AIF-C01 Practice Test 12 — 15 Questions

Question 1

A data governance team wants to enforce fine-grained access control on data in an Amazon S3 data lake used by multiple business units for AI training. Which AWS service should they use to define and manage data permissions at the table and column level?

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

Accepted Answer

AWS Lake Formation. AWS Lake Formation is purpose-built to define and manage fine-grained access control (table, column, row, and cell level) on data stored in Amazon S3 data lakes. It provides a centralized permission model that works across analytics and AI/ML services, making it the correct choice for multi-business-unit governance. IAM alone cannot express column-level permissions on S3 data.

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AWS Identity and Access Management (IAM)

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AWS CloudTrail

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Amazon Macie

Question 2

A company has deployed a model on Amazon SageMaker and enabled Model Monitor. They notice that the model's prediction accuracy has declined over time. Which type of drift is this, and what should they do?

Accepted Answer

This is concept drift; they should retrain the model with new data. The decline in prediction accuracy over time, despite the model and data pipeline remaining the same, is characteristic of concept drift—the relationship between input features and the target variable has changed. The appropriate action is to retrain the model with new data that reflects the current relationship. SageMaker Model Monitor can detect this via accuracy metrics.

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This is bias drift; they should run SageMaker Clarify

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This is model drift; they should delete and redeploy the model

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This is data drift; they should update the training data

Question 3

An e-commerce company is using a foundation model to generate product descriptions. They want to reduce costs by caching frequently requested descriptions. Which AWS service should they use to implement a cache?

Accepted Answer

Amazon ElastiCache. Amazon ElastiCache is the correct choice because it provides an in-memory caching layer (using Redis or Memcached) that can store frequently requested product descriptions, reducing the need to invoke the foundation model repeatedly. This directly lowers inference costs and latency by serving cached responses instead of generating new ones each time.

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Amazon CloudFront

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Amazon DynamoDB

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Amazon S3

Question 4

A company is using Amazon Bedrock to generate marketing content. They want to evaluate the quality of the generated text. Which TWO metrics are most appropriate for evaluating text quality?

Accepted Answer

Perplexity. Perplexity (B) is a standard intrinsic metric for generative language models that measures how well a probability distribution predicts a sample, with lower values indicating the model is less surprised by the text and thus produces more fluent, coherent output. BLEU (E) is a reference-based metric that compares n-gram overlap between generated text and one or more reference texts, making it well suited for evaluating the quality of machine-generated marketing copy against human-written examples. Precision (A), Accuracy (C), and F1 score (D) are classification metrics that require discrete predicted labels and ground-truth classes, so they do not apply to free-form text generation where the output is a sequence of tokens rather than a category.

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Precision

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Accuracy

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F1 score

Question 5

A developer sends the above request to Amazon Bedrock with Anthropic Claude. The model returns a response that stops before reaching 500 tokens. What is the most likely reason?

Accepted Answer

The model reached a stop sequence. The model stopped before reaching 500 tokens because the request likely included a stop sequence (e.g., `

Human:` or a custom stop token) that matched the generated output. When a stop sequence is encountered, Bedrock immediately halts generation, even if the token limit has not been reached. This is the most direct explanation for a premature stop.

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The temperature is set too high

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The model is not trained on this topic

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The token limit is exceeded

Question 6

A developer is building a multimodal application that needs to process both text and images to generate a description. Which Amazon Bedrock model provider offers a multimodal foundation model capable of accepting images and text as input?

Accepted Answer

Anthropic. Anthropic's Claude 3 and Claude 3.5 models on Amazon Bedrock are multimodal, accepting both text and image inputs to generate descriptions. This capability is built into the model's architecture, which processes images alongside text prompts natively, unlike other providers that focus solely on text or image generation.

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Mistral AI

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Cohere

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Stability AI

Question 7

A developer is using Amazon Bedrock's Converse API to build a multi-turn conversation. They notice the model forgets earlier context after a few exchanges. What is the most likely cause?

Accepted Answer

The developer is not sending the previous messages in each request. The Converse API is stateless by design, meaning it does not retain conversation history between API calls. To maintain context across multiple turns, the developer must explicitly include the entire message history (previous user and assistant messages) in each new request. If the developer omits these previous messages, the model has no memory of earlier exchanges and will appear to forget context.

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The API has a rate limit that truncates history

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The model's context window is too small for the conversation

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The model's maximum output length is set too low

Question 8

A developer is building a multi-step reasoning agent using Amazon Bedrock Agents. The agent needs to first check inventory levels via a database query, then call a shipping API to calculate delivery dates, and finally compose a response. How should the developer define the tool integrations?

Accepted Answer

Create action groups for the database query and the shipping API, and configure the agent to use them in its orchestration. Amazon Bedrock Agents natively support multi-step reasoning through action groups, which allow the agent to invoke external tools (e.g., a database query and a shipping API) as discrete steps in its orchestration. The agent uses its built-in chain-of-thought reasoning to decide when to call each action group and how to compose the final response, without requiring external orchestrators like Step Functions or SageMaker Pipelines.

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Define two separate Lambda functions and let the agent orchestrate them via a custom step function

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Use Amazon SageMaker Pipelines to orchestrate the steps

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Embed the logic in a single Lambda function that calls both the database and shipping API

Question 9

A startup needs to build a real-time text translation feature for a customer chat application. Latency must be under 200 ms per request. Which AWS approach is BEST suited?

Accepted Answer

Use Amazon Translate with real-time translation. Amazon Translate's real-time translation API is purpose-built for low-latency text translation, typically achieving sub-200 ms response times for small payloads. It directly translates text without the overhead of running a large foundation model or performing intermediate steps like transcription, making it the best fit for this latency-sensitive chat application.

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Use Amazon Comprehend for language detection and a custom translation model

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Use Amazon Bedrock with a multilingual foundation model

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Use Amazon Transcribe and then Amazon Bedrock

Question 10

A data scientist is building a binary classifier to predict customer churn. The dataset is highly imbalanced (95% non-churn, 5% churn). Which metric should be prioritized for model evaluation?

Accepted Answer

F1 score. In imbalanced datasets, accuracy can be misleading because a model that always predicts the majority class achieves 95% accuracy but fails to identify churners. The F1 score balances precision and recall, making it more informative for the minority class.

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Root Mean Squared Error (RMSE)

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Mean Absolute Error (MAE)

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Accuracy

Question 11

A company uses AWS Lake Formation to manage data lakes for analytics. They want to ensure that only authorized users can access specific columns in a table containing sensitive data used for ML training. Which Lake Formation feature should they use?

Accepted Answer

Column-level permissions. Lake Formation column-level permissions allow fine-grained access control to specific columns within a table.

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Row-level security filters

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Cell-level security

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S3 bucket policies

Question 12

A company wants to build a GenAI application that translates customer emails from English to Spanish. They need to manage multiple versions of translation prompts and track which version performs best. Which TWO features should they use? (Select TWO.)

Accepted Answer

Amazon Bedrock Prompt Management to create and version prompts. Option C is correct because Amazon Bedrock Prompt Management lets you create, store, and version prompts as managed resources, so the company can maintain multiple translation-prompt versions and reference them by ARN rather than hard-coding text. Option D is correct because Bedrock prompt aliases map a stable alias to a specific prompt version, enabling controlled A/B testing (for example, routing traffic between alias variants) so the team can measure which translation prompt performs best. Option A is not appropriate because AWS Lambda is compute, not a prompt-versioning service, and manual version management does not provide Bedrock's native tracking. Option B is not the right fit because Bedrock Studio is a collaborative development workspace, not the mechanism for versioning and comparing prompt variants. Option E is not correct because the Bedrock Playground is for ad-hoc, real-time experimentation and does not provide persistent prompt versioning or A/B test tracking.

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AWS Lambda to manage prompt versions manually

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Amazon Bedrock Studio for collaborative prompt design

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Bedrock Playground for real‑time testing

Question 13

Which AWS service provides a fully managed experience for building generative AI applications with a variety of foundation models through a unified API?

Accepted Answer

Amazon Bedrock. Amazon Bedrock is the correct answer because it is a fully managed AWS service that provides access to a variety of foundation models (FMs) from providers like AI21 Labs, Anthropic, Cohere, Meta, Stability AI, and Amazon through a single, unified API. This allows developers to build and scale generative AI applications without managing underlying infrastructure or model hosting, directly addressing the requirement for a fully managed experience with diverse FMs.

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AWS Lambda

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Amazon SageMaker

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Amazon Rekognition

Question 14

A team is using Amazon Bedrock with a Claude model and wants to ensure responses adhere to a specific output format such as JSON. Which technique should be applied?

Accepted Answer

Include a system prompt with explicit formatting instructions. Amazon Bedrock with Claude models supports system prompts that can include explicit formatting instructions, such as 'Respond in valid JSON format.' This technique directly controls the model's output structure without requiring external tools or retraining, making it the simplest and most effective method for enforcing a specific output format like JSON.

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Use a retrieval-augmented generation (RAG) approach

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Attach a guardrail with a JSON schema

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Customize the model with a JSON training dataset

Question 15

A company uses Amazon Bedrock to generate summarizations of lengthy reports. Users report that the summaries are too verbose and include excessive detail. Which prompt engineering technique should the team apply to address this issue?

Accepted Answer

Add explicit constraints like 'Provide a concise summary in two sentences.'. Adding explicit constraints like 'Provide a concise summary in two sentences' directly instructs the model to limit verbosity and detail. This prompt engineering technique uses clear, specific instructions to control output length and style, which is the most effective way to address overly verbose summaries without altering model parameters or input data.

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Reduce the input context length to limit available information.

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Increase the maxTokens parameter in the inference request.

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Include few-shot examples of desired outputs.