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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,
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
Question #6
A customer service team is developing an application to analyze customer feedback and automatically classify the feedback into different categories. The categories include product quality, customer service, and delivery experience.Which AI concept does this scenario present?
A. Computer vision
B. Natural language processing (NLP)
C. Recommendation systems
D. Fraud detection
Answer: B
Explanation
The scenario involves analyzing customer feedback and automatically classifying it into categories such as
product quality, customer service, and delivery experience. This task requires processing and understanding
textual data, which is a core application of natural language processing (NLP). NLP encompasses techniques
for analyzing, interpreting, and generating human language, including tasks like text classification, sentiment
analysis, and topic modeling, all of which are relevant to this use case.
Exact Extract from AWS AI Documents:
From the AWS AI Practitioner Learning Path:
"Natural Language Processing (NLP) enables machines to understand and process human language. Common
NLP tasks include text classification, sentiment analysis, named entity recognition, and topic modeling.
Services like Amazon Comprehend can be used to classify text into predefined categories based on content."
(Source: AWS AI Practitioner Learning Path, Module on AI and ML Concepts)
Detailed Explanation:
Option A: Computer visionComputer vision involves processing and analyzing visual data, such as images or
videos. Since the scenario deals with textual customer feedback, computer vision is not applicable.
Option B: Natural language processing (NLP)This is the correct answer. The task of classifying customer
feedback into categories requires understanding and processing text, which is an NLP task. AWS services like
Amazon Comprehend are specifically designed for such text classification tasks.
Option C: Recommendation systemsRecommendation systems suggest items or content based on user
preferences or behavior. The scenario does not involve recommending products or services but rather
classifying feedback, so this option is incorrect.
Option D: Fraud detectionFraud detection involves identifying anomalous or fraudulent activities, typically in
financial or transactional data. The scenario focuses on text classification, not anomaly detection, making this
option irrelevant.
References:
AWS AI Practitioner Learning Path: Module on AI and ML Concepts
A company has developed an ML model for image classification. The company wants to deploy the model to production so that a web application can use the model.The company needs to implement a solution to host the model and serve predictions without managing any of the underlying infrastructure.Which solution will meet these requirements?
A. Use Amazon SageMaker Serverless Inference to deploy the model.
B. Use Amazon CloudFront to deploy the model.
C. Use Amazon API Gateway to host the model and serve predictions.
D. Use AWS Batch to host the model and serve predictions.
Answer: A
Explanation
Amazon SageMaker Serverless Inference is the correct solution for deploying an ML model to production in a
way that allows a web application to use the model without the need to manage the underlying infrastructure.
Amazon SageMaker Serverless Inference provides a fully managed environment for deploying machine
learning models. It automatically provisions, scales, and manages the infrastructure required to host the
model, removing the need for the company to manage servers or other underlying infrastructure.
Why Option A is Correct:
No Infrastructure Management: SageMaker Serverless Inference handles the infrastructure management for
deploying and serving ML models. The company can simply provide the model and specify the required
compute capacity, and SageMaker will handle the rest.
Cost-Effectiveness: The serverless inference option is ideal for applications with intermittent or unpredictable
traffic, as the company only pays for the compute time consumed while handling requests.
Integration with Web Applications: This solution allows the model to be easily accessed by web applications
via RESTful APIs, making it an ideal choice for hosting the model and serving predictions.
Why Other Options are Incorrect:
B. Use Amazon CloudFront to deploy the model: CloudFront is a content delivery network (CDN) service for
distributing content, not for deploying ML models or serving predictions.
C. Use Amazon API Gateway to host the model and serve predictions: API Gateway is used for creating,
deploying, and managing APIs, but it does not provide the infrastructure or the required environment to host
and run ML models.
D. Use AWS Batch to host the model and serve predictions: AWS Batch is designed for running batch
computing workloads and is not optimized for real-time inference or hosting machine learning models.
Thus, A is the correct answer, as it aligns with the requirement of deploying an ML model without managing
any underlying infrastructure.
Question #8
A financial company uses AWS to host its generative AI models. The company must generate reports to show adherence to international regulations for handling sensitive customer data
A. Amazon Macie
B. AWS Artifact
C. AWS Secrets Manager
D. AWS Config
Answer: B
Explanation
AWS Artifact provides compliance reports and certifications (ISO, SOC, GDPR-related documentation) to
prove regulatory adherence.
Question #9
A company uses Amazon Bedrock to implement a generative AI assistant on a website. The AI assistant helps customers with product recommendations and purchasing decisions. The company wants to measure the direct impact of the AI assistant on sales performance.
A. The conversion rate of customers who purchase products after AI assistant interactions
B. The number of customer interactions with the AI assistant
C. Sentiment analysis scores from customer feedback after AI assistant interactions
D. Natural language understanding accuracy rates
Answer: A
Explanation
The most direct business KPI for sales performance is conversion rate (percentage of users who purchase after
AI assistant interaction).
Number of interactions (B) shows engagement, not sales impact.
Sentiment analysis (C) shows customer satisfaction but not revenue impact.
NLU accuracy (D) is a technical metric, not a business outcome.
# Reference:
AWS Generative AI Use Cases – Measuring Business Value
Question #10
A company is using few-shot prompting on a base model that is hosted on Amazon Bedrock. The model currently uses 10 examples in the prompt. The model is invoked once daily and is performing well. The company wants to lower the monthly cost.Which solution will meet these requirements?
A. Customize the model by using fine-tuning.
B. Decrease the number of tokens in the prompt.
C. Increase the number of tokens in the prompt.
D. Use Provisioned Throughput.
Answer: B
Explanation
Decreasing the number of tokens in the prompt reduces the cost associated with using an LLM model on
Amazon Bedrock, as costs are often based on the number of tokens processed by the model.
Token Reduction Strategy:
By decreasing the number of tokens (words or characters) in each prompt, the company reduces the
computational load and, therefore, the cost associated with invoking the model.
Since the model is performing well with few-shot prompting, reducing token usage without sacrificing
performance can lower monthly costs.
Why Option B is Correct:
Cost Efficiency: Directly reduces the number of tokens processed, lowering costs without requiring additional
adjustments.
Maintaining Performance: If the model is already performing well, a reduction in tokens should not
significantly impact its performance.
Why Other Options are Incorrect:
A. Fine-tuning: Can be costly and time-consuming and is not needed if the current model is already
performing well.
C. Increase the number of tokens: Would increase costs, not lower them.
D. Use Provisioned Throughput: Is unrelated to token costs and applies more to read/write capacity in
databases.
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