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| Section | Objectives |
|---|---|
| Fundamentals of Generative AI | - Large language models (LLMs) concepts - AWS generative AI services overview (e.g., Amazon Bedrock) - Foundation models and prompt engineering basics |
| 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 | - AI-powered assistants and automation - Use cases for generative AI in business - Content generation and summarization |
| Responsible AI and Security | - Security, privacy, and governance in AI systems - Bias, fairness, and explainability - AI ethics and responsible use |
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質問 # 407
A company wants to group its customer base to understand different customer groups. The company has an unlabeled dataset that includes customer demographics, purchase history, and browsing behavior.
Which ML technique will meet these requirements?
正解：A
解説：
Clustering is an unsupervised machine learning technique used to group data points based on similarity without requiring labeled data. AWS documentation explains that clustering is commonly used for customer segmentation, where the goal is to discover natural groupings within a dataset.
In this scenario, the dataset is unlabeled and includes behavioral and demographic features. Clustering algorithms analyze patterns and distances between data points to identify distinct customer groups, making this technique ideal for understanding customer segments.
Regression and classification require labeled outputs, and reinforcement learning focuses on sequential decision-making, not grouping. AWS positions clustering as the correct approach for this use case.
質問 # 408
A large retailer receives thousands of customer support inquiries about products every day. The customer support inquiries need to be processed and responded to quickly. The company wants to implement Agents for Amazon Bedrock.
What are the key benefits of using Amazon Bedrock agents that could help this retailer?
正解：A
解説：
Amazon Bedrock Agents provide the capability to automate repetitive tasks and orchestrate complex workflows using generative AI models. This is particularly beneficial for customer support inquiries, where quick and efficient processing is crucial.
Option B (Correct): "Automation of repetitive tasks and orchestration of complex workflows": This is the correct answer because Bedrock Agents can automate common customer service tasks and streamline complex processes, improving response times and efficiency.
Option A: "Generation of custom foundation models (FMs) to predict customer needs" is incorrect as Bedrock agents do not create custom models.
Option C: "Automatically calling multiple foundation models (FMs) and consolidating the results" is incorrect because Bedrock agents focus on task automation rather than combining model outputs.
Option D: "Selecting the foundation model (FM) based on predefined criteria and metrics" is incorrect as Bedrock agents are not designed for selecting models.
AWS AI Practitioner References:
Amazon Bedrock Documentation: AWS explains that Bedrock Agents automate tasks and manage complex workflows, making them ideal for customer support automation.
質問 # 409
A company needs to choose a model from Amazon Bedrock to use internally. The company must identify a model that generates responses in a style that the company's employees prefer.
What should the company do to meet these requirements?
正解：C
解説：
To determine which model generates responses in a style that the company's employees prefer, the best approach is to use a human workforce to evaluate the models with custom prompt datasets. This method allows for subjective evaluation based on the specific stylistic preferences of the company's employees, which cannot be effectively assessed through automated methods or pre-built datasets.
* Option B (Correct): "Evaluate the models by using a human workforce and custom prompt datasets": This is the correct answer as it directly involves human judgment to evaluate the style and quality of the responses, aligning with employee preferences.
* Option A: "Evaluate the models by using built-in prompt datasets" is incorrect because built-in datasets may not capture the company's specific stylistic requirements.
* Option C: "Use public model leaderboards to identify the model" is incorrect as leaderboards typically measure model performance on standard benchmarks, not on stylistic preferences.
* Option D: "Use the model InvocationLatency runtime metrics in Amazon CloudWatch" is incorrect because latency metrics do not provide any information about the style of the model's responses.
AWS AI Practitioner References:
* Model Evaluation Techniques on AWS: AWS suggests using human evaluators to assess qualitative aspects of model outputs, such as style and tone, to ensure alignment with organizational preferences
質問 # 410
Which AWS service creates business intelligence reports and automatically generates executive summaries based on data that users provide?
正解：A
解説：
The verified answer is A. Amazon Q in QuickSight . AWS documentation describes Amazon Q in QuickSight as part of QuickSight's generative business intelligence capability. AWS states that with Amazon Quick chat, users can use the Generative BI authoring experience, create executive summaries of data , ask and answer questions of data, and generate data stories. This directly matches the question because the required service must create business intelligence reports and automatically generate executive summaries from user-provided data.
AWS documentation also explains that dashboard readers can generate executive summaries that provide a summary of all insights generated for a dashboard. These executive summaries help readers find key insights and information about a dashboard at a glance. In another QuickSight documentation page, AWS states that large language models can be used to generate executive summaries of dashboards, and that executive summaries are based on QuickSight's suggested insights. This confirms that the service is Amazon Q in QuickSight, not a general-purpose AI service.
Amazon Rekognition is incorrect because it is a computer vision service used for image and video analysis, such as object detection, facial analysis, and content moderation. It is not a business intelligence reporting or executive-summary service. Amazon Textract is incorrect because it extracts text, handwriting, tables, and forms from documents. While Textract can help process documents, it does not create BI reports or generate executive summaries from dashboard data. Amazon Polly is incorrect because it converts text into lifelike speech. Polly is a speech synthesis service, not a BI analytics or reporting service.
Therefore, the only option that fits both parts of the requirement-business intelligence reporting and automatically generated executive summaries-is Amazon Q in QuickSight .
質問 # 411
Which use case is MOST suitable for a binary classification model?
正解：A
解説：
Binary classification is a supervised machine learning technique that categorizes input data into exactly two predefined classes.
Spam email detection is a classic binary classification use case because the model determines whether an incoming email belongs to the spam or non-spam category.
The model learns from labeled training examples containing email characteristics, such as sender information, message content, suspicious links, and linguistic patterns.
AWS machine learning services, including Amazon SageMaker AI, support classification algorithms that can be trained for these tasks.
Option A describes a recommendation system that identifies relevant products. Option B generally involves regression or pricing optimization, where the predicted output may be a continuous numerical value. Option D describes targeted marketing, which can involve customer segmentation or predictive analytics.
Therefore, detecting spam emails is the most appropriate binary classification use case.
質問 # 412
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