Free AIF-C01 Exam Braindumps
AWS Certified AI Practitioner (page: 4)

Updated On: 19-Oct-2026

A company runs an application on servers in an Amazon VPC. The company’s application uses Amazon
Bedrock APIs for AI features. The company does not want API calls to travel across the public internet.
Which solution will meet this requirement?

  1. Use AWS PrivateLink to establish a private connection between the Amazon VPC and Amazon Bedrock.
  2. Sign API requests by using an access key ID and a secret access key that is associated with an IAM
    principal.
  3. Move the application to an on-premises server. Make API calls to the public endpoint of Amazon Bedrock.
  4. Encrypt the data in transit from clients to the APIs that have been implemented in the Amazon VPC. Encrypt the data in transit for API calls from the Amazon VPC to Amazon Bedrock.

Answer(s): A

Explanation:

AWS PrivateLink lets the application in the Amazon VPC connect privately to Amazon Bedrock through interface VPC endpoints, so the API traffic does not traverse the public internet.



Which technique can a company use to lower bias and toxicity in generative AI applications during the post-processing ML lifecycle?

  1. Human-in-the-loop
  2. Data augmentation
  3. Feature engineering
  4. Adversarial training

Answer(s): A

Explanation:

Human-in-the-loop (HITL) is a technique where human oversight is involved in the decision-making process of AI systems. In the context of generative AI applications, HITL can be used during the post-processing phase to identify and mitigate biases or toxic outputs. Humans can review and intervene when the model generates inappropriate or biased content, providing corrections or adjustments that help reduce the likelihood of toxicity and bias. This feedback loop helps refine and improve the model’s outputs over time.



A company is developing an ML model to predict customer churn.
Which evaluation metric will assess the model's performance on a binary classification task such as predicting churn?

  1. F1 score
  2. Mean squared error (MSE)
  3. R-squared
  4. Time used to train the model

Answer(s): A

Explanation:

For binary classification tasks like churn prediction, the F1 score balances precision and recall into a single metric, making it ideal for evaluating how well the model identifies churners without over- or under-predicting.



A company stores customer data in OpenSearch. The company wants an AI solution to retrieve specific customer information from the stored data. The AI solution must convert queries into data requests and generate CSV files from the results. Then, the AI solution must upload the CSV files to Amazon S3.
Which solution will meet these requirements in the MOST operationally-efficient way?

  1. Create an AI agent to perform the required steps.
  2. Use a single foundation model (FM) with few-shot prompting.
  3. Create a software application without using AI to perform the required steps.
  4. Train a decision tree model to generate a solution based on user questions.

Answer(s): A

Explanation:

An AI agent can orchestrate multiple actions end to end, including translating user queries into OpenSearch requests, generating CSV files from the results, and uploading the files to Amazon S3, making it the most operationally efficient approach.



A large retailer receives thousands of customer support inquiries about products every day. The customer support inquiries need to be processed 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?

  1. Generation of custom foundation models (FMs) to predict customer needs
  2. Automation of repetitive tasks and orchestration of complex workflows
  3. Automatically calling multiple foundation models (FMs) and consolidating the results
  4. Selecting the foundation model (FM) based on predefined criteria and metrics

Answer(s): B

Explanation:

Amazon Bedrock agents help automate repetitive tasks and orchestrate complex workflows, which is ideal for handling thousands of customer support inquiries efficiently. This helps reduce response times and improves productivity. The other options do not directly address automation and orchestration of tasks for customer support.



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