Free AIF-C01 Exam Braindumps
AWS Certified AI Practitioner (page: 8)

Updated On: 19-Oct-2026

A large retail bank wants to develop an ML system to help the risk management team decide on loan allocations for different demographics.
What must the bank do to develop an unbiased ML model?

  1. Reduce the size of the training dataset.
  2. Ensure that the ML model predictions are consistent with historical results.
  3. Create a different ML model for each demographic group.
  4. Measure class imbalance on the training dataset. Adapt the training process accordingly.

Answer(s): D

Explanation:

In machine learning, class imbalance occurs when certain classes are underrepresented in the training dataset,
leading to biased model predictions. To develop an unbiased model, it's crucial to assess the class distribution and adjust the training process to address any imbalances. This can be achieved through techniques such as oversampling the minority class, undersampling the majority class, or applying class weights to ensure the model treats all classes equitably.



A company needs to log all requests made to its Amazon Bedrock API. The company must retain the logs securely for 5 years at the lowest possible cost.
Which combination of AWS service and storage class meets these requirements? (Choose two.)

  1. AWS CloudTrail
  2. Amazon CloudWatch
  3. AWS Audit Manager
  4. Amazon S3 Intelligent-Tiering
  5. Amazon S3 Standard

Answer(s): A,D

Explanation:

AWS CloudTrail is the AWS service designed for logging and monitoring API calls made to AWS services,
including Amazon Bedrock. CloudTrail records detailed information about the API requests, including the identity of the requester, the time of the request, and the source IP address. This service is ideal for logging all requests made to the Amazon Bedrock API and meets the logging requirement.
Amazon S3 Intelligent-Tiering is a storage class designed for storing data that has unpredictable access patterns. It automatically moves data between two access tiers (frequent and infrequent) based on usage,
which helps reduce costs while ensuring data is still available when needed. For retaining logs securely over 5
years at the lowest possible cost, this storage class provides an efficient way to handle long-term storage requirements without incurring unnecessary costs.



A company is evaluating several large language models (LLMs) for a text summarization task. The company needs to select a metric to evaluate the quality of the summaries that the LLMs generate.
Which metric will meet this requirement?

  1. Recall
  2. Area under the ROC curve (AUC)
  3. Recall-Oriented Understudy for Gisting Evaluation (ROUGE)
  4. Mean squared error (MSE)

Answer(s): C

Explanation:

The ROUGE metric is widely used to evaluate the quality of summaries generated by language models, as it measures the overlap between the generated summary and reference summaries in terms of recall, precision,
and F1-score for n-grams and sequences.



A company has implemented a large language model (LLM) solution by using a pre-trained model. The company needs to ensure that the model’s responses are transparent and accurate. The company wants to ground the model’s responses in factual information from the company’s authoritative data sources.
Which technique should the company use to meet these requirements?

  1. Prompt engineering
  2. Reinforcement learning
  3. Retrieval Augmented Generation (RAG)
  4. Static knowledge base

Answer(s): C

Explanation:

Retrieval Augmented Generation (RAG) grounds the model’s responses in authoritative company data by retrieving relevant information at inference time, which improves factual accuracy and makes responses more transparent and trustworthy.



A company needs to deploy AI agents built with various frameworks and models on a flexible infrastructure.
Which Amazon Bedrock feature meets these requirements?

  1. Amazon Bedrock Agents
  2. Amazon Bedrock Marketplace
  3. Amazon Bedrock AgentCore
  4. Amazon Bedrock Flows

Answer(s): C

Explanation:

Amazon Bedrock AgentCore provides flexible infrastructure for deploying and operating AI agents built with different agent frameworks and foundation models. It supports production-ready agent workloads without requiring the agents to be built exclusively with Amazon Bedrock Agents.



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