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Amazon AWS Certified AI Practitioner Sample Questions (Q148-Q153):
NEW QUESTION # 148
A company wants to display the total sales for its top-selling products across various retail locations in the past 12 months.
Which AWS solution should the company use to automate the generation of graphs?
Answer: B
Explanation:
Amazon QuickSight is a fully managed business intelligence (BI) service that allows users to create and publish interactive dashboards that include visualizations like graphs, charts, and tables. "Amazon Q" is the natural language query feature within Amazon QuickSight. It enables users to ask questions about their data in natural language and receive visual responses such as graphs.
* Option C (Correct): "Amazon Q in Amazon QuickSight": This is the correct answer because Amazon QuickSight Q is specifically designed to allow users to explore their data through natural language queries, and it can automatically generate graphs to display sales data and other metrics. This makes it an ideal choice for the company to automate the generation of graphs showing total sales for its top-selling products across various retail locations.
* Option A, B, and D: These options are incorrect:
* A. Amazon Q in Amazon EC2: Amazon EC2 is a compute service that provides virtual servers, but it is not directly related to generating graphs or providing natural language querying features.
* B. Amazon Q Developer: This is not an existing AWS service or feature.
* D. Amazon Q in AWS Chatbot: AWS Chatbot is a service that integrates with Amazon Chime and Slack for monitoring and managing AWS resources, but it is not used for generating graphs based on sales data.
AWS AI Practitioner References:
* Amazon QuickSight Q is designed to provide insights from data by using natural language queries, making it a powerful tool for generating automated graphs and visualizations directly from queried data.
* Business Intelligence (BI) on AWS: AWS services such as Amazon QuickSight provide business intelligence capabilities, including automated reporting and visualization features, which are ideal for companies seeking to visualize data like sales trends over time.
NEW QUESTION # 149
An ML research team develops custom ML models. The model artifacts are shared with other teams for integration into products and services. The ML team retains the model training code and data. The ML team wants to builk a mechanism that the ML team can use to audit models.
Which solution should the ML team use when publishing the custom ML models?
Answer: D
Explanation:
The ML research team needs a mechanism to audit custom ML models while sharing model artifacts with other teams. Amazon SageMaker Model Cards provide a structured way todocument model details, including intended uses, training data, and inference performance, making them ideal for auditing and ensuring transparency when publishing models.
Exact Extract from AWS AI Documents:
From the Amazon SageMaker Developer Guide:
"Amazon SageMaker Model Cards enable you to document critical details about your machine learning models, such as intended uses, training data, evaluation metrics, and inference details. Model Cards support auditing by providing a centralized record that can be reviewed by teams to understand model behavior and limitations." (Source: Amazon SageMaker Developer Guide, SageMaker Model Cards) Detailed Explanation:
Option A: Create documents with the relevant information. Store the documents in Amazon S3.While storing documents in S3 is feasible, it lacks the structured format and integration with SageMaker that Model Cards provide, making it less suitable for auditing purposes.
Option B: Use AWS AI Service Cards for transparency and understanding models.AWS AI Service Cards are not a standard feature in AWS documentation. This option appears to be a distractor and is not a valid solution.
Option C: Create Amazon SageMaker Model Cards with Intended uses and training and inference details.This is the correct answer. SageMaker Model Cards are specifically designed to document model details for auditing, transparency, and collaboration, meeting the team's requirements.
Option D: Create model training scripts. Commit the model training scripts to a Git repository.Sharing training scripts in a Git repository provides access to code but does not offer a structured auditing mechanism for model details like intended uses or inference performance.
References:
Amazon SageMaker Developer Guide: SageMaker Model Cards (https://docs.aws.amazon.com/sagemaker
/latest/dg/model-cards.html)
AWS AI Practitioner Learning Path: Module on Model Governance and Auditing AWS Documentation: Responsible AI with SageMaker (https://aws.amazon.com/sagemaker/)
NEW QUESTION # 150
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.
Which strategy should the AI practitioner use?
Answer: A
NEW QUESTION # 151
An AI practitioner wants to use a foundation model (FM) to design a search application. The search application must handle queries that have text and images.
Which type of FM should the AI practitioner use to power the search application?
Answer: D
NEW QUESTION # 152
A company wants to make a chatbot to help customers. The chatbot will help solve technical problems without human intervention. The company chose a foundation model (FM) for the chatbot. The chatbot needs to produce responses that adhere to company tone.
Which solution meets these requirements?
Answer: C
Explanation:
Experimenting and refining the prompt is the best approach to ensure that the chatbot using a foundation model (FM) produces responses that adhere to the company's tone.
* Prompt Engineering:
* Adjusting and refining the prompt allows for better control over the FM's outputs, ensuring they align with the desired tone and style.
* This iterative process involves testing different prompts and modifying them based on the model's responses to achieve the desired outcome.
* Why Option C is Correct:
* Directly Influences Output Quality: Allows for fine-tuning of the model's responses to match the company's tone.
* Cost-Effective: Does not require modifying the model itself, only the inputs to it.
* Why Other Options are Incorrect:
* A. Low limit on tokens: Limits response length but not the adherence to company tone.
* B. Batch inferencing: Relates to processing multiple inputs, not controlling response tone.
* D. Higher temperature: Increases randomness in responses, which could deviate from the desired tone.
NEW QUESTION # 153
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