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  1. Home
  2. Amazon Certification
  3. AIF-C01 Exam
  4. Amazon.AIF-C01.v2025-08-07.q87 Dumps
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Question 36

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?

Correct Answer: B
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.
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Question 37

In which stage of the generative AI model lifecycle are tests performed to examine the model's accuracy?

Correct Answer: D
The evaluation stage of the generative AI model lifecycle involves testing the model to assess its performance, including accuracy, coherence, and other metrics. This stage ensures the model meets the desired quality standards before deployment.
Exact Extract from AWS AI Documents:
From the AWS AI Practitioner Learning Path:
"The evaluation phase in the machine learning lifecycle involves testing the model against validation or test datasets to measure its performance metrics, such as accuracy, precision, recall, or task-specific metrics for generative AI models." (Source: AWS AI Practitioner Learning Path, Module on Machine Learning Lifecycle) Detailed Explanation:
* Option A: DeploymentDeployment involves making the model available for use in production. While monitoring occurs post-deployment, accuracy testing is performed earlier in the evaluation stage.
* Option B: Data selectionData selection involves choosing and preparing data for training, not testing the model's accuracy.
* Option C: Fine-tuningFine-tuning adjusts a pre-trained model to improve performance for a specific task, but it is not the stage where accuracy is formally tested.
* Option D: EvaluationThis is the correct answer. The evaluation stage is where tests are conducted to examine the model's accuracy and other performance metrics, ensuring it meets requirements.
References:
AWS AI Practitioner Learning Path: Module on Machine Learning Lifecycle Amazon SageMaker Developer Guide: Model Evaluation (https://docs.aws.amazon.com/sagemaker/latest/dg
/model-evaluation.html)
AWS Documentation: Generative AI Lifecycle (https://aws.amazon.com/machine-learning/)
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Question 38

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?

Correct Answer: B
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Question 39

A digital devices company wants to predict customer demand for memory hardware. The company does not have coding experience or knowledge of ML algorithms and needs to develop a data-driven predictive model.
The company needs to perform analysis on internal data and external data.
Which solution will meet these requirements?

Correct Answer: D
Amazon SageMaker Canvas is a visual, no-code machine learning interface that allows users to build machine learning models without having any coding experience or knowledge of machine learning algorithms. It enables users to analyze internal and external data, and make predictions using a guided interface.
* Option D (Correct): "Import the data into Amazon SageMaker Canvas. Build ML models and demand forecast predictions by selecting the values in the data from SageMaker Canvas": This is the correct answer because SageMaker Canvas is designed for users without coding experience, providing a visual interface to build predictive models with ease.
* Option A: "Store the data in Amazon S3 and use SageMaker built-in algorithms" is incorrect because it requires coding knowledge to interact with SageMaker's built-in algorithms.
* Option B: "Import the data into Amazon SageMaker Data Wrangler" is incorrect. Data Wrangler is primarily for data preparation and not directly focused on creating ML models without coding.
* Option C: "Use Amazon Personalize Trending-Now recipe" is incorrect as Amazon Personalize is for building recommendation systems, not for general demand forecasting.
AWS AI Practitioner References:
* Amazon SageMaker Canvas Overview: AWS documentation emphasizes Canvas as a no-code solution for building machine learning models, suitable for business analysts and users with no coding experience.
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Question 40

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?

Correct Answer: B
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