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Amazon AWS Certified AI Practitioner Sample Questions (Q111-Q116):

NEW QUESTION # 111
Which technique breaks a complex task into smaller subtasks that are sent sequentially to a large language model (LLM)?

A. Retrieval Augmented Generation (RAG)
B. Prompt chaining
C. Tree of thoughts
D. One-shot prompting

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 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.
Reference:
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 # 112
A company wants more customized responses to its generative AI models' prompts.
Select the correct customization methodology from the following list for each use case. Each use case should be selected one time. (Select THREE.)
* Continued pre-training
* Data augmentation
* Model fine-tuning

Answer:

Explanation:

NEW QUESTION # 113
A media company wants to analyze viewer behavior and demographics to recommend personalized content. The company wants to deploy a customized ML model in its production environment. The company also wants to observe if the model quality drifts over time.
Which AWS service or feature meets these requirements?

A. Amazon SageMaker Model Monitor
B. Amazon Comprehend
C. Amazon SageMaker Clarify
D. Amazon Rekognition

Answer: A

Explanation:
A: Amazon Rekognition: This service is designed for image and video analysis, such as object detection, facial recognition, and text extraction. It is not suited for deploying custom ML models or monitoring model quality drift.
B: Amazon SageMaker Clarify: This feature helps detect bias in ML models and explains model predictions. While it addresses fairness and interpretability, it does not specifically focus on monitoring model quality drift over time in production.
C: Amazon Comprehend: This is a natural language processing (NLP) service for extracting insights from text, such as sentiment analysis or entity recognition. It does not support deploying custom ML models or monitoring model performance drift.
D: Amazon SageMaker Model Monitor: This feature is part of Amazon SageMaker and is specifically designed to monitor ML models in production. It tracks metrics such as data drift, model drift, and performance degradation over time, alerting users when issues are detected.
Exact Extract Reference: According to the AWS documentation on Amazon SageMaker, "Amazon SageMaker Model Monitor allows you to detect and remediate data and model quality issues in production. It continuously monitors the performance of deployed models, capturing data and model predictions to detect deviations from expected behavior, such as data drift or model performance degradation." (Source: AWS SageMaker Documentation - Model Monitoring, https://docs.aws.amazon.com/sagemaker/latest/dg/model-monitor.html).
This directly aligns with the requirement to observe model quality drift, making Amazon SageMaker Model Monitor the correct choice.
Explanation:
The requirement is to deploy a customized machine learning (ML) model and monitor its quality for potential drift over time in a production environment. Let's evaluate each option:
Reference:
AWS SageMaker Documentation: Model Monitoring (https://docs.aws.amazon.com/sagemaker/latest/dg/model-monitor.html) AWS AI Practitioner Study Guide (conceptual alignment with monitoring deployed ML models)

NEW QUESTION # 114
A company is using Amazon SageMaker Studio notebooks to build and train ML models. The company stores the data in an Amazon S3 bucket. The company needs to manage the flow of data from Amazon S3 to SageMaker Studio notebooks.
Which solution will meet this requirement?

A. Use Amazon Inspector to monitor SageMaker Studio.
B. Use Amazon Macie to monitor SageMaker Studio.
C. Configure SageMaker to use S3 Glacier Deep Archive.
D. Configure SageMaker to use a VPC with an S3 endpoint.

Answer: D

NEW QUESTION # 115
An AI practitioner is using a large language model (LLM) to create content for marketing campaigns. The generated content sounds plausible and factual but is incorrect.
Which problem is the LLM having?

A. Overfitting
B. Underfitting
C. Data leakage
D. Hallucination

Answer: D

NEW QUESTION # 116
......

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