AIF-C01 Fundamentals of Generative AI Practice Test 9 — 15 Questions

Question 1

A financial services firm is evaluating foundation models for a customer-facing assistant. Compliance requires that prompts and completions never leave the firm's own AWS account boundary. Which characteristic of a foundation model deployment should the team evaluate FIRST against this constraint?

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

Accepted Answer

Whether the model is consumed as a fully managed service or deployed into infrastructure the firm controls.. Data-residency and account-boundary requirements are determined by where inference actually executes, so the deployment and consumption model must be assessed before capability metrics. Fully managed endpoints process requests on provider-managed infrastructure, while models deployed into the firm's own account keep prompt and completion data inside that boundary.

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The number of parameters in the model and its published benchmark scores.

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The maximum number of tokens the model can accept in a single request.

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The licensing terms that govern commercial redistribution of the model weights.

Question 2

A financial services company wants to deploy a generative AI assistant that answers questions using its internal policy documents. The team is concerned that the model may invent policies that do not exist. Which approach best reduces this risk while keeping the model's answers tied to the company's actual documents?

Accepted Answer

Use retrieval augmented generation to fetch relevant document passages and include them in the prompt.. Retrieval augmented generation grounds the model by supplying relevant passages from the company's own document repository at inference time. Because the model conditions its answer on retrieved, verifiable content, it is far less likely to invent policies. This directly satisfies the requirement that answers stay tied to actual internal documents rather than relying on the model's parametric memory.

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Switch to a larger foundation model with more parameters to improve factual recall.

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Lower the maximum token limit so the model produces shorter answers with fewer chances to err.

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Increase the model's temperature setting so it explores more diverse phrasings.

Question 3

A data science intern is reading about the transformer architecture that underpins most modern large language models. The intern asks which mechanism allows a transformer to weigh the relevance of every other word in a sentence when encoding a given word, regardless of how far apart the words appear. Which mechanism should you name?

Accepted Answer

Self-attention. Self-attention is the operation that lets each token build its representation from all other tokens in the sequence with learned weights, which is what removes the long-distance constraint of sequential models. Positional encoding, tokenization, and recurrent state propagation all play supporting or unrelated roles, but none of them performs the relevance weighting described in the scenario.

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Positional encoding

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Tokenization

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Recurrent state propagation

Question 4

A content team wants to build an internal tool that drafts blog posts from short outlines. They have no machine learning engineers on staff and want AWS to manage the underlying model infrastructure while they focus only on prompts and output. Which AWS service should they use?

Accepted Answer

Amazon Bedrock. The team needs managed access to generative foundation models with minimal operational overhead, and Amazon Bedrock provides exactly that by serving multiple providers' models through one API without requiring the team to train or host anything. SageMaker AI offers more control but demands ML engineering, while Polly and Rekognition serve speech and vision tasks respectively.

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Amazon SageMaker AI

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Amazon Polly

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Amazon Rekognition

Question 5

A product team wants its generative AI assistant to answer questions about internal policy documents accurately rather than from the model's general training knowledge. Which TWO techniques should they apply? (Choose two.)

Accepted Answer

Instruct the model to answer only from the provided context and to state when the context is insufficient.. Grounding combines retrieval of authoritative source passages with an instruction that limits the model to that evidence and requires an explicit fallback when evidence is missing. Together these reduce reliance on parametric knowledge and make answers traceable to internal documents, which is what accurate policy question answering demands.

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Increase the temperature setting so the model explores a wider range of possible answers.

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Shorten the system prompt to a single sentence to reduce token consumption.

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Ask the model to produce the longest possible answer for every question.

Question 6

A financial services company wants to deploy a generative AI assistant that summarizes internal earnings reports. The reports contain confidential, non-public financial data, and the security team requires that the data never leaves the company's AWS environment or be used to train the provider's public models. The company already uses Amazon Bedrock. Which characteristic of Amazon Bedrock directly satisfies this requirement?

Accepted Answer

Amazon Bedrock does not use customer inputs or outputs to train the base foundation models.. The security requirement is specifically that confidential data must not be used to train the provider's models. Amazon Bedrock's design keeps customer prompts and completions out of base foundation model training, which is the property that satisfies that constraint. Encryption, redaction, and regional pinning are useful but address different risks, so they do not meet the stated training-data isolation requirement.

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Amazon Bedrock restricts all inference to a single AWS Region chosen at account creation.

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Amazon Bedrock provides server-side encryption with AWS KMS keys for data at rest.

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Amazon Bedrock automatically redacts personally identifiable information from all prompts before inference.

Question 7

A media company wants a generative AI assistant that drafts scripts in a distinctive house style. The team has several thousand pages of approved scripts but no labeled input-output pairs, and they want to adapt an existing foundation model rather than train one from scratch. Which approach best matches their data and goal?

Accepted Answer

Continued pre-training on the unlabeled script corpus to adapt the model to the house style. Continued pre-training is designed for exactly this situation: a large body of unlabeled domain text and a desire to shift an existing foundation model toward that domain. Supervised fine-tuning and RLHF both need labels the team does not have, and few-shot prompting cannot absorb thousands of pages of stylistic signal into the model itself.

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Reinforcement learning from human feedback using preference rankings of generated scripts

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Supervised fine-tuning on prompt-completion pairs extracted from the scripts

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Few-shot prompting with three example scripts inserted into each request

Question 8

A machine learning engineer is preparing to fine-tune a foundation model for a specialized legal summarization task. The team has only a few thousand labeled examples and a limited budget. Which fine-tuning approach is most appropriate to adapt the model efficiently under these constraints?

Accepted Answer

Use parameter-efficient fine-tuning that updates only a small subset of adapter weights.. Parameter-efficient fine-tuning, such as LoRA, freezes the base model and trains only a small set of additional parameters. This cuts compute and memory requirements sharply, making adaptation feasible with limited data and budget. It still specializes the model for legal summarization, delivering the task adaptation the team needs without the expense of full retraining or training from scratch.

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Increase the context window size so the model can read entire legal contracts at once.

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Perform full fine-tuning that updates every parameter in the network.

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Train the foundation model from scratch on the legal corpus.

Question 9

A startup is choosing between two foundation models for a summarization feature. Model X is a large general-purpose model with strong benchmark scores; Model Y is a smaller domain-specialized model with lower general benchmarks but excellent results on the startup's own document samples. Cost per token for Model Y is roughly one third of Model X. Which evaluation practice should the team follow?

Accepted Answer

Evaluate both models on a held-out set of representative documents, then weigh measured quality against cost and latency.. Model selection should be driven by measured performance on data that resembles the production workload, combined with operational constraints such as cost and latency. Public benchmarks and price alone are weak proxies, while a held-out evaluation set gives direct evidence of whether the cheaper specialized model meets the quality bar for summarization.

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Select Model Y solely because its cost per token is lower, since summarization quality is equivalent across models.

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Deploy both models and let end users vote on which summaries they prefer before any offline evaluation.

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Select Model X because higher public benchmark scores reliably predict performance on every downstream task.

Question 10

A media company wants to create an internal tool that drafts short news summaries from press releases. They have no machine learning engineers, no labeled training data, and need a working prototype within two weeks. Which approach best describes how they should build this capability using generative AI concepts on AWS?

Accepted Answer

Use a pretrained foundation model through an API and steer its behavior with prompt engineering and few-shot examples.. Foundation models are pretrained on broad corpora and can be adapted to a new task through prompting rather than retraining. Supplying instructions plus a few example summaries in context lets a team with no ML specialists and no labeled dataset produce a functional summarizer within days. Training from scratch or using a vision service does not fit the constraint or the modality.

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Build a rules-based extractive summarizer that selects the first and last sentence of each press release.

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Deploy an Amazon Rekognition custom labels project and post-process its output into summary sentences.

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Train a small transformer model from scratch on the company's archive of press releases using Amazon SageMaker training jobs.

Question 11

A solutions architect is explaining why a foundation model can answer questions about topics it was never explicitly programmed for. Which characteristic of generative AI best explains this behaviour?

Accepted Answer

The model learned statistical patterns and relationships from large-scale training data, allowing it to generalize to new prompts.. Generative models generalize because training over massive datasets encodes statistical patterns and relationships among tokens into the model weights. At inference the prompt is processed against those learned parameters, enabling coherent responses to inputs never seen in training. Retrieval, verbatim lookup, and per-prompt retraining all describe different mechanisms that do not explain inherent generalization.

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The model retrieves live answers from a search index at inference time.

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The model re-trains itself on each user prompt before producing a response.

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The model stores every training example verbatim and looks up the closest match when prompted.

Question 12

A media company is experimenting with an Amazon Bedrock text model to draft short news summaries. The team notices that when they ask the same question twice, the model returns noticeably different wording each time, and sometimes the summary drifts off topic. They want more deterministic, focused responses without retraining the model. Which combination of inference parameters should they adjust to reduce randomness and keep the output on topic?

Accepted Answer

Decrease the temperature and set a lower topP value.. Randomness and topical drift in foundation model output are governed by sampling parameters. Temperature scales the probability distribution, and topP restricts sampling to the smallest set of tokens whose cumulative probability exceeds the threshold. Lowering both concentrates generation on the most likely tokens, yielding more consistent and focused summaries without any model retraining.

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Increase the temperature and increase the topP value.

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Enable streaming responses and lower the topK value to zero.

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Increase maxTokens and decrease the stop sequence length.

Question 13

A media company is evaluating foundation models for a generative AI application that produces image captions and short video summaries. The team must balance output quality, latency, and operational cost. Which TWO considerations are most important when selecting a foundation model for this multimodal task? (Choose two.)

Accepted Answer

Whether the model supports the required input modalities, such as images and video frames.. For a multimodal captioning and summarization workload, the model must accept images and video frames as input, so modality support is a prerequisite. Because the team must also balance latency and cost, evaluating inference speed and pricing against throughput needs is equally critical. Together these ensure the model can handle the data and remain practical to operate at scale.

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Whether the model can generate outputs in multiple human languages simultaneously.

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Whether the model was trained using a specific programming language's libraries.

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Whether the model's license permits the company's intended commercial distribution of outputs.

Question 14

A financial services firm wants its generative AI assistant to answer questions using its private policy documents, which change frequently. The firm does not want to retrain the foundation model each time a document is updated. Which approach best meets these requirements?

Accepted Answer

Use retrieval-augmented generation to fetch relevant passages from a knowledge base and include them in the prompt.. Retrieval-augmented generation decouples knowledge from model weights by retrieving relevant passages at query time and placing them in the prompt. Policy updates then require only re-indexing the documents, avoiding retraining entirely, while answers remain grounded in current private content. Temperature changes and larger context windows do not supply the missing knowledge on their own.

Answer

Expand the model's context window by requesting a larger variant of the same model.

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Fine-tune the foundation model on the policy documents after every update.

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Increase the model's temperature setting so it explores more of its training knowledge.

Question 15

A support organization notices its generative AI assistant sometimes invents policy details that do not exist. Leadership wants a measurable way to track whether this behavior improves over time. Which action should the team take?

Accepted Answer

Build a labeled evaluation set of questions with verified answers and score responses for factual consistency on a recurring basis.. Turning an observed quality problem into a tracked metric requires a fixed evaluation set with verified answers and a scoring procedure that can be rerun after each change. This makes comparisons across prompts, retrieval configurations, and models meaningful, and it establishes a regression baseline so the organization can prove whether fabrication is actually declining.

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Switch to a model with a larger parameter count and assume the fabrication problem disappears.

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Increase the model's maximum output token limit so it has more room to explain itself.

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Rely on customer complaints as the primary signal for whether fabricated policy details are decreasing.