Free Amazon Web Services AIF-C01 Practice Exam with Questions & Answers | Set: 10

A prompt template defines how the model is structured and guided (system prompts, roles, guardrails).

An attack that reveals or leaks this prompt template is known as a prompt extraction attack.

The other options (persona switching, exploiting friendliness, ignoring prompts) describe adversarial techniques but do not directly expose the internal configured behavior.

???? Reference:

AWS Responsible AI – Prompt Injection & Extraction Attacks

Generative AI models (such as large language models) are designed to generate new content, such as text, summaries, images, and more. Summarizing text—like customer complaints—is a classic application of generative AI.

A is correct:

"Text summarization is a core generative AI use case, as it involves generating new, concise content from a larger body of text."

(Reference: AWS Generative AI Use Cases)

B and C are standard ML classification/segmentation tasks.

D is a regression/prediction task, not generative.

The company wants to enhance the response quality of an LLM for complex problem-solving tasks requiring detailed reasoning and step-by-step explanations. Chain-of-thought prompting encourages the LLM to break down the problem into intermediate steps, providing a clear reasoning process before arriving at the final answer, which is ideal for this requirement.

Exact Extract from AWS AI Documents:

From the AWS Bedrock User Guide:

"Chain-of-thought prompting improves the reasoning capabilities of large language models by encouraging them to break down complex tasks into intermediate steps, providing a step-by-step explanation that leads to the final answer. This technique is particularly effective for problem-solving tasks requiring detailed reasoning."

(Source: AWS Bedrock User Guide, Prompt Engineering Techniques)

Detailed Explanation:

Option A: Few-shot promptingFew-shot prompting provides a few examples to guide the LLM but does not explicitly encourage step-by-step reasoning or detailed explanations.

Option B: Zero-shot promptingZero-shot prompting relies on the LLM’s pre-trained knowledge without examples, making it less effective for complex tasks requiring detailed reasoning.

Option C: Directional stimulus promptingDirectional stimulus prompting is not a standard technique in AWS documentation, likely a distractor, and does not address step-by-step reasoning.

Option D: Chain-of-thought promptingThis is the correct answer. Chain-of-thought prompting enhances response quality for complex tasks by guiding the LLM to reason step-by-step, providing detailed explanations.

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.

Amazon SageMaker Model Cards provide a centralized and standardized repository for documenting machine learning models. They capture key details such as the model's intended use, training and evaluation datasets, performance metrics, ethical considerations, and other relevant information. This documentation facilitates governance and reporting by ensuring that all stakeholders have access to consistent and comprehensive information about each model. While Amazon SageMaker Debugger is used for real-time debugging and monitoring during training, and Amazon SageMaker Model Monitor tracks deployed models for data and predictionquality, neither offers the comprehensive documentation capabilities of Model Cards. Amazon SageMaker JumpStart provides pre-built models and solutions but does not focus on governance documentation.

The retail company wants to build an ML model for product recommendations using responsible practices to decrease model bias. Collecting balanced and diverse data ensures the model does not favor specific groups, reducing bias and promoting fairness, a key responsible AI practice.

Exact Extract from AWS AI Documents:

From the AWS AI Practitioner Learning Path:

"To reduce model bias, it is critical to collect balanced and diverse data that represents various demographics and user groups. This practice ensures fairness and prevents the model from disproportionately favoring certain populations."

(Source: AWS AI Practitioner Learning Path, Module on Responsible AI)

Detailed Explanation:

Option A: Use data from only customers who match the demography of the company's overall customer base.Limiting data to a specific demographic may reinforce existing biases, failing to address underrepresented groups and increasing bias.

Option B: Collect data from customers who have a past purchase history.Focusing only on customers with purchase history may exclude new users, potentially introducing bias, and does not address diversity.

Option C: Ensure that the data is balanced and collected from a diverse group.This is the correct answer. A balanced and diverse dataset reduces bias by ensuring the model learns from a representative sample, aligning with responsible AI practices.

Option D: Ensure that the data is from a publicly available dataset.Public datasets may not be diverse or representative of the company’s customer base and could introduce unrelated biases, failing to address fairness.

The correct answer is D. Amazon Bedrock Agents are used to orchestrate and execute complex workflows by connecting foundation models with APIs, databases, and tools. According to AWS documentation, agents interpret user inputs, plan the necessary steps, call external APIs or systems, and return structured results. This allows the model to go beyond text generation into full automation workflows—such as booking tasks, querying internal systems, or summarizing reports. Option A describes multi-modal models, B refers to prompt tuning, and C misstates control delegation; agents act autonomously based on model reasoning. Thus, Bedrock Agents function as intelligent orchestrators, handling multi-step task execution through integrated tool use.

Referenced AWS AI/ML Documents and Study Guides:

Amazon Bedrock Developer Guide – Agents Overview

AWS Generative AI Best Practices – Workflow Orchestration

Retraining or pre-training is costly and unnecessary for just using company policies.

Fine-tuning adapts models but is inefficient for frequently changing company documents.

Retrieval-Augmented Generation (RAG) is the best approach — it retrieves relevant policy documents from a knowledge base and feeds them into the model context in real time, ensuring accurate and up-to-date responses.

???? Reference:

AWS Documentation – RAG with Amazon Bedrock

When you have a labeled dataset and need to scale a generative AI solution for more complex or diverse product categories, fine-tuning the foundation model with your dataset is the best approach for consistent, accurate tagging.

D is correct:

“Fine-tuning a foundation model with your labeled data allows the model to generalize to new categories and improve tagging accuracy for your inventory.”

(Reference: Amazon Bedrock Fine-Tuning, AWS Generative AI)

A (zero-shot) and B (prompt templates) do not leverage the labeled data or scale as accurately.

C (continued pre-training) uses unlabeled data, not labeled.

The correct answer is Amazon Bedrock PartyRock, a playground for building and experimenting with generative AI apps in a low-cost, no-code environment. PartyRock is designed for innovation and learning. It enables users to try out prompts, LLM apps, and templates using Amazon Bedrock under a free-tier friendly setup. According to AWS, PartyRock abstracts infrastructure and allows rapid prototyping using models from Bedrock providers. This makes it ideal for early experimentation, especially for non-developers or those not ready to invest in full production pipelines. In contrast, Amazon Q Developer is for software engineering tasks, SageMaker JumpStart focuses on deploying ML models, and Q Business targets enterprise knowledge workers. None of those are as cost-effective and experimental-focused as PartyRock.

Referenced AWS AI/ML Documents and Study Guides:

Amazon Bedrock Documentation – PartyRock Overview

AWS Generative AI Learning Path – Getting Started Tools