-C01 practice exams again and again you get familiar with the AWS Certified AI Practitioner (AIF-C01) real test pressure and learn to handle it for better outcomes. Features of the web-based and desktop AIF-C01 Practice Exams are similar. The only difference is that the AWS Certified AI Practitioner (AIF-C01) web-based version works online.

Amazon AWS Certified AI Practitioner Sample Questions (Q369-Q374):

NEW QUESTION # 369
A company uses Amazon SageMaker AI to generate article summaries in multiple languages. The company needs a metric to evaluate the quality of the summary translations in multiple languages.
Which evaluation metric will meet these requirements?

Answer: C

Explanation:
* BLEU (Bilingual Evaluation Understudy) is the standard metric for evaluating machine translation quality across multiple languages.
* ROUGE is for summarization quality (not translation).
* AUC is for classification model performance.
* Precision is a general metric but not specific for evaluating translations.
# Reference:
AWS Documentation - Evaluation Metrics for NLP


NEW QUESTION # 370
A company is comparing two foundation models (FMs) for a customer service AI assistant. The company wants to evaluate the FMs based on helpfulness, correctness, and tone. The company needs an evaluation technique that is automated, repeatable, and does not require human reviewers.
Which evaluation technique will meet these requirements?

Answer: D

Explanation:
AWS documentation describes LLM-as-a-judge as an automated evaluation technique where a large language model is used to assess the outputs of another model based on qualitative criteria such as helpfulness, correctness, tone, and alignment with expectations. This approach enables scalable and repeatable evaluations without requiring human reviewers.
In this scenario, the company needs to compare two foundation models across subjective dimensions that are difficult to measure using traditional metrics. LLM-as-a-judge allows the evaluator model to score or rank responses using predefined evaluation prompts and criteria, ensuring consistent and automated assessment.
The other options do not meet the requirements. String matching and ROUGE focus on lexical similarity and are unsuitable for evaluating tone or helpfulness in customer service interactions. Retrieval Augmented Generation is an architectural pattern, not an evaluation technique.
AWS highlights LLM-as-a-judge as a practical approach for automated qualitative evaluation of generative AI outputs, making it the correct choice.


NEW QUESTION # 371
Which technique breaks a complex task into smaller subtasks that are sent sequentially to a large language model (LLM)?

Answer: B

Explanation:
Prompt chaining is a technique where a complex task is broken into smaller subtasks, and the outputs of one subtask are used as inputs for the next, sequentially guiding a large language model (LLM) to solve the problem step-by-step. This method is particularly useful for complex tasks that require multiple reasoning steps.
Exact Extract from AWS AI Documents:
From the AWS Bedrock User Guide:
"Prompt chaining involves breaking a complex task into smaller subtasks and sequentially passing the output of one subtask as input to the next, enabling large language models to handle intricate problems by solving them step-by-step." (Source: AWS Bedrock User Guide, Prompt Engineering Techniques) Detailed Explanation:
* Option A: One-shot promptingOne-shot prompting provides a single example to guide the LLM, but it does not break tasks into smaller subtasks or handle sequential processing.
* Option B: Prompt chainingThis is the correct answer. Prompt chaining divides a complex task into smaller, manageable subtasks, solving them sequentially with the LLM, as described.
* Option C: Tree of thoughtsTree of thoughts involves exploring multiple reasoning paths simultaneously, not breaking tasks into sequential subtasks.
* Option D: Retrieval Augmented Generation (RAG)RAG retrieves external information to augment LLM responses but does not specifically break tasks into sequential subtasks.
References:
AWS Bedrock User Guide: Prompt Engineering Techniques (https://docs.aws.amazon.com/bedrock/latest
/userguide/prompt-engineering.html)
AWS AI Practitioner Learning Path: Module on Generative AI Prompting
Amazon Bedrock Developer Guide: Advanced Prompting Strategies (https://aws.amazon.com/bedrock/)


NEW QUESTION # 372
A media streaming platform wants to provide movie recommendations to users based on the users' account history.

Answer: D

Explanation:
Comprehensive and Detailed
Amazon Personalize is a fully managed ML service for personalized recommendations (movies, products, music, etc.) based on user behavior and history.
Polly converts text to lifelike speech.
Comprehend performs NLP tasks like sentiment analysis.
Transcribe is speech-to-text.
Reference:
AWS Documentation - Amazon Personalize


NEW QUESTION # 373
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?

Answer: B

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 # 374
......

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