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Amazon AIF-C01 Exam (page: 8)
Amazon AWS Certified AI Practitioner
Updated on: 15-Feb-2026

Viewing Page 8 of 43
AIF-C01 PDF 385 Questions

A company has installed a security camera. The company uses an ML model to evaluate the security camera footage for potential thefts. The company has discovered that the model disproportionately flags people who are members of a specific ethnic group.

Which type of bias is affecting the model output?

  1. Measurement bias
  2. Sampling bias
  3. Observer bias
  4. Confirmation bias

Answer(s): B

Explanation:

Sampling bias occurs when the training data is not representative of the overall population, leading to disproportionate flagging of specific groups. In this case, the model may have been trained on biased data that did not adequately represent all ethnic groups, resulting in skewed predictions. The other types of bias do not directly apply to the selection of training data or its representativeness.



A company is building a customer service chatbot. The company wants the chatbot to improve its responses by learning from past interactions and online resources.

Which AI learning strategy provides this self-improvement capability?

  1. Supervised learning with a manually curated dataset of good responses and bad responses
  2. Reinforcement learning with rewards for positive customer feedback
  3. Unsupervised learning to find clusters of similar customer inquiries
  4. Supervised learning with a continuously updated FAQ database

Answer(s): B

Explanation:

Reinforcement learning allows the chatbot to learn from interactions by receiving rewards for positive customer feedback, which helps the model self-improve over time. The other options do not directly provide a mechanism for continuous self-improvement based on interactions.



An AI practitioner has built a deep learning model to classify the types of materials in images. The AI practitioner now wants to measure the model performance.

Which metric will help the AI practitioner evaluate the performance of the model?

  1. Confusion matrix
  2. Correlation matrix
  3. R2 score
  4. Mean squared error (MSE)

Answer(s): A

Explanation:

A confusion matrix provides detailed insights into the performance of a classification model by showing the true positives, false positives, true negatives, and false negatives. This metric helps evaluate how well the model classifies the different types of materials in images. The other metrics are not as suitable for evaluating a classification model.



A company has built a chatbot that can respond to natural language questions with images. The company wants to ensure that the chatbot does not return inappropriate or unwanted images.

Which solution will meet these requirements?

  1. Implement moderation APIs.
  2. Retrain the model with a general public dataset.
  3. Perform model validation.
  4. Automate user feedback integration.

Answer(s): A

Explanation:

Implementing moderation APIs can help filter and block inappropriate or unwanted images before they are returned by the chatbot. The other options do not directly address ensuring that the chatbot avoids returning inappropriate images.



An AI practitioner is using an Amazon Bedrock base model to summarize session chats from the customer service department. The AI practitioner wants to store invocation logs to monitor model input and output data.

Which strategy should the AI practitioner use?

  1. Configure AWS CloudTrail as the logs destination for the model.
  2. Enable model invocation logging in Amazon Bedrock.
  3. Configure AWS Audit Manager as the logs destination for the model.
  4. Configure model invocation logging in Amazon EventBridge.

Answer(s): B

Explanation:

Enabling invocation logging in Amazon Bedrock allows the AI practitioner to monitor and store the input and output data for model invocations. The other options are not directly used for logging model invocations in Amazon Bedrock.



A company is building an ML model to analyze archived data. The company must perform inference on large datasets that are multiple GBs in size. The company does not need to access the model predictions immediately.

Which Amazon SageMaker inference option will meet these requirements?

  1. Batch transform
  2. Real-time inference
  3. Serverless inference
  4. Asynchronous inference

Answer(s): A

Explanation:

Batch transform is ideal for processing large datasets that do not require real-time predictions. It allows the company to perform inference on multiple GBs of data efficiently without needing immediate results. The other options are more suitable for scenarios requiring real-time or near real-time access.



Which term describes the numerical representations of real-world objects and concepts that AI and natural language processing (NLP) models use to improve understanding of textual information?

  1. Embeddings
  2. Tokens
  3. Models
  4. Binaries

Answer(s): A

Explanation:

Embeddings are numerical representations of real-world objects and concepts that help AI and NLP models understand and work with textual information more effectively by capturing relationships and similarities between words or phrases. The other options do not describe this concept.



A research company implemented a chatbot by using a foundation model (FM) from Amazon Bedrock. The chatbot searches for answers to questions from a large database of research papers.

After multiple prompt engineering attempts, the company notices that the FM is performing poorly because of the complex scientific terms in the research papers.

How can the company improve the performance of the chatbot?

  1. Use few-shot prompting to define how the FM can answer the questions.
  2. Use domain adaptation fine-tuning to adapt the FM to complex scientific terms.
  3. Change the FM inference parameters.
  4. Clean the research paper data to remove complex scientific terms.

Answer(s): B

Explanation:

Domain adaptation fine-tuning allows the FM to better understand the complex scientific terms by training it with domain-specific data, improving its performance on such specialized content. The other options are either insufficient or not directly related to handling complex terminology effectively.



Viewing Page 8 of 43



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