AIF-C01 Practice Test Questions to Help You Prepare with Confidence
Getting ready for an Amazon AWS Certified AI Practitioner Exam certification exam can feel confusing at first. There’s a lot to cover, limited time, and plenty of pressure to do well. That’s where our practice test questions for AIF-C01 come in.
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Amazon AIF-C01 Sample Questions – Free Practice Test & Real Exam Prep
Question #1
A bank is fine-tuning a large language model (LLM) on Amazon Bedrock to assist customers with questions about their loans. The bank wants to ensure that the model does not reveal any private customer data.Which solution meets these requirements?
A. Use Amazon Bedrock Guardrails.
B. Remove personally identifiable information (PII) from the customer data before fine-tuning the LLM.
C. Increase the Top-K parameter of the LLM.
D. Store customer data in Amazon S3. Encrypt the data before fine-tuning the LLM.
Answer: B
Explanation
The goal is to prevent a fine-tuned large language model (LLM) on Amazon Bedrock from revealing private
customer data. Let’s analyze the options:
A. Amazon Bedrock Guardrails: Guardrails in Amazon Bedrock allow users to define policies to filter
harmful or sensitive content in model inputs and outputs. While useful for real-time content moderation, they
do not address the risk of private data being embedded in the model during fine-tuning, as the model could
still memorize sensitive information.
B. Remove personally identifiable information (PII) from the customer data before fine-tuning the LLM:
Removing PII (e.g., names, addresses, account numbers) from the training dataset ensures that the model does not learn or memorize sensitive customer data, reducing the risk of data leakage. This is a proactive and
effective approach to data privacy during model training.
C. Increase the Top-K parameter of the LLM: The Top-K parameter controls the randomness of the model’s
output by limiting the number of tokens considered during generation. Adjusting this parameter affects output
diversity but does not address the privacy of customer data embedded in the model.
D. Store customer data in Amazon S3. Encrypt the data before fine-tuning the LLM: Encrypting data in
Amazon S3 protects data at rest and in transit, but during fine-tuning, the data is decrypted and used to train
the model. If PII is present, the model could still learn and potentially expose it, so encryption alone does not
solve the problem.
Exact Extract Reference: AWS emphasizes data privacy in AI/ML workflows, stating, “To protect sensitive
data, you can preprocess datasets to remove personally identifiable information (PII) before using them for
model training. This reduces the risk of models inadvertently learning or exposing sensitive information.”
AWS AI Practitioner Study Guide (emphasis on data privacy in LLM fine-tuning
Question #2
Sentiment analysis is a subset of which broader field of AI?
A. Computer vision
B. Robotics
C. Natural language processing (NLP)
D. Time series forecasting
Answer: C
Explanation
Sentiment analysis is the task of determining the emotional tone or intent behind a body of text (positive,
negative, neutral).
This falls under Natural Language Processing (NLP) because it deals with understanding and processing
human language.
Computer vision relates to images, robotics to autonomous machines, and time series forecasting to predicting
values from sequential data.
# Reference:
AWS ML Glossary – NLP
Question #3
Which prompting technique can protect against prompt injection attacks?
A. Adversarial prompting
B. Zero-shot prompting
C. Least-to-most prompting
D. Chain-of-thought prompting
Answer: A
Explanation
The correct answer is A because adversarial prompting is a defensive technique used to identify and protect
against prompt injection attacks in large language models (LLMs). In adversarial prompting, developers
intentionally test the model with manipulated or malicious prompts to evaluate how it behaves under attack
and to harden the system by refining prompts, filters, and validation logic.
From AWS documentation:
"Adversarial prompting is used to evaluate and defend generative AI models against harmful or manipulative
inputs (prompt injections). By testing with adversarial examples, developers can identify vulnerabilities and
apply safeguards such as Guardrails or context filtering to prevent model misuse."
Prompt injection occurs when an attacker tries to override system or developer instructions within a prompt,
leading the model to disclose restricted information or behave undesirably. Adversarial prompting helps
uncover and mitigate these risks before deployment.
Explanation of other options:
B. Zero-shot prompting provides no examples and does not protect against injection attacks.
C. Least-to-most prompting is a reasoning technique used to break down complex problems step-by-step, not
a security measure.
D. Chain-of-thought prompting encourages detailed reasoning by the model but can actually increase
exposure to prompt injection if not properly constrained.
Referenced AWS AI/ML Documents and Study Guides:
AWS Responsible AI Practices – Prompt Injection and Safety Testing
Amazon Bedrock Developer Guide – Secure Prompt Design and Evaluation
AWS Generative AI Security Whitepaper – Adversarial Testing and Guardrails
Question #4
A digital devices company wants to predict customer demand for memory hardware. The company does not have coding experience or knowledge of ML algorithms and needs to develop a data-driven predictive model. The company needs to perform analysis on internal data and external data.Which solution will meet these requirements?
A. Store the data in Amazon S3. Create ML models and demand forecast predictions by using Amazon
SageMaker built-in algorithms that use the data from Amazon S3.
B. Import the data into Amazon SageMaker Data Wrangler. Create ML models and demand forecast
predictions by using SageMaker built-in algorithms.
C. Import the data into Amazon SageMaker Data Wrangler. Build ML models and demand forecast
predictions by using an Amazon Personalize Trending-Now recipe.
Answer: D
Explanation
Amazon SageMaker Canvas is a visual, no-code machine learning interface that allows users to build machine
learning models without having any coding experience or knowledge of machine learning algorithms. It
enables users to analyze internal and external data, and make predictions using a guided interface.
Option D (Correct): "Import the data into Amazon SageMaker Canvas. Build ML models and demand
forecast predictions by selecting the values in the data from SageMaker Canvas": This is the correct answer
because SageMaker Canvas is designed for users without coding experience, providing a visual interface to
build predictive models with ease.
Option A: "Store the data in Amazon S3 and use SageMaker built-in algorithms" is incorrect because it
requires coding knowledge to interact with SageMaker's built-in algorithms.
Option B: "Import the data into Amazon SageMaker Data Wrangler" is incorrect. Data Wrangler is primarily
for data preparation and not directly focused on creating ML models without coding.
Option C: "Use Amazon Personalize Trending-Now recipe" is incorrect as Amazon Personalize is for building
recommendation systems, not for general demand forecasting.
AWS AI Practitioner References:
Amazon SageMaker Canvas Overview: AWS documentation emphasizes Canvas as a no-code solution for
building machine learning models, suitable for business analysts and users with no coding experience.
Question #5
A company that streams media is selecting an Amazon Nova foundation model (FM) to process documents and images. The company is comparing Nova Micro and Nova Lite. The company wants to minimize costs.
A. Nova Micro uses transformer-based architectures. Nova Lite does not use transformer-based
architectures.
B. Nova Micro supports only text data. Nova Lite is optimized for numerical data.
C. Nova Micro supports only text. Nova Lite supports images, videos, and text.
D. Nova Micro runs only on CPUs. Nova Lite runs only on GPUs.
Answer: C
Explanation
The correct answer is C, because Amazon Nova Micro is a smaller, lower-cost foundation model that is text
only, while Nova Lite is a more capable multimodal model that supports images, videos, and text. According
to AWS Bedrock documentation, the Nova model family includes variants that differ in capability and cost.
Nova Micro is optimized for lightweight text-based tasks, including summarization, question answering, and
basic reasoning. This makes it cheaper to operate and well-suited for cost-sensitive workloads. Nova Lite, on
the other hand, is a multimodal FM that can analyze documents, screenshots, photographs, charts, and videos,
making it ideal for media companies requiring cross-format understanding. AWS clarifies that both Micro and
Lite use transformer-based architectures, and run on managed infrastructure that abstracts hardware
considerations. Therefore, the main differentiator is capability—and Nova Micro being text-only is the more
cost-effective option. Nova Lite is appropriate only when image or video analysis is required.
Referenced AWS Documentation:
Amazon Bedrock – Nova Model Family Overview
AWS Generative AI Model Selection Guide
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