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Amazon AIF-C01 Exam Questions

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AWS Certified AI Practitioner Exam

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  Updated 05, 13,2026
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Amazon AIF-C01 Practice Test Questions ( Updated) – Real Exam Questions & Dumps PDF

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Amazon AIF-C01 Sample Questions – Free Practice Test & Real Exam Prep

Question #1

A company acquires International Organization for Standardization (ISO) accreditation to manage AI risks and to use AI responsibly. What does this accreditation certify?

  • A. All members of the company are ISO certified.
  • B. All AI systems that the company uses are ISO certified.
  • C. All AI application team members are ISO certified.
  • D. The company’s development framework is ISO certified.
Answer: D
Explanation
ISO certifications apply to processes, frameworks, and systems — not individuals or every piece of software.
When a company is ISO-certified, its development framework and governance processes comply with ISO 
standards for security, risk, or AI responsibility.
# Reference:
AWS Compliance Programs – ISO
Question #2

A company wants to use a large language model (LLM) on Amazon Bedrock for sentiment analysis. The company wants to know how much information can fit into one prompt.Which consideration will inform the company's decision?

  • A. Temperature
  • B. Context window
  • C. Batch size
  • D. Model size
Answer: B
Explanation
The context window determines how much information can fit into a single prompt when using a large 
language model (LLM) like those on Amazon Bedrock.
Context Window:
The context window is the maximum amount of text (measured in tokens) that a language model can process 
in a single pass.
For LLM applications, the size of the context window limits how much input data, such as text for sentiment 
analysis, can be fed into the model at once.
Why Option B is Correct:
Determines Prompt Size: The context window size directly informs how much information (e.g., words or 
sentences) can fit in one prompt.
Model Capacity: The larger the context window, the more information the model can consider for generating 
outputs.
Why Other Options are Incorrect:
A. Temperature: Controls randomness in model outputs but does not affect the prompt size.
C. Batch size: Refers to the number of training samples processed in one iteration, not the amount of 
information in a prompt.
D. Model size: Refers to the number of parameters in the model, not the input size for a single prompt.

Question #3

A company wants to label training datasets by using human feedback to fine-tune a foundation model (FM). The company does not want to develop labeling applications or manage a labeling workforce. Which AWS service or feature meets these requirements?

  • A. Amazon SageMaker Data Wrangler
  • B. Amazon SageMaker Ground Truth Plus
  • C. Amazon Transcribe
  • D. Amazon Macie
Answer: B
Explanation
Amazon SageMaker Ground Truth Plus provides a fully managed data labeling service where AWS manages 
the workforce, tools, and processes.
Data Wrangler is for data preparation and transformation.
Transcribe is for speech-to-text.
Macie is for sensitive data discovery, not labeling.
# Reference:
AWS Documentation – SageMaker Ground Truth Plus
Question #4

A bank has fine-tuned a large language model (LLM) to expedite the loan approval process. During an external audit of the model, the company discovered that the model was approving loans at a faster pace for a specific demographic than for other demographics.How should the bank fix this issue MOST cost-effectively?

  • A. Include more diverse training data. Fine-tune the model again by using the new data.
  • B. Use Retrieval Augmented Generation (RAG) with the fine-tuned model.
  • C. Use AWS Trusted Advisor checks to eliminate bias.
  • D. Pre-train a new LLM with more diverse training data.
Answer: A
Explanation
Comprehensive and Detailed Explanation From Exact Extract:
The best practice for mitigating bias in AI/ML models, according to AWS and responsible AI frameworks, is 
to ensure that the training data is representative and diverse. If a model demonstrates bias (such as favoring a 
particular demographic), the recommended, cost-effective approach is to collect additional data from 
underrepresented groups and retrain (fine-tune) the model with the improved dataset.
A. Include more diverse training data. Fine-tune the model again by using the new data:
“The most effective method to reduce model bias is to curate and include diverse, representative training data, 
then retrain or fine-tune the model.”
(Reference: AWS Responsible AI, SageMaker Clarify Bias Mitigation)
B (RAG) is unrelated to model fairness or bias mitigation; it’s for grounding LLMs with external knowledge.
C (AWS Trusted Advisor) is for AWS resource optimization/security—not for ML model bias detection or 
mitigation.
D (Pre-train a new LLM) would be extremely costly and is unnecessary; fine-tuning with better data is much 
more efficient.
References:
Responsible AI on AWS
Amazon SageMaker Clarify: Detecting and Mitigating Bias
AWS Certified AI Practitioner Exam Guide
Question #5

Which scenario describes a potential risk and limitation of prompt engineering In the context of a generative AI model?

  • A. Prompt engineering does not ensure that the model always produces consistent and deterministic outputs, eliminating the need for validation.
  • B. Prompt engineering could expose the model to vulnerabilities such as prompt injection attacks.
  • C. Properly designed prompts reduce but do not eliminate the risk of data poisoning or model hijacking.
  • D. Prompt engineering does not ensure that the model will consistently generate highly reliable outputs when working with real-world data.
Answer: B
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