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NEW QUESTION # 76
An ecommerce company is developing a generative Al solution to create personalized product recommendations for its application users. The company wants to track how effectively the Al solution increases product sales and user engagement in the application.
Select the correct business metric from the following list for each business goal. Each business metric should be selected one time. (Select THREE.) Average order value (AOV) Click-through rate (CTR) Retention rate
Answer:
Explanation:
NEW QUESTION # 77
A company is building an ML model. The company collected new data and analyzed the data by creating a correlation matrix, calculating statistics, and visualizing the data.
Which stage of the ML pipeline is the company currently in?
Answer: B
Explanation:
Exploratory data analysis (EDA) involves understanding the data by visualizing it, calculating statistics, and creating correlation matrices. This stage helps identify patterns, relationships, and anomalies in the data, which can guide further steps in the ML pipeline.
Option C (Correct): "Exploratory data analysis": This is the correct answer as the tasks described (correlation matrix, calculating statistics, visualizing data) are all part of the EDA process.
Option A: "Data pre-processing" is incorrect because it involves cleaning and transforming data, not initial analysis.
Option B: "Feature engineering" is incorrect because it involves creating new features from raw data, not analyzing the data's existing structure.
Option D: "Hyperparameter tuning" is incorrect because it refers to optimizing model parameters, not analyzing the data.
AWS AI Practitioner Reference:
Stages of the Machine Learning Pipeline: AWS outlines EDA as the initial phase of understanding and exploring data before moving to more specific preprocessing, feature engineering, and model training stages.
NEW QUESTION # 78
A company wants to build an ML application.
Select and order the correct steps from the following list to develop a well-architected ML workload. Each step should be selected one time. (Select and order FOUR.)
* Deploy model
* Develop model
* Monitor model
* Define business goal and frame ML problem
Answer:
Explanation:
Explanation:
Building a well-architected ML workload follows a structured lifecycle as outlined in AWS best practices.
The process begins with defining the business goal and framing the ML problem to ensure the project aligns with organizational objectives. Next, the model is developed, which includes data preparation, training, and evaluation. Once the model is ready, it is deployed tomake predictions in a production environment. Finally, the model is monitored to ensure it performs as expected and to address any issues like drift or degradation over time. This order ensures a systematic approach to ML development.
Exact Extract from AWS AI Documents:
From the AWS AI Practitioner Learning Path:
" The machine learning lifecycle typically follows these stages: 1) Define the business goal and frame the ML problem, 2) Develop the model (including data preparation, training, and evaluation), 3) Deploy the model to production, and 4) Monitor the model for performance and drift to ensure it continues to meet business needs.
"
(Source: AWS AI Practitioner Learning Path, Module on Machine Learning Lifecycle) Detailed Explanation:
Step 1: Define business goal and frame ML problemThis is the first step in any ML project. It involves understanding the business objective (e.g., reducing churn) and framing the ML problem (e.g., classification or regression). Without this step, the project lacks direction. The hotspot lists this option as " Define business goal and frame ML problem, " which matches this stage.
Step 2: Develop modelAfter defining the problem, the next step is to develop the model. This includes collecting and preparing data, selecting an algorithm, training the model, and evaluating its performance. The hotspot lists " Develop model " as an option, aligning with this stage.
Step 3: Deploy modelOnce the model is developed and meets performance requirements, it is deployed to a production environment to make predictions or automate decisions. The hotspot includes " Deploy model " as an option, which fits this stage.
Step 4: Monitor modelAfter deployment, the model must be monitored to ensure it performs well over time, addressing issues like data drift or performance degradation. The hotspot lists " Monitor model " as an option, completing the lifecycle.
Hotspot Selection Analysis:
The hotspot provides four steps, each with the same dropdown options: " Select..., " " Deploy model, " " Develop model, " " Monitor model, " and " Define business goal and frame ML problem. " The correct selections are:
Step 1: Define business goal and frame ML problem
Step 2: Develop model
Step 3: Deploy model
Step 4: Monitor model
Each option is used exactly once, as required, and follows the logical order of the ML lifecycle.
References:
AWS AI Practitioner Learning Path: Module on Machine Learning Lifecycle Amazon SageMaker Developer Guide: Machine Learning Workflow (https://docs.aws.amazon.com/sagemaker
/latest/dg/how-it-works-mlconcepts.html)
AWS Well-Architected Framework: Machine Learning Lens (https://docs.aws.amazon.com/wellarchitected
/latest/machine-learning-lens/)
NEW QUESTION # 79
A hospital developed an AI system to provide personalized treatment recommendations for patients. The AI system must provide the rationale behind the recommendations and make the insights accessible to doctors and patients.
Which human-centered design principle does this scenario present?
Answer: A
NEW QUESTION # 80
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?
Answer: B
Explanation:
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.
NEW QUESTION # 81
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