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| Section | Objectives |
|---|---|
| Fundamentals of Generative AI | - Foundation models and prompt engineering basics - AWS generative AI services overview (e.g., Amazon Bedrock) - Large language models (LLMs) concepts |
| Fundamentals of Artificial Intelligence and Machine Learning | - Core AI and ML concepts - Common ML workflows and lifecycle - Data fundamentals for AI/ML |
| Applications of Foundation Models | - Content generation and summarization - Use cases for generative AI in business - AI-powered assistants and automation |
| Responsible AI and Security | - Security, privacy, and governance in AI systems - AI ethics and responsible use - Bias, fairness, and explainability |
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NEW QUESTION # 350
A company wants to build an ML model to detect abnormal patterns in sensor dat a. The company does not have labeled data for training. Which ML method will meet these requirements?
Answer: A
Explanation:
The correct answer is D because autoencoders are an unsupervised machine learning method commonly used for anomaly detection when labeled data is not available.
From AWS documentation:
"Autoencoders learn to compress and reconstruct input data. During anomaly detection, they learn normal patterns in data. Data points that the model cannot accurately reconstruct are flagged as anomalies." This approach is ideal when there is no labeled data and when patterns must be learned based on normal behavior alone - a common situation in IoT sensor data environments.
Explanation of other options:
A . Linear regression requires labeled data and is used for predicting continuous values.
B . Classification requires labeled data to assign inputs into categories.
C . Decision trees are supervised learning models and also require labeled datasets.
Referenced AWS AI/ML Documents and Study Guides:
AWS Machine Learning Specialty Guide - Unsupervised Learning Techniques Amazon SageMaker Examples - Anomaly Detection Using Autoencoders
NEW QUESTION # 351
An AI practitioner is using an Amazon Bedrock base model to summarize session chats from the customer service department. The AI practitioner wants to store invocation logs to monitor model input and output data.
Answer: D
Explanation:
The correct answer is B - Enable model invocation logging in Amazon Bedrock. AWS Bedrock provides native functionality to log all model invocations, including input prompts, parameters, and generated outputs, to Amazon CloudWatch Logs or S3. According to AWS documentation, this feature helps developers monitor model usage, analyze errors, and audit for compliance. Unlike CloudTrail or Audit Manager, which record API events and compliance data respectively, invocation logging captures real inference transactions. EventBridge is used for event routing, not persistent log storage. Bedrock's logging feature ensures complete traceability for debugging, usage analytics, and governance in accordance with Responsible AI principles.
Referenced AWS AI/ML Documents and Study Guides:
Amazon Bedrock Developer Guide - Model Invocation Logging
AWS Responsible AI Documentation - Monitoring and Auditability
NEW QUESTION # 352
A company needs to automate recurring compliance assessments for its AI workloads. The assessments must include documented evidence mapped to regulatory frameworks.
Which AWS service meets these requirements?
Answer: A
Explanation:
The verified answer is A. AWS Audit Manager. The requirement is recurring compliance assessments with documented evidence mapped to regulatory frameworks. AWS documentation states that AWS Audit Manager helps customers continually audit AWS usage to simplify risk and compliance management. It automates evidence collection so customers can assess whether policies, procedures, and controls are operating effectively. AWS also states that Audit Manager provides prebuilt frameworks that structure and automate assessments for compliance standards and regulations.
The key phrase is "documented evidence mapped to regulatory frameworks." AWS Audit Manager creates assessments from frameworks, automatically runs resource assessments, collects data from in-scope AWS accounts, transforms that data into audit-friendly evidence, and attaches it to relevant controls. That is the exact service function being tested. AWS also explains that Audit Manager can generate reports for auditors and supports prebuilt frameworks for standards such as PCI DSS, GDPR, HIPAA, SOC 2, and AWS operational best practices.
AWS Trusted Advisor is incorrect because Trusted Advisor provides recommendations for cost optimization, performance, security, fault tolerance, service quotas, and operational excellence. It is not a compliance- evidence automation service.
AWS Secrets Manager is incorrect because it stores, rotates, and manages secrets such as database credentials and API keys. It does not automate compliance assessments.
Amazon Inspector is incorrect because it scans workloads for software vulnerabilities and unintended network exposure. It is security-focused, but it does not create recurring evidence-mapped compliance assessments.
Therefore, the correct service is AWS Audit Manager.
NEW QUESTION # 353
A company is using a large language model (LLM) on Amazon Bedrock to build a chatbot. The chatbot processes customer support requests. To resolve a request, the customer and the chatbot must interact a few times.
Which solution gives the LLM the ability to use content from previous customer messages?
Answer: B
Explanation:
The company is building a chatbot using an LLM on Amazon Bedrock, and the chatbot needs to use content from previous customer messages to resolve requests. Adding previous messages to the model prompt (also known as providing conversation history) enables the LLM to maintain context across interactions, allowing it to respond coherently based on the ongoing conversation.
Exact Extract from AWS AI Documents:
From the AWS Bedrock User Guide:
"To enable a large language model (LLM) to maintain context in a conversation, you can include previous messages in the model prompt. This approach, often referred to as providing conversation history, allows the LLM to generate responses that are contextually relevant toprior interactions." (Source: AWS Bedrock User Guide, Building Conversational Applications) Detailed Option A: Turn on model invocation logging to collect messages.Model invocation logging records interactions for auditing or debugging but does not provide the LLM with access to previous messages during inference to maintain conversation context.
Option B: Add messages to the model prompt.This is the correct answer. Including previous messages in the prompt gives the LLM the conversation history it needs to respond appropriately, a common practice for chatbots on Amazon Bedrock.
Option C: Use Amazon Personalize to save conversation history.Amazon Personalize is for building recommendation systems, not for managing conversation history in a chatbot. This option is irrelevant.
Option D: Use Provisioned Throughput for the LLM.Provisioned Throughput in Amazon Bedrock ensures consistent performance for model inference but does not address the need to use previous messages in the conversation.
Reference:
AWS Bedrock User Guide: Building Conversational Applications (https://docs.aws.amazon.com/bedrock/latest/userguide/conversational-apps.html) AWS AI Practitioner Learning Path: Module on Generative AI and Chatbots Amazon Bedrock Developer Guide: Managing Conversation Context (https://aws.amazon.com/bedrock/)
NEW QUESTION # 354
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: B
Explanation:
Comprehensive and Detailed
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 # 355
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