AWS Certified AI Practitioner (AIF-C01) — All Questions

Which description best defines prompt engineering?

• a.Physically upgrading the servers on which the model runs
• b.Manually relabeling the model's entire training dataset
• c.Designing and refining the input instructions to a model to get better outputs✓
• d.Rewriting the model's internal source code in order to change its neural architecture

Prompt engineering is the practice of crafting and refining prompts, wording, context, examples, and structure, to guide a model toward higher-quality outputs, without altering the model's weights or code.

A prompt includes two or three worked examples before asking the model to handle a new case. What prompting technique is this?

• a.Fine-tuning, which permanently updates the model's internal weights using a labeled dataset
• b.Zero-shot prompting, which supplies the model with no examples
• c.Data augmentation, which synthetically expands the training set by transforming existing samples
• d.Few-shot prompting, which supplies a handful of example input-output pairs as guidance✓

Few-shot prompting includes a small number of demonstrations in the prompt so the model can infer the desired pattern. Zero-shot gives no examples. Neither changes the model's weights, unlike fine-tuning.

When is fine-tuning a foundation model more appropriate than prompt engineering alone?

• a.When you need results instantly, with zero setup time and no additional training cost whatsoever
• b.When you specifically want to avoid using any of your own organization's data at all
• c.When you only need to slightly change the wording of one single question you are asking
• d.When the model must reliably adopt specialized knowledge or a style prompting cannot achieve✓

Fine-tuning adjusts model weights on your curated data, useful for deeply embedding domain knowledge, tone, or formats that prompting cannot reliably produce. It costs more and takes longer than simply editing a prompt.

In Amazon Bedrock, what does a Knowledge Base primarily enable?

• a.Automatically writing unit tests for your application code
• b.Encrypting all of the network traffic that flows between two separate AWS Regions in transit
• c.Provisioning virtual servers, load balancers, and networking for a scalable web application tier
• d.Connecting foundation models to your own data sources to support retrieval-augmented generation✓

Bedrock Knowledge Bases let you ground foundation models in your proprietary data by managing ingestion, embedding, and retrieval for RAG, so responses can cite current, organization-specific information.

What is an agent in the context of Amazon Bedrock?

• a.A component that lets a model plan and take multi-step actions by calling APIs and tools✓
• b.A physical networking appliance that is installed inside an on-premises corporate data center
• c.A human customer-support representative who manually answers and resolves customer help tickets
• d.A billing alarm that sends you a notification whenever monthly account spending exceeds a limit

Bedrock Agents orchestrate multi-step tasks: the model can break a request into steps, call APIs or tools, and use knowledge bases to complete an objective, going beyond a single text response.

Which AWS AI service extracts printed and handwritten text, forms, and tables from scanned documents?

• a.Amazon Textract, which reads text, form fields, and tables out of documents✓
• b.Amazon Polly, which converts written input text into natural-sounding spoken audio
• c.Amazon Translate, which converts written text from one human language into another
• d.Amazon Lex, which builds conversational chatbots from speech and text

Amazon Textract performs OCR-plus: it extracts text, key-value form fields, and tables from documents and images. Polly is text-to-speech, Lex builds chatbots, and Translate handles language translation.

A company wants to convert written text into natural-sounding speech for an accessibility feature. Which service fits?

• a.Amazon Comprehend, which analyzes the meaning and sentiment of text
• b.Amazon Transcribe, which converts recorded or streaming speech into written text
• c.Amazon Rekognition, which analyzes objects and scenes in images and video
• d.Amazon Polly, which synthesizes lifelike spoken audio from written text✓

Amazon Polly is text-to-speech, turning written text into lifelike audio. Transcribe does the reverse (speech-to-text), Comprehend analyzes text meaning, and Rekognition analyzes images and video.

Which service automatically detects the dominant language, key phrases, entities, and sentiment in a body of text?

• a.Amazon Textract, a service that extracts written content out of scanned documents
• b.Amazon Polly, a service that turns written text into natural-sounding spoken audio
• c.Amazon Kendra, a service that provides intelligent search across content
• d.Amazon Comprehend, a natural language processing service for analyzing text✓

Amazon Comprehend is a natural language processing service that identifies language, entities, key phrases, sentiment, and more within text. Textract extracts document content, Polly is TTS, and Kendra is enterprise search.

A business wants to build a conversational chatbot with automatic speech recognition and natural language understanding. Which service is purpose-built for this?

• a.Amazon Polly, which converts written input text into natural-sounding spoken audio
• b.Amazon Textract, which pulls text, forms, and tables from scanned documents
• c.Amazon Forecast, which predicts future values from historical time-series input data
• d.Amazon Lex, which supplies speech recognition and language understanding for bots✓

Amazon Lex provides the automatic speech recognition and natural language understanding needed to build voice and text chatbots. Forecast handles time-series, Textract reads documents, and Polly synthesizes speech.

Which service provides intelligent, natural-language enterprise search across an organization's internal content repositories?

• a.Amazon Translate, which converts written text from one human language into another one
• b.Amazon Rekognition, which detects objects, scenes, and faces within images and video
• c.Amazon Kendra, which understands natural-language queries across internal content✓
• d.Amazon Transcribe, which converts recorded or streaming speech audio into written text

Amazon Kendra is an intelligent enterprise search service that understands natural-language queries across documents and data sources. The other services handle images, speech-to-text, and translation respectively.

Which AWS service analyzes images and video to detect objects, scenes, faces, and unsafe content?

• a.Amazon Polly, the service that converts written text into natural-sounding speech
• b.Amazon Comprehend, the natural language service for analyzing text
• c.Amazon Rekognition, the computer vision service for analyzing images and video✓
• d.Amazon Lex, the service for building chatbots

Amazon Rekognition is the computer vision service for images and video: object and scene detection, facial analysis, text-in-image, and content moderation. Comprehend works on text, not visuals.

What is the main advantage of using a managed AWS AI service like Amazon Rekognition instead of building a custom model?

• a.You are fully guaranteed that the model will never make a mistake
• b.You get production-ready capabilities through an API with no data, training, or infrastructure work✓
• c.You are given complete low-level control over the model's internals
• d.You must still collect and label millions of your own images first

Managed AI services expose ready-to-use capabilities through simple APIs, removing the burden of data collection, training, tuning, and infrastructure. The trade-off is less control over the underlying model internals.

In a RAG-based application, what is the typical role of a vector database?

• a.Scheduling and running the nightly backups of the database
• b.Compiling and bundling all of the application's front-end JavaScript and CSS source code
• c.Storing embeddings so relevant content can be retrieved quickly to augment the prompt✓
• d.Rotating the cryptographic encryption keys that are used to protect stored data at rest

A vector database stores embeddings and supports fast similarity search, so the most semantically relevant chunks can be retrieved and added to the model's prompt. This retrieval step is the 'R' in RAG.

Why might adding clear context and explicit constraints to a prompt improve a model's output?

• a.It fully disables the model's ability to make a factual mistake
• b.It reduces ambiguity, helping the response better match the user's intent and format✓
• c.It reliably lowers the price that is charged per token to zero
• d.It permanently retrains the underlying model on the instructions

Specific context, role, and constraints narrow the space of acceptable answers, guiding the model toward the intended content, tone, and format. It shapes a single response and does not retrain the model or change pricing rules.

A team wants to customize a foundation model's behavior using labeled examples, accepting higher cost than prompting. Which approach is this?

• a.Reducing the maximum output token limit for each response
• b.Simply lowering the temperature sampling parameter at inference time
• c.Fine-tuning the model on their own curated dataset of labeled example pairs✓
• d.Switching the language of the final output by routing it through a translation service

Fine-tuning updates model weights using labeled examples to adapt behavior, at greater cost and effort than prompt engineering. Adjusting temperature or token limits only tweaks inference settings, not learned behavior.

A support team needs answers grounded in internal policy documents that change frequently. Which approach avoids retraining the model each time the documents change?

• a.Fine-tuning the model again after every document edit
• b.Continued pre-training the model on the whole public internet
• c.Raising the temperature parameter on each request
• d.Retrieval Augmented Generation (RAG) using a knowledge base✓

RAG retrieves the latest relevant passages at query time and adds them to the prompt, so updated documents are reflected immediately without retraining. Fine-tuning would require re-running training whenever content changes.

Asking a model to 'think step by step' and show its reasoning before giving a final answer is which prompting technique?

• a.Retrieval augmentation with external documents
• b.Zero-shot prompting
• c.Negative prompting
• d.Chain-of-thought prompting✓

Chain-of-thought prompting encourages the model to reason through intermediate steps, which can improve performance on multi-step problems. Zero-shot gives no examples, and retrieval augmentation supplies external documents.

Giving a model an instruction with no worked examples and expecting it to perform the task is called what?

• a.Few-shot prompting
• b.Fine-tuning
• c.Zero-shot prompting✓
• d.Chain-of-thought prompting

Zero-shot prompting provides only the instruction, with no examples. Few-shot includes a handful of demonstrations, while fine-tuning changes the model's weights rather than just the prompt.

How does the Top K inference parameter influence text generation?

• a.It sets the AWS Region used for the request
• b.It restricts the next-token choice to the K most probable tokens at each step✓
• c.It updates the model's weights after each token is produced
• d.It encrypts the generated output before returning it

Top K limits sampling to the K highest-probability candidate tokens at each step, controlling diversity. It does not change weights, choose Regions, or encrypt output.

For a task that requires consistent, factual answers, how should you set the temperature parameter?

• a.As high as possible to maximize creativity
• b.Low (near zero) to reduce randomness and make outputs more deterministic✓
• c.Randomly for each request
• d.It has no effect on factual consistency

Lower temperature makes token selection more deterministic and focused, which suits factual, repeatable tasks. High temperature increases randomness and creativity, useful for brainstorming but riskier for facts.

What is the purpose of a 'stop sequence' inference parameter?

• a.It defines the persona the model should adopt
• b.It selects which foundation model to use
• c.It specifies text that, when generated, tells the model to stop producing further output✓
• d.It encrypts the prompt while it is in transit

A stop sequence marks where generation should end, useful for cutting off output cleanly at a delimiter. It does not set the model's persona, choose the model, or handle encryption.

What is a 'system prompt' in a conversational LLM application?

• a.High-level instructions that set the model's role, behavior, and constraints for the whole conversation✓
• b.The user's most recent chat message
• c.The billing summary generated for the session
• d.The list of GPUs assigned to the request

A system prompt establishes the assistant's role, tone, and rules that persist across the conversation, shaping how it responds to each user message. It is distinct from any single user turn.

Which Amazon Bedrock feature helps you compare foundation models and pick the best one for your use case using automatic metrics or human review?

• a.Amazon Bedrock Guardrails
• b.Amazon Bedrock Knowledge Bases for RAG
• c.Amazon Bedrock Agents
• d.Amazon Bedrock model evaluation✓

Bedrock model evaluation lets you assess and compare models with automatic metrics or human reviewers to choose the best fit. Guardrails enforce safety, Knowledge Bases enable RAG, and Agents orchestrate actions.

Which AWS service can serve as the vector store backing an Amazon Bedrock knowledge base?

• a.Amazon Polly
• b.Amazon OpenSearch Serverless✓
• c.Amazon Transcribe speech-to-text
• d.AWS CloudTrail

Amazon OpenSearch Serverless offers a vector engine that stores embeddings for similarity search behind a Bedrock knowledge base. Polly, Transcribe, and CloudTrail are unrelated to vector storage.

Besides OpenSearch, which AWS database can store vector embeddings for RAG by using the pgvector extension?

• a.Amazon Aurora PostgreSQL✓
• b.Amazon Redshift
• c.Amazon DynamoDB key-value tables
• d.Amazon Neptune

Amazon Aurora PostgreSQL supports the pgvector extension to store and query embeddings, making it a viable vector store for RAG. The other databases are not the pgvector-based option referenced here.

In a RAG pipeline on Amazon Bedrock, what is the role of an embeddings model such as Amazon Titan Text Embeddings?

• a.To generate the final natural-language answer for the user
• b.To convert documents and queries into numeric vectors so relevant chunks can be found by similarity✓
• c.To encrypt the knowledge base while it is at rest
• d.To provision the underlying servers for the pipeline

The embeddings model turns text into vectors so the system can retrieve the most semantically relevant chunks. A separate generation model then writes the final answer using those retrieved chunks.

Why are large documents split into smaller 'chunks' before being stored in a knowledge base for RAG?

• a.To permanently delete the original documents
• b.To translate the documents into another language
• c.To increase the model's temperature
• d.So the most relevant passages can be retrieved and fit within the model's context window✓

Chunking lets retrieval return just the passages that matter and keeps them within the context window, improving relevance and efficiency. It does not delete, translate, or change inference parameters.

In Amazon Bedrock Agents, what does an 'action group' define?

• a.The billing alerts configured for the account
• b.The list of users allowed to chat with the agent
• c.The APIs or AWS Lambda functions the agent can call to take actions on the user's behalf✓
• d.The encryption keys used to protect the model

Action groups tell a Bedrock agent which operations, via API schemas and Lambda functions, it may invoke to complete tasks, enabling multi-step automation. They are about capabilities, not billing or keys.

What does fine-tuning a foundation model on Amazon Bedrock require that prompt engineering does not?

• a.A physically larger monitor
• b.A curated training dataset plus additional compute time and cost✓
• c.Turning off all of the safety filters
• d.A dedicated public internet connection for each and every inference call

Fine-tuning trains on a prepared dataset and consumes extra compute, time, and money, unlike simply editing a prompt. It is worth it when prompting cannot reliably deliver the needed behavior.

Which Amazon Bedrock customization method further trains a model on large amounts of your unlabeled domain text to adapt its general knowledge?

• a.Zero-shot prompting
• b.Continued pre-training✓
• c.Provisioned Throughput capacity reservation
• d.Guardrails

Continued pre-training uses large unlabeled domain corpora to adapt a model's broad knowledge. Fine-tuning instead uses labeled examples; Provisioned Throughput is a capacity option and Guardrails handle safety.

A production application needs guaranteed, consistent inference capacity for a specific Amazon Bedrock model. Which option fits?

• a.Provisioned Throughput✓
• b.Spot capacity
• c.On-demand inference pricing
• d.An EC2 savings plan

Provisioned Throughput reserves dedicated model capacity for steady, predictable performance, often required for large or latency-sensitive workloads. On-demand bills per token but does not reserve capacity.

Which Amazon Bedrock pricing approach charges per token processed with no long-term commitment, suiting variable workloads?

• a.Provisioned Throughput
• b.Reserved Instances
• c.A one-year commitment plan
• d.On-demand inference✓

On-demand inference bills for the tokens you actually process, with no commitment, ideal for spiky or exploratory use. Provisioned Throughput instead reserves capacity for steady, high-volume needs.

Which is a prompt-engineering best practice for getting better, more relevant outputs?

• a.Be specific and provide clear context, constraints, and the desired output format✓
• b.Keep the prompt as vague as possible
• c.Never give the model any examples
• d.Always request the longest possible answer

Clear, specific prompts with context, constraints, and a target format reduce ambiguity and steer the model to the intended result. Vague prompts leave too much room for off-target responses.

Telling a model 'You are an experienced tax advisor...' before giving it the task is an example of what?

• a.Fine-tuning
• b.Role (persona) prompting✓
• c.Data augmentation technique
• d.Vector search

Assigning the model a role or persona in the prompt guides its tone, framing, and domain focus. It shapes the response through wording only and does not change the model's weights like fine-tuning.

How does grounding a model's answers in retrieved source documents (RAG) improve reliability?

• a.It makes the model train itself continuously
• b.It removes the need to review outputs at all
• c.It reduces hallucination by basing responses on real, relevant source content✓
• d.It lowers the token price to zero

By supplying verified, relevant passages at query time, RAG anchors answers in actual sources, cutting down on fabricated content. It does not eliminate the value of human review or change pricing.

Where in the Amazon Bedrock console can you quickly test prompts against different foundation models before building an application?

• a.AWS CloudTrail
• b.The Bedrock playgrounds (text, chat, and image)✓
• c.AWS Config
• d.Amazon Macie

Bedrock playgrounds let you experiment with prompts and compare model responses interactively before you build. CloudTrail, Macie, and Config are security and governance tools, not prompt-testing surfaces.

An application needs fast, low-cost responses for a simple text-classification task. What is a reasonable model choice?

• a.A smaller, less expensive foundation model that still meets the accuracy requirement✓
• b.Always the largest available model regardless of cost
• c.A text-to-image generation model
• d.A speech-to-text model

Matching model size to task keeps latency and cost down; a smaller model that meets accuracy targets is often the smart choice for simple tasks. The biggest model is not automatically the best fit, and image or speech models suit other tasks.

Where do you typically store the source documents that an Amazon Bedrock knowledge base ingests?

• a.Inside an IAM policy
• b.In an Amazon S3 bucket connected as the knowledge base data source✓
• c.Inside a CloudTrail log
• d.Inside the model's weights

Knowledge bases commonly ingest documents from an Amazon S3 data source, then embed and index them for retrieval. IAM policies grant permissions and CloudTrail logs activity; neither stores the documents.

In which situation is prompt engineering usually sufficient, making fine-tuning unnecessary?

• a.When you must permanently embed many thousands of proprietary domain facts directly into the model itself
• b.When the task can be handled by clearly instructing the model and optionally giving a few examples✓
• c.When the model must adopt an entirely new language it never saw in training
• d.When only a full retraining could possibly meet the requirement

If a well-crafted prompt (optionally with a few examples) reliably produces the needed output, fine-tuning's added cost and effort are unnecessary. Deeply embedding large bodies of knowledge is where customization earns its cost.

An app lets users upload a photo and then ask questions about it in text. Which foundation-model capability does this require?

• a.A multimodal model that accepts both image and text inputs✓
• b.A text-only embeddings model
• c.A speech synthesis model
• d.A time-series forecasting model

Answering text questions about an uploaded image requires a multimodal model that understands both images and text. Text-only, speech, or forecasting models cannot interpret the image content.