Free AIF-C01 Exam Braindumps
AWS Certified AI Practitioner (page: 17)

Updated On: 19-Oct-2026

A company needs to classify equipment types from images. The company has text descriptions and sample images for each equipment type.
Which solution will meet these requirements with the LEAST operational overhead?

  1. Use the existing images to train a new Amazon SageMaker AI model.
  2. Use a multimodal model on Amazon Bedrock to classify the images by providing the text descriptions.
  3. Augment the existing images and fine-tune an existing Amazon SageMaker AI model.
  4. Generate synthetic images by using an Amazon Bedrock model. Train an Amazon SageMaker AI model.

Answer(s): B

Explanation:

A multimodal foundation model in Amazon Bedrock can analyze images together with text instructions or descriptions, allowing the company to classify equipment without training, fine-tuning, or managing a separate machine learning model. This provides the required image understanding capability with the least operational overhead.



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.



A financial services company must ensure that its generative AI-powered chatbot provides factual responses for regulatory compliance.
Which solution prevents the underlying foundation model (FM) from hallucinating?

  1. Use AWS Config to query compliance metadata by using natural language.
  2. Configure Amazon Bedrock Guardrails to evaluate user inputs and model responses.
  3. Use Amazon Fraud Detector to detect potentially fraudulent online activities.
  4. Use AWS Audit Manager to prepare IT audit and compliance reports.

Answer(s): B

Explanation:

Amazon Bedrock Guardrails allows you to define rules and policies that evaluate and constrain both user inputs and model responses. By configuring guardrails, you can prevent the generative AI model from producing hallucinated or non-factual outputs, ensuring the chatbot adheres to compliance requirements and provides factual responses. This directly addresses the need to control hallucinations in generative AI systems for regulatory compliance.



A company uses Amazon SageMaker AI to generate article summaries in multiple languages. The company needs a metric to evaluate the quality of the summary translations in multiple languages.
Which evaluation metric will meet these requirements?

  1. Recall-Oriented Understudy for Gisting Evaluation (ROUGE)
  2. Bilingual evaluation understudy (BLEU)
  3. Area Under the ROC Curve (AUC)
  4. Precision

Answer(s): B

Explanation:

Bilingual Evaluation Understudy measures how closely a generated translation matches one or more high-quality reference translations, making it well suited for evaluating the quality of multilingual summary translations.



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.



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