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| Section | Objectives |
|---|---|
| Applications of Foundation Models | - Content generation and summarization - AI-powered assistants and automation - Use cases for generative AI in business |
| Fundamentals of Generative AI | - Large language models (LLMs) concepts - Foundation models and prompt engineering basics - AWS generative AI services overview (e.g., Amazon Bedrock) |
| Responsible AI and Security | - Security, privacy, and governance in AI systems - Bias, fairness, and explainability - AI ethics and responsible use |
| Fundamentals of Artificial Intelligence and Machine Learning | - Core AI and ML concepts - Common ML workflows and lifecycle - Data fundamentals for AI/ML |
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NEW QUESTION # 408
Which AWS service creates business intelligence reports and automatically generates executive summaries based on data that users provide?
Answer: A
Explanation:
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 .
NEW QUESTION # 409
A company is building a generative Al application and is reviewing foundation models (FMs). The company needs to consider multiple FM characteristics.
Select the correct FM characteristic from the following list for each definition. Each FM characteristic should be selected one time. (Select THREE.) Concurrency Context windows Latency
Answer:
Explanation:
Explanation:
AWS References:
Amazon Bedrock - Model parameters and context window
AWS ML Inference - Latency and Throughput
AWS Scalability - Concurrency
NEW QUESTION # 410
An AI company periodically evaluates its systems and processes with the help of independent software vendors (ISVs). The company needs to receive email message notifications when an ISV's compliance reports become available.
Which AWS service meets this requirement?
Answer: C
Explanation:
The correct answer is AWS Artifact, which is a central resource for accessing AWS compliance documentation, audit reports, and certifications from both AWS and third-party providers, including ISVs. According to AWS documentation, Artifact supports notifications for newly available reports. Customers can subscribe to notification emails when specific compliance documents or updates are published. This feature is ideal for regulated industries or companies needing third-party verification of compliance and security postures. Artifact provides access to reports such as SOC 2, ISO, PCI, and more, and is commonly used for audit preparation and vendor risk management. In contrast, AWS Audit Manager helps create internal audit frameworks but is not focused on third-party report distribution. AWS Data Exchange is for marketplace data sharing, and Trusted Advisor provides performance and cost optimization recommendations, not compliance document alerts. Thus, AWS Artifact is the purpose-built service that meets this requirement.
Referenced AWS AI/ML Documents and Study Guides:
AWS Artifact Documentation - Compliance Reports and Subscriptions
AWS Security and Compliance Whitepaper - Using Artifact for Third-Party Assurance
NEW QUESTION # 411
A company is developing a new image classification model by using a dataset of photos. The dataset must follow the AWS principles of responsible AI.
Which characteristics should the dataset have to meet this requirement?
Answer: B
Explanation:
Comprehensive and Detailed Explanation (AWS AI documents):
AWS Responsible AI principles stress the importance of fairness, robustness, and accountability, which begin with the quality of the data used to train models. A responsible dataset should:
Be diverse, representing different populations, environments, and conditions to reduce bias Be sourced from reputable and appropriate sources, ensuring data quality and ethical use Have balanced categories, preventing the model from favoring one class over others and reducing the risk of discriminatory outcomes These characteristics help ensure that the resulting image classification model behaves fairly, performs reliably across groups, and aligns with AWS Responsible AI best practices.
Why the other options are incorrect:
B focuses on dataset size rather than quality, balance, or representativeness.
C increases the risk of bias and poor generalization.
D limits diversity and does not inherently ensure fairness or accountability.
AWS AI Study Guide Reference:
AWS Responsible AI principles: fairness and data quality
AWS guidance on dataset selection and preparation for responsible ML
NEW QUESTION # 412
Sated and order the steps from the following bat to correctly describe the ML Lifecycle for a new custom modal Select each step one time. (Select and order FOUR.)
* Define the business objective.
* Deploy the modal.
* Develop and tram the model.
* Process the data.
Answer:
Explanation:
Step 1: Define the business objective.
Step 2: Process the data.
Step 3: Develop and train the model.
Step 4: Deploy the model.
The correct order represents the machine learning lifecycle as defined by AWS in the Amazon SageMaker documentation and AWS Certified Machine Learning Specialty Study Guide. The lifecycle describes the sequence of tasks required to build, train, and deploy a custom ML model effectively.
From AWS documentation:
"The machine learning process begins with defining the business problem, followed by collecting and processing data, developing and training models, and finally deploying them into production for inference." Step 1 - Define the business objective:
This step involves clearly identifying the business problem to be solved and determining the measurable outcomes expected from the ML model. This ensures alignment between business goals and ML outputs.
Step 2 - Process the data:
Data is collected, cleaned, transformed, and prepared for training. This includes handling missing values, normalizing data, and performing feature engineering - a crucial phase that influences model performance.
Step 3 - Develop and train the model:
The model is built and trained on the processed data using algorithms appropriate to the problem (e.g., regression, classification, clustering). Hyperparameters are tuned to optimize model accuracy.
Step 4 - Deploy the model:
Once validated, the model is deployed to a production environment (e.g., Amazon SageMaker endpoint) to make predictions on new data. Continuous monitoring and retraining ensure the model remains effective.
Referenced AWS AI/ML Documents and Study Guides:
Amazon SageMaker Developer Guide - Machine Learning Lifecycle
AWS Certified Machine Learning Specialty Study Guide - Model Development Lifecycle AWS ML Best Practices Whitepaper - End-to-End ML Workflow
NEW QUESTION # 413
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