AIF-C01 Fundamentals of Generative AI Practice Test 10 — 15 Questions

Question 1

A financial services firm is deploying a generative AI assistant that answers employee questions about internal policies. Compliance requires that every response cite the exact policy document and section used. The assistant currently relies only on the foundation model's pretrained knowledge and frequently invents policy details. Which technique should the team implement to ground responses in the firm's own documents and produce citations?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Implement Retrieval Augmented Generation by embedding the policy documents in a vector store and retrieving relevant passages at query time.. Grounding a model in proprietary content requires supplying that content at inference time rather than relying on pretrained weights. Retrieval Augmented Generation retrieves the most relevant document chunks and places them in the prompt, so the model's answer is conditioned on real policy text and can reference the source document and section. Sampling or length adjustments do not add knowledge.

Answer

Increase the model's temperature setting so it explores more of its pretrained knowledge about corporate policy.

Answer

Lower the maximum token limit so the assistant gives shorter answers that are less likely to contain errors.

Answer

Fine-tune the foundation model on a large corpus of public financial regulations.

Question 2

A solutions architect must choose a foundation model for an application that summarizes lengthy internal audit reports. The reports average 60,000 tokens, and the summaries must reflect details from the beginning, middle, and end of each document. Cost per request matters, but recall of details is the top priority. Which model characteristic should drive the selection?

Accepted Answer

The model's maximum context window length. When a single request must contain an entire long document, the maximum context window is the hard constraint that decides feasibility. Parameter count, output modality, and latency influence quality or experience but do not determine whether 60,000 tokens can be processed without losing the details the task requires.

Answer

The model's inference latency percentile

Answer

The number of parameters in the model

Answer

The model's supported output modalities

Question 3

A healthcare startup is building a patient-education chatbot. They want the model to answer only using an approved set of clinical guideline documents, and they must be able to update those documents weekly without retraining any model. They plan to use Amazon Bedrock. Which approach best meets these requirements?

Accepted Answer

Use Retrieval Augmented Generation by storing the guidelines in a vector store and retrieving relevant passages at inference time.. Retrieval Augmented Generation separates knowledge from model weights. The approved guidelines live in a vector store that can be updated weekly, and relevant passages are retrieved and passed to the model as context. This grounds answers in approved content and avoids the cost and delay of fine-tuning for every content change.

Answer

Fine-tune a foundation model in Amazon Bedrock each week on the updated guideline documents.

Answer

Increase the model's context window by raising the maxTokens parameter for every request.

Answer

Create a separate Bedrock agent for each clinical guideline document and route questions by keyword.

Question 4

A developer is prompting a foundation model to classify customer feedback into categories. The model sometimes returns extra commentary along with the category label, breaking downstream parsing. The developer wants more deterministic, tightly formatted output without retraining the model. Which technique best addresses this?

Accepted Answer

Set the temperature to zero and constrain the output using a structured format specification.. Setting temperature to zero makes generation greedy and deterministic, while a structured output specification constrains the response to an exact schema. This combination eliminates extraneous commentary and guarantees a parseable label, achieving the desired formatting control without any model retraining. It directly targets both the randomness and the format-enforcement gaps causing the parsing failures.

Answer

Add more few-shot examples that include verbose explanations of each category.

Answer

Increase the maximum output token count to give the model more room to respond.

Answer

Increase the top-p sampling value to broaden the token selection pool.

Question 5

A media company is evaluating foundation models for an application that will summarize long earnings-call transcripts in English for internal analysts. The transcripts are up to 40,000 words, and the summaries must remain faithful to the source. Which TWO model characteristics are most important to evaluate for this workload? (Choose two.)

Accepted Answer

The model's quality on text summarization and faithfulness to source content. Long transcripts demand a model whose context window can accommodate the input, and financial summarization demands strong faithfulness so the output does not distort the source. Those two characteristics together determine whether the application can produce trustworthy summaries without excessive chunking. Image generation, speech quality, and edge deployment do not address the stated requirements.

Answer

The model's ability to run on edge devices without network connectivity

Answer

The model's text-to-speech voice quality

Answer

The model's ability to generate images from text

Question 6

A product team is comparing foundation models for a customer-facing FAQ bot. They need low latency for real-time chat and want to pay only for what they use without managing servers. Which combination of model characteristic and AWS consumption model best fits these requirements?

Accepted Answer

A smaller, lower-latency model invoked on demand so billing is based on input and output tokens processed.. Real-time chat favors models that emit tokens quickly, and a smaller model usually wins on latency. On-demand invocation bills by tokens processed, so a variable-traffic FAQ bot pays only for actual usage and the provider handles scaling and infrastructure. Provisioned capacity and self-hosted GPUs suit steady high volume but add cost and operational burden here.

Answer

A large model with the highest parameter count, deployed on a provisioned throughput commitment billed hourly.

Answer

An open-weight model downloaded and hosted on AWS Lambda for per-request execution.

Answer

A multimodal model with image understanding, called through a self-managed Amazon EC2 GPU instance.

Question 7

A financial services firm is deploying a generative AI assistant that answers employee questions about internal policies. Compliance requires that every response cite the exact policy document and section used. The assistant currently relies only on the foundation model's pretrained knowledge and frequently invents policy details. Which technique should the team implement to ground responses in the firm's own documents and produce citations?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Instruct the model to answer only from the supplied context and to state when the context is insufficient. Grounding comes from combining retrieval of authoritative passages with instructions that confine the model to that evidence and permit abstention. Together they make answers traceable and suppress invention. Higher temperature, larger parameter counts, and removal of system instructions either increase variability, fail to supply evidence, or discard the constraints that keep output faithful.

Answer

Increase the temperature setting so the model explores a wider range of responses

Answer

Remove all system instructions so the model can respond more naturally to each question

Answer

Expand the model's parameter count by switching to the largest available variant

Question 8

A marketing team wants to build a generative AI application that produces short promotional copy and accompanying images for product launches. They are evaluating Amazon Bedrock and want to understand which statements accurately describe its capabilities for this use case. (Choose two.)

Accepted Answer

Amazon Bedrock supports both text generation models and image generation models.. Amazon Bedrock offers a unified API across many foundation models and includes both text and image generation capabilities, which fits a promotional copy and imagery workload. It is serverless with respect to model hosting, is available in many Regions, and does not guarantee legal compliance, so the two accurate capability statements are the multi-provider single API and support for text and image models.

Answer

Amazon Bedrock requires customers to provision and manage the underlying GPU infrastructure for each model.

Answer

Amazon Bedrock only supports models hosted in a single AWS Region worldwide.

Answer

Amazon Bedrock automatically guarantees that generated promotional claims are legally compliant.

Question 9

A healthcare analytics team is evaluating whether a generative AI solution is appropriate for summarizing patient intake notes into structured clinical fields. They are concerned about the model producing confident but incorrect medical details. Which TWO practices best reduce the risk of fabricated content in this scenario? (Choose two.)

Accepted Answer

Instruct the model to respond with a specific phrase such as 'not stated' when the source note does not contain a requested field.. Fabrication drops when the model is anchored to the actual source text and is given permission to report that information is absent. Supplying the intake note in the prompt keeps generation evidence-based, and defining an explicit abstention phrase prevents the model from inventing values for missing fields. Randomness and self-reported confidence do not reliably reduce hallucination.

Answer

Ask the model to state its confidence as a percentage after each extracted field and trust fields scored above ninety percent.

Answer

Fine-tune the model on the entire historical patient record database to expand its clinical knowledge.

Answer

Raise the temperature to its maximum so the model produces diverse clinical phrasing across repeated runs.

Question 10

A developer sets a low temperature value when calling a foundation model through Amazon Bedrock for a use case that extracts structured fields from invoices. A colleague argues that lowering temperature reduces hallucination and guarantees correct extraction. How should the developer respond?

Accepted Answer

Lowering temperature makes sampling more deterministic, which improves consistency but does not guarantee factual correctness or eliminate hallucination.. Temperature governs the randomness of token sampling, so lowering it makes outputs more deterministic and repeatable. Determinism is not the same as correctness: a model can consistently produce the same wrong field value, and hallucinations can persist even at very low temperature. Accurate extraction therefore still depends on model capability, prompt design, and validation logic.

Answer

Lowering temperature forces the model to retrieve the correct field values from the invoice image before generating text.

Answer

Lowering temperature disables the model's ability to generate any text that was not present in its training data.

Answer

Lowering temperature guarantees the response will conform to the requested JSON schema without any prompt instructions.

Question 11

A product team is designing an internal tool that helps engineers understand a large legacy codebase. They want the assistant to explain functions, suggest refactors, and answer questions about dependencies. The team is comparing a general-purpose foundation model with a model pre-trained specifically on source code. Which consideration most strongly favors the code-specialized model?

Accepted Answer

Its pre-training corpus emphasized programming languages and code structure, improving performance on code comprehension tasks. Domain specialization during pre-training is what gives a code-focused model an edge on comprehension, refactoring, and dependency reasoning, because its training data emphasized programming constructs. Context window size, determinism, and knowledge of private repositories are independent of the training domain and are not improved simply by specializing on code.

Answer

It is guaranteed to have a larger context window than any general-purpose model

Answer

It will always produce deterministic output for identical prompts

Answer

It eliminates the need for retrieval or context when answering questions about private repositories

Question 12

A data science team is comparing two approaches for customizing a foundation model in Amazon Bedrock for a domain-specific classification task. Approach one is providing a small set of labeled examples directly in the prompt for each request. Approach two is fine-tuning the model on a larger labeled dataset. The team wants the lowest operational overhead and the fastest way to start, and their label set is small and changes frequently. Which statement best describes the trade-off they should consider?

Accepted Answer

Prompt-based examples offer lower operational overhead and adapt quickly to changing labels, while fine-tuning can improve consistency when a larger stable dataset is available.. The team's constraints are low operational overhead, fast start, and a small label set that changes often. Few-shot prompting embeds labeled examples in each request, needs no training job, and updates instantly. Fine-tuning is more appropriate for larger, stable datasets where consistent behavior is worth the training and maintenance effort, so it is not the best fit here.

Answer

Prompt-based examples require a dedicated training job in Amazon Bedrock, so they carry the same operational overhead as fine-tuning.

Answer

Fine-tuning eliminates the need for any labeled data because the model learns the task from unlabeled inputs automatically.

Answer

Fine-tuning is preferred because it always produces higher accuracy than prompt-based examples regardless of dataset size.

Question 13

A developer is explaining how a large language model generates text so that a business stakeholder understands why the same prompt can yield different answers. Which description accurately captures the generation process?

Accepted Answer

The model predicts a probability distribution over the next token, samples from it, and appends the chosen token to repeat the process.. Text generation is autoregressive and probabilistic. At each step the model computes a distribution over the next token conditioned on everything before it, a token is sampled according to settings such as temperature and top-p, and the sequence grows one token at a time. Stochastic sampling is why identical prompts can yield different outputs, and why temperature changes affect variability.

Answer

The model queries an external search index and paraphrases the top-ranked web page for the prompt.

Answer

The model searches a stored database of previously written sentences and returns the closest exact match to the prompt.

Answer

The model compiles the prompt into a set of if-then rules that deterministically map keywords to canned responses.

Question 14

A hospital's IT team wants a generative AI assistant that answers patient-billing questions using only the hospital's internal policy documents, without retraining the model. Which approach should they use?

Accepted Answer

Retrieval Augmented Generation (RAG) by querying a vector store of the policy documents and passing retrieved passages to the model. Retrieval Augmented Generation supplies the model with relevant, current policy text at query time, so responses are grounded in the hospital's own documents. Fine-tuning changes model weights and needs retraining, temperature alters sampling randomness, and a system prompt alone provides no source content. Only retrieval of the policy passages gives accurate, updatable answers without retraining.

Answer

Fine-tuning the foundation model on the full set of policy documents

Answer

Increasing the model's temperature setting so it explores more of its pretrained knowledge

Answer

Prepending a system instruction telling the model to answer only from hospital policy

Question 15

A media company's generative AI writing assistant produces fluent but sometimes fabricated statistics. The team wants to reduce these hallucinations without changing the foundation model. Which action best addresses the root cause?

Accepted Answer

Ground the model's responses by supplying verified reference documents in the prompt and instructing it to cite them. Fabricated statistics stem from the model generating text without an authoritative factual anchor. Supplying verified documents and requiring citations ties each claim to a source, which reduces invention while leaving the model untouched. Token limits, parameter count, and sampling parameters influence length, capacity, and randomness respectively, none of which ground output in verified facts.

Answer

Raise the top-p sampling value to make token selection more deterministic

Answer

Lower the maximum token limit for each response

Answer

Switch the model to a smaller parameter count to reduce creativity