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NEW QUESTION # 251
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 # 252
A company has developed an ML model for image classification. The company wants to deploy the model to production so that a web application can use the model.
The company needs to implement a solution to host the model and serve predictions without managing any of the underlying infrastructure.
Which solution will meet these requirements?
Answer: A
NEW QUESTION # 253
A financial company uses a generative AI model to assign credit limits to new customers. The company wants to make the decision-making process of the model more transparent to its customers.
Answer: C
Explanation:
According to the AWS Certified AI Practitioner documentation, explainable AI (XAI) refers to methods and techniques that make the behavior and predictions of machine learning models more understandable and transparent to users and stakeholders. In financial use cases, especially when decisions such as credit limits are made, regulatory and ethical concerns demand transparency about how such decisions are reached.
* Option B is correct because applying explainable AI techniques (such as SHAP, LIME, or Amazon SageMaker Clarify) allows organizations to provide customers with clear insights into which data points or factors contributed to the model's decision. This aligns with best practices for responsible AI as defined in the AWS documentation, which states:
"Explainable AI increases transparency and trust in machine learning applications by helping users and regulators understand the decision process behind model predictions." (Reference: AWS AI/ML Best Practices - Explainable AI, AWS AI Practitioner Exam Guide)
* Option A suggests switching to a rule-based system, which is not practical for complex problems addressed by generative AI and may reduce model performance.
* Option C (just a UI) does not inherently provide transparency into the model's reasoning, unless paired with explainability techniques.
* Option D (accuracy over transparency) does not address the company's requirement for transparency.
References:
AWS Certified AI Practitioner Exam Guide
Amazon SageMaker Clarify Documentation
NEW QUESTION # 254
A company wants to collaborate with several research institutes to develop an AI model. The company needs standardized documentation of model version tracking and a record of model development.
Which solution meets these requirements?
Answer: B
NEW QUESTION # 255
A company is using Amazon Bedrock to generate images. The images must not contain specific attributes.
Which prompt engineering technique will meet this requirement?
Answer: C
Explanation:
Negative prompting is a prompt engineering technique that specifies elements, characteristics, or attributes that should be excluded from generated content.
In image generation, negative prompts help guide the model away from producing unwanted objects, visual features, or stylistic characteristics.
For example, a company generating promotional images could specify negative prompt attributes such as watermarks, text overlays, distorted objects, or unwanted backgrounds.
Amazon Bedrock supports image generation models that provide negative prompting capabilities. Certain Stability AI image models expose a negative_prompt parameter for specifying undesirable visual elements.
Zero-shot prompting involves asking a model to perform a task without providing examples. Experimentation prompting and discovery prompting do not specifically describe excluding image attributes.
Negative prompting is therefore the appropriate technique, although generated images should still undergo validation because negative instructions cannot guarantee complete exclusion.
NEW QUESTION # 256
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