Practice Free AIF-C01 Exam Online Questions – Page 9

Question #81

A company needs to train an ML model to classify images of different types of animals. The company has a large dataset of labeled images and will not label more data.

Which type of learning should the company use to train the model?

A . Supervised learning.
B . Unsupervised learning.
C . Reinforcement learning.
D . Active learning.

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Question #82

A company is using an Amazon Bedrock base model to summarize documents for an internal use case. The company trained a custom model to improve the summarization quality.

Which action must the company take to use the custom model through Amazon Bedrock?

A . Purchase Provisioned Throughput for the custom model.
B . Deploy the custom model in an Amazon SageMaker endpoint for real-time inference.
C . Register the model with the Amazon SageMaker Model Registry.
D . Grant access to the custom model in Amazon Bedrock.

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Question #83

HOTSPOT

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

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Correct Answer:

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

Step 1: Define business goal and frame ML problem This 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 model After 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 model Once 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 model After 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.

Reference: 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/)

Question #84

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.

A . Use a rule-based system instead of an ML model.
B . Apply explainable AI techniques to show customers which factors influenced the model’s decision.
C . Develop an interactive UI for customers and provide clear technical explanations about the system.
D . Increase the accuracy of the model to reduce the need for transparency.

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Question #85

A social media company wants to use a large language model (LLM) to summarize messages. The company has chosen a few LLMs that are available on Amazon SageMaker JumpStart. The company wants to compare the generated output toxicity of these models.

Which strategy gives the company the ability to evaluate the LLMs with the LEAST operational overhead?

A . Crowd-sourced evaluation
B . Automatic model evaluation
C . Model evaluation with human workers
D . Reinforcement learning from human feedback (RLHF)

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Question #86

A company has set up a translation tool to help its customer service team handle issues from customers around the world. The company wants to evaluate the performance of the translation tool. The company sets up a parallel data process that compares the responses from the tool to responses from actual humans. Both sets of responses are generated on the same set of documents.

Which strategy should the company use to evaluate the translation tool?

A . Use the Bilingual Evaluation Understudy (BLEU) score to estimate the absolute translation quality of the two methods.
B . Use the Bilingual Evaluation Understudy (BLEU) score to estimate the relative translation quality of the two methods.
C . Use the BERT Score to estimate the absolute translation quality of the two methods.
D . Use the BERT Score to estimate the relative translation quality of the two methods.

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Question #87

A company needs to automate recurring compliance assessments for its AI workloads. The assessments must include documented evidence mapped to regulatory frameworks.

Which AWS service meets these requirements?

A . AWS Audit Manager
B . AWS Trusted Advisor
C . AWS Secrets Manager
D . Amazon Inspector

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Question #88

What does inference refer to in the context of AI?

A . The process of creating new AI algorithms
B . The use of a trained model to make predictions or decisions on unseen data
C . The process of combining multiple AI models into one model
D . The method of collecting training data for AI systems

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Question #89

A company is building a generative AI application with a foundation model (FM). The application needs to automatically generate marketing emails. The company wants the application’s output text to be creative and short in length.

Which configuration of inference parameters will meet these requirements?

A . Decrease the temperature and the response length.
B . Increase the temperature and the response length.
C . Increase the temperature and decrease the response length.
D . Decrease the temperature and increase the response length.

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Question #90

HOTSPOT

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

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