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Amazon AWS Certified AI Practitioner Sample Questions (Q185-Q190):

NEW QUESTION # 185
A bank has fine-tuned a large language model (LLM) to expedite the loan approval process. During an external audit of the model, the company discovered that the model was approving loans at a faster pace for a specific demographic than for other demographics.
How should the bank fix this issue MOST cost-effectively?

A. Use Retrieval Augmented Generation (RAG) with the fine-tuned model.
B. Pre-train a new LLM with more diverse training data.
C. Include more diverse training data. Fine-tune the model again by using the new data.
D. Use AWS Trusted Advisor checks to eliminate bias.

Answer: C

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
The best practice for mitigating bias in AI/ML models, according to AWS and responsible AI frameworks, is to ensure that the training data is representative and diverse. If a model demonstrates bias (such as favoring a particular demographic), the recommended, cost-effective approach is to collect additional data from underrepresented groups and retrain (fine-tune) the model with the improved dataset.
A . Include more diverse training data. Fine-tune the model again by using the new data:
"The most effective method to reduce model bias is to curate and include diverse, representative training data, then retrain or fine-tune the model." (Reference: AWS Responsible AI, SageMaker Clarify Bias Mitigation) B (RAG) is unrelated to model fairness or bias mitigation; it's for grounding LLMs with external knowledge.
C (AWS Trusted Advisor) is for AWS resource optimization/security-not for ML model bias detection or mitigation.
D (Pre-train a new LLM) would be extremely costly and is unnecessary; fine-tuning with better data is much more efficient.
Reference:
Responsible AI on AWS
Amazon SageMaker Clarify: Detecting and Mitigating Bias
AWS Certified AI Practitioner Exam Guide

NEW QUESTION # 186
A documentary filmmaker wants to reach more viewers. The filmmaker wants to automatically add subtitles and voice-overs in multiple languages to their films.
Which combination of steps will meet these requirements? (Select TWO.)

A. Use Amazon Textract to generate voice-overs in other languages
B. Use Amazon Textract and Amazon Translate to generate subtitles in other languages
C. Use Amazon Polly to generate voice-overs in other languages
D. Use Amazon Transcribe and Amazon Translate to generate subtitles in other languages
E. Use Amazon Translate to generate voice-overs in other languages

Answer: C,D

Explanation:
The correct answers are A and C because:
* Amazon Transcribe converts spoken dialogue from video into text (captions or subtitles).
* Amazon Translate translates that transcribed text into other languages.
* Amazon Polly can then convert the translated text into lifelike speech for voice-overs in different languages.
From AWS documentation:
"Amazon Transcribe is used to create accurate speech-to-text transcripts. You can feed this text into Amazon Translate to support multilingual subtitles. To generate audio output, Amazon Polly provides neural text-to- speech in multiple languages." Explanation of other options:
B). Amazon Textract is used for extracting text from documents/images, not audio or video, and is not applicable here.
D). Amazon Translate does not generate speech - it only translates text.
E). Amazon Textract does not generate audio.
Referenced AWS AI/ML Documents and Study Guides:
* Amazon Transcribe Developer Guide - Media Transcription
* Amazon Translate Developer Guide - Multilingual Support
* Amazon Polly Documentation - Text-to-Speech in Multiple Languages
* AWS ML Specialty Guide - Multimedia AI Workflows

NEW QUESTION # 187
A company is developing an ML model to predict customer churn.
Which evaluation metric will assess the model's performance on a binary classification task such as predicting chum?

A. Mean squared error (MSE)
B. F1 score
C. R-squared
D. Time used to train the model

Answer: B

Explanation:
The company is developing an ML model to predict customer churn, a binary classification task (churn or no churn). The F1 score is an evaluation metric that balances precision and recall, making it suitable for assessing the performance of binary classification models, especially when dealing with imbalanced datasets, which is common in churn prediction.
Exact Extract from AWS AI Documents:
From the Amazon SageMaker Developer Guide:
"The F1 score is a metric for evaluating binary classification models, combining precision and recall into a single value. It is particularly useful for tasks like churn prediction, where class imbalance may exist, ensuring the model performs well on both positive and negative classes." (Source: Amazon SageMaker Developer Guide, Model Evaluation Metrics) Detailed Option A: F1 scoreThis is the correct answer. The F1 score is ideal for binary classification tasks like churn prediction, as it measures the model's ability to correctly identify both churners and non-churners.
Option B: Mean squared error (MSE)MSE is used for regression tasks to measure the average squared difference between predicted and actual values, not for binary classification.
Option C: R-squaredR-squared is a metric for regression models, indicating how well the model explains the variability of the target variable. It is not applicable to classification tasks.
Option D: Time used to train the modelTraining time is not an evaluation metric for model performance; it measures the duration of training, not the model's accuracy or effectiveness.
Reference:
Amazon SageMaker Developer Guide: Model Evaluation Metrics (https://docs.aws.amazon.com/sagemaker/latest/dg/model-evaluation.html) AWS AI Practitioner Learning Path: Module on Model Performance and Evaluation AWS Documentation: Metrics for Classification (https://aws.amazon.com/machine-learning/)

NEW QUESTION # 188
How can companies use large language models (LLMs) securely on Amazon Bedrock?

A. Enable Amazon Bedrock automatic model evaluation jobs.
B. Design clear and specific prompts. Configure AWS Identity and Access Management (IAM) roles and policies by using least privilege access.
C. Use Amazon CloudWatch Logs to make models explainable and to monitor for bias.
D. Enable AWS Audit Manager for automatic model evaluation jobs.

Answer: B

NEW QUESTION # 189
A company is building a new generative AI chatbot. The chatbot uses an Amazon Bedrock foundation model (FM) to generate responses. During testing, the company notices that the chatbot is prone to prompt injection attacks.
What can the company do to secure the chatbot with the LEAST implementation effort?

A. Use Amazon Bedrock Guardrails content filters and denied topics.
B. Fine-tune the FM to avoid harmful responses.
C. Use chain-of-thought prompting to produce secure responses.
D. Change the FM to a more secure FM.

Answer: A

NEW QUESTION # 190
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