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Amazon AWS Certified AI Practitioner Sample Questions (Q118-Q123):

NEW QUESTION # 118
A company deploys a foundation model (FM). The company notices that the FM is producing answers to user- submitted questions about politics. The company wants to ensure that the model does not send answers to political questions to users.
Which AWS solution will meet this requirement?

Answer: A

Explanation:
The verified answer is A. Amazon Bedrock Guardrails . The requirement is to prevent a deployed foundation model from returning answers about a specific unwanted topic: politics. AWS documentation states that Amazon Bedrock Guardrails provides configurable safeguards to help you build safe generative AI applications . It also explains that guardrails provide safety and privacy controls across foundation models and help detect and filter undesirable content in user inputs and model responses. Most importantly for this question, AWS specifically identifies denied topics as a guardrail capability. With denied topics, you can define a set of topics that are undesirable in the context of your application, and the filter helps block them if they are detected in user queries or model responses. That exactly matches the scenario because the company wants political questions blocked before answers are sent to users.
Amazon Bedrock Agents is incorrect because agents are used to build applications that can reason, orchestrate tasks, call APIs, and connect to knowledge sources or action groups. Agents do not primarily exist to block topic categories in model input or output. Amazon SageMaker Clarify is incorrect because AWS describes SageMaker Clarify as a service for fairness, model explainability, feature attribution, and bias detection. It is useful for understanding model behavior and detecting bias, but it is not the correct control for filtering political responses from a generative AI application.
Amazon SageMaker Model Monitor is also incorrect because it monitors deployed model quality, data quality, bias drift, and feature attribution drift. Monitoring can identify problems after deployment, but it does not directly enforce real-time content filtering for foundation model responses. The question asks for a solution that ensures political answers are not sent to users. AWS Bedrock Guardrails is purpose-built for that control by applying safeguards to FM inputs and outputs.


NEW QUESTION # 119
A company wants to enhance response quality for a large language model (LLM) for complex problem- solving tasks. The tasks require detailed reasoning and a step-by-step explanation process.
Which prompt engineering technique meets these requirements?

Answer: C

Explanation:
The company wants to enhance the response quality of an LLM for complex problem-solving tasks requiring detailed reasoning and step-by-step explanations. Chain-of-thought prompting encourages the LLM to break down the problem into intermediate steps, providing a clear reasoning process before arriving at the final answer, which is ideal for this requirement.
Exact Extract from AWS AI Documents:
From the AWS Bedrock User Guide:
"Chain-of-thought prompting improves the reasoning capabilities of large language models by encouraging them to break down complex tasks into intermediate steps, providing a step-by-step explanation that leads to the final answer. This technique is particularly effective for problem-solving tasks requiring detailed reasoning." (Source: AWS Bedrock User Guide, Prompt Engineering Techniques) Detailed Explanation:
* Option A: Few-shot promptingFew-shot prompting provides a few examples to guide the LLM but does not explicitly encourage step-by-step reasoning or detailed explanations.
* Option B: Zero-shot promptingZero-shot prompting relies on the LLM's pre-trained knowledge without examples, making it less effective for complex tasks requiring detailed reasoning.
* Option C: Directional stimulus promptingDirectional stimulus prompting is not a standard technique in AWS documentation, likely a distractor, and does not address step-by-step reasoning.
* Option D: Chain-of-thought promptingThis is the correct answer. Chain-of-thought prompting enhances response quality for complex tasks by guiding the LLM to reason step-by-step, providing detailed explanations.
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 # 120
A company has documents that are missing some words because of a database error. The company wants to build an ML model that can suggest potential words to fill in the missing text.
Which type of model meets this requirement?

Answer: C

Explanation:
BERT-based models (Bidirectional Encoder Representations from Transformers) are suitable for tasks that involve understanding the context of words in a sentence and suggesting missing words. These models use bidirectional training, which considers the context from both directions (left and right of the missing word) to predict the appropriate word to fill in the gaps.
* BERT-based Models:
* BERT is a pre-trained transformer model designed for natural language understanding tasks, including text completion, where certain words are missing.
* It excels at understanding context and relationships between words in a sentence, making it ideal for suggesting potential words to fill in missing text.
* Why Option D is Correct:
* Contextual Understanding: BERT uses its bidirectional training to understand the context around missing words, making it highly accurate in suggesting suitable replacements.
* Text Completion Capability: BERT's architecture is explicitly designed for tasks like masked language modeling, where certain words in a text are masked (or missing), and the model predicts the missing words.
* Why Other Options are Incorrect:
* A. Topic modeling: Focuses on identifying topics in a text corpus, not on predicting missing words.
* B. Clustering models: Group similar data points together, which is not suitable for predicting missing text.
* C. Prescriptive ML models: Focus on providing recommendations based on data analysis, not on natural language processing tasks like filling in missing text.


NEW QUESTION # 121
A loan company is building a generative AI-based solution to offer new applicants discounts based on specific business criteri a. The company wants to build and use an AI model responsibly to minimize bias that could negatively affect some customers.
Which actions should the company take to meet these requirements? (Select TWO.)

Answer: A,B

Explanation:
To build an AI model responsibly and minimize bias, it is essential to ensure fairness and transparency throughout the model development and deployment process. This involves detecting and mitigating data imbalances and thoroughly evaluating the model's behavior to understand its impact on different groups.
Option A (Correct): "Detect imbalances or disparities in the data": This is correct because identifying and addressing data imbalances or disparities is a critical step in reducing bias. AWS provides tools like Amazon SageMaker Clarify to detect bias during data preprocessing and model training.
Option C (Correct): "Evaluate the model's behavior so that the company can provide transparency to stakeholders": This is correct because evaluating the model's behavior for fairness and accuracy is key to ensuring that stakeholders understand how the model makes decisions. Transparency is a crucial aspect of responsible AI.
Option B: "Ensure that the model runs frequently" is incorrect because the frequency of model runs does not address bias.
Option D: "Use the Recall-Oriented Understudy for Gisting Evaluation (ROUGE) technique to ensure that the model is 100% accurate" is incorrect because ROUGE is a metric for evaluating the quality of text summarization models, not for minimizing bias.
Option E: "Ensure that the model's inference time is within the accepted limits" is incorrect as it relates to performance, not bias reduction.
AWS AI Practitioner Reference:
Amazon SageMaker Clarify: AWS offers tools such as SageMaker Clarify for detecting bias in datasets and models, and for understanding model behavior to ensure fairness and transparency.
Responsible AI Practices: AWS promotes responsible AI by advocating for fairness, transparency, and inclusivity in model development and deployment.


NEW QUESTION # 122
A company manually reviews all submitted resumes in PDF format. As the company grows, the company expects the volume of resumes to exceed the company's review capacity. The company needs an automated system to convert the PDF resumes into plain text format for additional processing.
Which AWS service meets this requirement?

Answer: D

Explanation:
Amazon Textract is a service that automatically extracts text and data from scanned documents, including PDFs. It is the best choice for converting resumes from PDF format to plain text for further processing.
* Amazon Textract:
* Extracts text, forms, and tables from scanned documents accurately.
* Ideal for automating the process of converting PDF resumes into plain text format.
* Why Option A is Correct:
* Automation of Text Extraction: Textract is designed to handle large volumes of documents and convert them into machine-readable text, perfect for the company's need.
* Scalability and Efficiency: Supports scalability to handle a growing volume of resumes as the company expands.
* Why Other Options are Incorrect:
* B. Amazon Personalize: Used for creating personalized recommendations, not for text extraction.
* C. Amazon Lex: A service for building conversational interfaces, not for processing documents.
* D. Amazon Transcribe: Used for converting speech to text, not for extracting text from documents.


NEW QUESTION # 123
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