AIF-C01 Practice Test 17 — 15 Questions

Question 1

A company wants to use AWS Lake Formation to govern access to data used for AI training. They need to ensure that only approved columns of sensitive tables are visible to data scientists. Which THREE steps should they implement? (Choose THREE)

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

Accepted Answer

Use AWS Glue crawlers to catalog the data and populate the Data Catalog. The scenario requires governing access to AI training data with Lake Formation and restricting visibility to approved columns, so the solution must include cataloging, registering the data location, and applying column-level grants. Option A is correct because AWS Glue crawlers scan the S3 data, infer schemas, and populate the AWS Glue Data Catalog, which Lake Formation relies on to define and enforce permissions on tables and columns. Option C is correct because Lake Formation supports column-level permissions, allowing you to grant SELECT on only specific columns of a table to the data scientist role, which directly satisfies the requirement that only approved columns be visible. Option E is correct because the S3 bucket containing the training data must be registered with Lake Formation so that Lake Formation manages access to the underlying data and can enforce its table and column permissions. Option B is not required because a SageMaker notebook instance with an IAM role is a compute/access mechanism, not a governance step for restricting column visibility. Option D is not relevant because S3 versioning provides object version retention and recovery, not fine-grained column access control.

Answer

Create a SageMaker notebook instance and attach an IAM role

Answer

Enable S3 versioning on the training data bucket

Question 2

A developer is building a chatbot that must refuse to answer questions about internal financial data. They also need to filter out any offensive language from user inputs. Which TWO Bedrock features should they use? (Choose TWO.)

Accepted Answer

Bedrock Guardrails – topic denial. Option B, Bedrock Guardrails – topic denial, is correct because it lets you define denied topics (such as internal financial data) that the model must refuse to discuss, directly satisfying the requirement to block questions about internal financial data. Option C, Bedrock Guardrails – content filtering, is correct because it detects and filters harmful or offensive content like hate speech, insults, and profanity in both inputs and outputs, which addresses the need to filter offensive language from user inputs. Option A, Bedrock Agent, is not correct because it orchestrates tasks and API calls for action-taking workflows, not content refusal or profanity filtering. Option D, prompt engineering with negative instructions, is not correct because prompt-level instructions are not a reliable enforcement mechanism and lack the managed policy controls of Guardrails. Option E, Bedrock Knowledge Base, is not correct because it is a RAG feature for retrieving and grounding answers in data sources, not for denying topics or filtering offensive language.

Answer

Bedrock Agent

Answer

Prompt engineering with negative instructions

Answer

Bedrock Knowledge Base

Question 3

A company wants to use Amazon Bedrock to generate product descriptions for an e-commerce catalog. They need to process 100,000 product records efficiently and cost-effectively. Which inference option should they choose?

Accepted Answer

Batch inference. Batch inference is the correct choice because it is designed for asynchronous, high-volume processing of large datasets like 100,000 product records. It processes requests in bulk, significantly reducing per-record cost compared to real-time options, and is ideal for non-latency-sensitive workloads such as generating product descriptions for an entire catalog.

Answer

On-demand inference

Answer

Model caching

Answer

Provisioned throughput

Question 4

A developer is using Bedrock Agents to create a travel planning assistant. The agent needs to call a hotel booking API and a flight API. What is the correct way to define these external API calls in Bedrock Agents?

Accepted Answer

Create action groups with the API schemas and Lambda functions. Action groups in Bedrock Agents define the tools (APIs) that the agent can call, including the OpenAPI schema and Lambda function integration. Knowledge Bases are for RAG, not API calls. Guardrails are for content filtering. The playground is for testing prompts.

Answer

Hardcode the API endpoints in the agent's prompt

Answer

Configure the APIs as guardrails content filters

Answer

Define the APIs as knowledge base data sources

Question 5

An enterprise deploys a foundation model on Amazon Bedrock with a knowledge base. Users report that the model is returning outdated information. What is the most likely cause?

Accepted Answer

The knowledge base data source is not refreshed. When a knowledge base is attached to a foundation model on Amazon Bedrock, the model retrieves information from the data source to augment its responses. If the data source is not refreshed, the model will return outdated information even if the model itself is current. Option C directly addresses this by identifying the stale data source as the root cause.

Answer

The model was fine-tuned

Answer

The model is not the latest version

Answer

The inference parameters are incorrect

Question 6

Refer to the exhibit. A developer is optimizing latency for a generative AI model deployed on SageMaker. Based on the exhibit, which change would most likely reduce per-token latency?

Accepted Answer

Reduce model size through quantization. Reducing model size through quantization directly decreases the computational and memory requirements per inference step, which lowers the time to generate each token. This is especially effective on GPU instances where smaller models fit better in GPU memory and reduce memory bandwidth bottlenecks, leading to lower per-token latency.

Answer

Use a CPU instance

Answer

Switch to a larger instance type

Answer

Increase batch size to 10

Question 7

A company is building a multi-modal application that processes images and text to answer questions about product defects. Which foundation model approach is BEST?

Accepted Answer

Use a multi-modal foundation model that processes both images and text. Multi-modal foundation models (e.g., CLIP, Flamingo, GPT-4V) are specifically designed to jointly process and reason over images and text in a unified architecture. This allows the model to directly correlate visual defects with textual descriptions without intermediate lossy transformations, making it the most effective approach for a multi-modal QA task.

Answer

Use an image captioning model and then analyze the caption text

Answer

Use a text-to-image generation model and analyze the generated image

Answer

Use a separate image analysis model and a text model, then combine outputs

Question 8

A team is developing a real-time code completion feature using an LLM deployed on Amazon SageMaker. They observe high latency under load. Which optimization technique should they prioritize?

Accepted Answer

Use model quantization. Model quantization reduces the precision of the model's weights (e.g., from FP32 to INT8), which decreases memory footprint and computational requirements, leading to lower latency per inference. This is the most effective optimization for real-time code completion because it directly reduces the time to generate each token without requiring additional infrastructure changes.

Answer

Increase batch size

Answer

Switch to a larger instance type

Answer

Increase instance count with Auto Scaling

Question 9

A team is using Amazon SageMaker to deploy a real-time inference endpoint. The endpoint must be accessible only from a specific IP range and must automatically scale based on request volume. Which configuration meets these requirements?

Accepted Answer

Configure the endpoint with a VPC and attach a security group that allows inbound traffic from the IP range, and enable automatic scaling for the endpoint.. Amazon SageMaker endpoints can be deployed within a VPC, allowing you to attach a security group that restricts inbound traffic to a specific IP range. Additionally, SageMaker supports automatic scaling for real-time endpoints using Application Auto Scaling, which adjusts the number of instances based on request volume metrics like InvocationsPerInstance.

Answer

Deploy the endpoint in a public subnet with a security group allowing the IP range.

Answer

Deploy the endpoint with a VPC and use a Network Load Balancer with target group health checks.

Answer

Deploy the endpoint with an AWS WAF ACL to filter by IP and enable auto scaling for the endpoint.

Question 10

A company wants to use a foundation model to automatically moderate user-generated content. The model must filter out inappropriate content with high accuracy. Which Amazon service is best suited for this task?

Accepted Answer

Amazon Comprehend. Amazon Comprehend is the correct choice because it is a natural language processing (NLP) service that can analyze text for sentiment, key phrases, and — critically — toxicity and inappropriate content using built-in or custom classifiers. This directly matches the requirement to moderate user-generated text with high accuracy, as it can detect hate speech, profanity, and other harmful language.

Answer

Amazon Translate

Answer

Amazon Rekognition

Answer

Amazon Polly

Question 11

Which TWO practices help ensure transparency in AI systems? (Choose 2)

Accepted Answer

Use model-agnostic explainability tools like SHAP. Option B is correct because model-agnostic explainability tools such as SHAP (SHapley Additive exPlanations) quantify each feature's contribution to a prediction using Shapley values from cooperative game theory, making the model's decision logic visible to stakeholders regardless of the underlying algorithm. Option D is correct because documenting model limitations, intended use, and data sources (as in model cards or datasheets for datasets) gives users the information needed to understand when and how a model's outputs can be trusted, which is a core requirement of transparency. Option A is not correct because combining multiple models to obscure decision logic deliberately hides how outputs are produced, which reduces rather than improves transparency. Option C is not correct because dropping all but the most predictive features is a feature-selection technique for performance or simplicity; it does not by itself explain decisions or disclose limitations and data provenance. Option E is not correct because black-box models intentionally conceal internal reasoning to protect proprietary algorithms, which is the opposite of transparency.

Answer

Combine multiple models to obscure decision logic

Answer

Remove all features except the most predictive ones

Answer

Use black-box models to protect proprietary algorithms

Question 12

A company is deploying a generative AI application using Amazon Bedrock and needs to optimize costs for a high-volume, latency-tolerant workload. Which TWO strategies should they implement? (Select TWO.)

Accepted Answer

Use Batch Inference for asynchronous processing. Option A is correct because Amazon Bedrock Batch Inference lets you submit large volumes of prompts as an asynchronous job, which is priced at a discount (typically 50%) compared to on-demand inference and is ideal for latency-tolerant workloads that don't need immediate responses. Option C is correct because selecting a smaller, more efficient foundation model reduces the number of parameters processed per token, directly lowering per-token input/output costs while still meeting the application's quality needs. Option B is not appropriate because fine-tuning a large model adds training costs and does not reduce per-inference pricing for a high-volume workload. Option D is wrong because Provisioned Throughput reserves dedicated capacity at a fixed hourly commitment, which is more expensive for spiky or latency-tolerant batch workloads than on-demand or batch pricing. Option E is incorrect because Bedrock does not offer a built-in response/model caching feature that avoids redundant inference charges, so it is not a valid cost-optimization strategy here.

Answer

Deploy a large model and fine-tune it

Answer

Enable Provisioned Throughput for guaranteed capacity

Answer

Implement model caching to avoid redundant inferences

Question 13

A company wants to use Amazon Bedrock to generate personalized marketing emails. They have thousands of customer profiles with demographic data. To generate tailored content efficiently, the application must dynamically insert customer-specific information into prompts. Which prompt management technique is BEST suited for this?

Accepted Answer

Prompt flows with variables. Prompt flows with variables allow the application to dynamically insert customer-specific data (e.g., name, age, purchase history) into a base prompt template at runtime. This technique avoids retraining or switching models for each customer, enabling efficient, personalized content generation without modifying the underlying foundation model.

Answer

Using a larger foundation model to understand the entire customer base

Answer

Fine-tuning the model on all customer profiles

Answer

Prompt versioning

Question 14

A company needs to select a vector store for their Amazon Bedrock Knowledge Base. Which TWO options are supported as vector stores? (Choose TWO.)

Accepted Answer

Amazon Aurora pgvector. Amazon Aurora pgvector (Option A) is a supported vector store for Amazon Bedrock Knowledge Bases because Aurora PostgreSQL supports the pgvector extension, allowing it to store and query embeddings via vector similarity search. Amazon OpenSearch Serverless (Option B) is also supported, as Bedrock Knowledge Bases can use an OpenSearch Serverless vector search collection as the backing vector index for embeddings. Amazon Redshift (Option C) is a data warehouse and is not a supported vector store for Bedrock Knowledge Bases. Amazon RDS for MySQL (Option D) does not natively support vector embeddings in the way required by Bedrock Knowledge Bases. Amazon DynamoDB (Option E) is a key-value and document database and is not a supported vector store for this purpose.

Answer

Amazon Redshift

Answer

Amazon RDS for MySQL

Answer

Amazon DynamoDB

Question 15

An application uses this configuration to enable RAG. What is required for the knowledge base to function?

Accepted Answer

The knowledge base must have a vector index configured in Amazon OpenSearch Serverless. For a knowledge base to function in a RAG (Retrieval-Augmented Generation) setup on AWS, the knowledge base must have a vector index configured in Amazon OpenSearch Serverless. This vector index stores the embeddings generated from the source documents, enabling efficient similarity search to retrieve relevant context for the agent. Without a vector index, the knowledge base cannot perform the vector search required to fetch relevant document chunks.

Answer

The agent must have internet access to retrieve documents

Answer

The embedding model ARN must include the account ID

Answer

The embedding model must be fine-tuned on the domain data