AIF-C01 Practice Exam 3 — 20 Questions

Question 1

A company is using Amazon SageMaker to manage the lifecycle of their machine learning models. They need to implement a governance framework that includes model versioning, monitoring for drift, and decommissioning of outdated models. Which THREE AWS services or features should they use together to meet these requirements? (Select THREE.)

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

Accepted Answer

SageMaker Pipelines. SageMaker Model Registry (C) is the correct service for model versioning and governance, as it maintains a catalog of model versions with metadata, approval statuses, and lineage, enabling tracking and controlled promotion of models through their lifecycle. SageMaker Model Monitor (E) is correct because it continuously monitors deployed endpoints for data drift, model quality drift, bias drift, and feature attribution drift, alerting when the model deviates from baseline behavior. SageMaker Pipelines (B) is correct because it provides CI/CD orchestration to automate the ML workflow, including training, evaluation, registration to the Model Registry, and deployment, which supports governance and decommissioning through repeatable, versioned pipeline executions. AWS CloudTrail (A) only records API activity for auditing and does not provide model versioning, drift monitoring, or lifecycle decommissioning, so it does not meet the requirements. SageMaker Role Manager (D) merely helps create and manage IAM roles and permissions for SageMaker personas; it does not provide versioning, drift detection, or model decommissioning capabilities.

Answer

AWS CloudTrail

Answer

SageMaker Role Manager

Question 2

A data scientist is designing a RAG pipeline using Amazon Bedrock Knowledge Bases. They need to store embeddings of document chunks and perform similarity searches. Which vector store is a serverless option that integrates directly with Bedrock Knowledge Bases?

Accepted Answer

Amazon OpenSearch Serverless. Amazon Bedrock Knowledge Bases natively integrates with Amazon OpenSearch Serverless as a vector store for storing embeddings and performing similarity searches. OpenSearch Serverless is a fully serverless option that automatically scales and requires no infrastructure management, making it the correct choice for a serverless vector store that works directly with Bedrock Knowledge Bases.

Answer

Amazon Aurora with pgvector

Answer

MongoDB Atlas

Answer

Pinecone

Question 3

A data scientist is prototyping a text summarisation application using Amazon Bedrock. They want to quickly test different foundation models and prompts without writing code. Which tool should they use?

Accepted Answer

Amazon Bedrock Playground. Amazon Bedrock Playground is the correct tool because it provides a no-code, web-based interface for interactively testing and comparing different foundation models and prompts directly within the AWS Management Console. This allows the data scientist to quickly iterate on prompt engineering and model selection without writing any code, which perfectly matches the requirement to prototype a text summarization application.

Answer

Amazon SageMaker Studio

Answer

Amazon Bedrock Agents

Answer

Amazon Bedrock Model Evaluation

Question 4

A data scientist wants to quickly build a supervised learning model for binary classification on a tabular dataset with 10,000 rows and 200 features. The dataset has some missing values and requires minimal code. Which AWS service should the data scientist use?

Accepted Answer

Amazon SageMaker Autopilot. Amazon SageMaker Autopilot is the correct choice because it automatically performs data preprocessing (including handling missing values), feature engineering, model selection, and hyperparameter tuning for supervised learning tasks like binary classification. It requires minimal code—users can simply point to a tabular dataset in Amazon S3 and specify the target column, and Autopilot will automatically train and evaluate multiple candidate models, making it ideal for quickly building a binary classifier on a 10,000-row, 200-feature dataset with missing values.

Answer

Amazon SageMaker Studio Lab

Answer

Amazon SageMaker Clarify

Answer

Amazon SageMaker JumpStart

Question 5

A company wants to build a system that automatically routes support tickets to the appropriate department based on the ticket text. The system must handle new categories that emerge over time without retraining. Which TWO approaches should the company combine to achieve this? (Select TWO.)

Accepted Answer

Use Amazon Comprehend for custom classification. Option B is correct because Amazon Comprehend custom classification can categorize ticket text into departments, providing the routing decision. Option E is correct because Amazon Kendra indexes support documents and uses semantic search to match ticket text to the most relevant department knowledge base, allowing new or emerging categories to be handled through indexed content without retraining the classifier. Together, Comprehend custom classification and Kendra enable routing that adapts to new categories by using Kendra to surface relevant department knowledge for unfamiliar tickets. Option A is incorrect because Amazon Forecast predicts time-series values like ticket volume, not ticket content or routing. Option C is incorrect because Comprehend topic modeling and entity detection discover topics and entities but do not directly route tickets to departments. Option D is incorrect because Amazon Rekognition analyzes images and video, not support ticket text.

Answer

Use Amazon Forecast to predict ticket volume

Answer

Use Amazon Comprehend for topic modeling and entity detection

Answer

Use Amazon Rekognition to analyze ticket images

Question 6

Which of the following is a key benefit of using foundation models over training a model from scratch for natural language tasks?

Question 7

A company is using Amazon Bedrock Agents to build a travel booking assistant that can search for flights, book hotels, and answer questions about travel policies. Which TWO components are required to enable the agent to call external services? (Select TWO.)

Accepted Answer

Action group with API schema. Option A (Action group with API schema) is correct because an action group defines the set of tasks the Bedrock Agent can perform and includes an OpenAPI schema that describes the external APIs the agent can invoke, which is the mechanism for calling external services. Option B (AWS Lambda function) is correct because Bedrock Agents use Lambda functions as the compute layer to execute the business logic for action groups, receiving the agent's request and returning the API response. Option C (Amazon DynamoDB table) is not required; it is only a possible data store for the Lambda function's implementation, not a component that enables external service calls. Option D (Amazon SageMaker endpoint) is not required; it is used for hosting custom ML models, not for enabling agent action groups. Option E (Bedrock Knowledge Base) is not required; it provides retrieval-augmented generation over data sources for answering questions, not for calling external services.

Answer

Amazon DynamoDB table

Answer

Amazon SageMaker endpoint

Answer

Bedrock Knowledge Base

Question 8

A company wants to build a generative AI application that can summarize customer support tickets. They need to ensure the model stays up-to-date with the latest product documentation without retraining. Which AWS service would best support this requirement?

Accepted Answer

Amazon Bedrock with Retrieval Augmented Generation (RAG). Amazon Bedrock with Retrieval Augmented Generation (RAG) is the correct choice because it allows the generative AI model to access and incorporate the latest product documentation from an external knowledge base without retraining. RAG works by retrieving relevant document chunks at inference time and injecting them into the model's context, ensuring responses reflect current information. This directly meets the requirement for staying up-to-date with evolving documentation while avoiding the cost and latency of full model retraining.

Answer

Amazon Comprehend

Answer

Amazon Rekognition

Answer

Amazon SageMaker Ground Truth

Question 9

A company deploys a large language model to automatically generate product descriptions. They want to ensure customers are aware that the content is AI-generated, as part of transparency requirements. What should they implement?

Accepted Answer

Include a disclosure statement such as 'This content was generated by AI' in the output. Transparency requirements for AI-generated content are best met by clearly disclosing to the end user that the content was generated by AI. Including a visible disclosure statement in the output directly informs customers at the point of consumption. This satisfies emerging AI transparency regulations and ethical guidelines.

Answer

Use Amazon Rekognition to add a visible watermark to images only

Answer

Store metadata in the content database indicating AI generation

Answer

Embed an invisible watermark in the generated text

Question 10

A company is building a RAG application using Amazon Bedrock Knowledge Bases. They want to ensure that the model only answers based on the retrieved documents and does not use its internal knowledge. Which configuration should they use?

Accepted Answer

Enable the 'Only use retrieved context' option in the Knowledge Base configuration. Amazon Bedrock Knowledge Bases provides a specific configuration toggle called 'Only use retrieved context' that forces the foundation model to generate responses solely from the information in the retrieved document chunks, ignoring any internal knowledge the model may have been trained on. This ensures strict adherence to the retrieved context, which is critical for RAG applications requiring factual grounding.

Answer

Set the model temperature to 0

Answer

Use a fine-tuned model that has only seen the company's documents

Answer

Use a Guardrail with topic denial for out-of-scope topics

Question 11

A data science team is evaluating the output quality of a text generation model. They want to use both automated metrics and human judgment. Which THREE approaches should they include in their evaluation strategy? (Select THREE.)

Accepted Answer

Conduct human evaluation with a rating rubric. Option A is correct because human evaluation with a rating rubric directly captures subjective quality dimensions such as fluency, coherence, relevance, and factual consistency that automated metrics cannot fully assess, which is essential when the team explicitly wants human judgment. Option D is correct because ROUGE (recall-oriented n-gram/LCS overlap) and BLEU (precision-oriented n-gram overlap with brevity penalty) are standard automated reference-based metrics for comparing generated text against reference texts, matching the team's goal of using automated metrics. Option E is correct because running the model on a task-specific benchmark dataset provides standardized, objective, task-relevant performance measurement (e.g., accuracy, F1, exact match) that complements both human ratings and text-overlap metrics. Option B does not belong because inference latency under load measures operational performance and scalability, not output quality of the generated text. Option C does not belong because perplexity is typically computed on a language model's token likelihoods over a corpus and is not a reliable measure of the quality of generated text, especially for open-ended generation.

Answer

Measure inference latency under load

Answer

Compute perplexity on the generated text

Question 12

A company is using Amazon Bedrock to generate responses for customer support. They want to ensure that the model does not expose personally identifiable information (PII) in its outputs. Which AWS feature can be configured to automatically redact PII from model responses?

Accepted Answer

Amazon Bedrock Guardrails. Amazon Bedrock Guardrails is the correct choice because it provides configurable policies that can automatically detect and redact personally identifiable information (PII) from model inputs and outputs. This feature is specifically designed for Amazon Bedrock to enforce content safety and compliance requirements, including PII redaction, without requiring custom code or external services.

Answer

Amazon Macie

Answer

Amazon SageMaker Model Monitor

Answer

AWS CloudTrail

Question 13

Which TWO statements about the bias-variance tradeoff are correct? (Choose TWO.)

Accepted Answer

High bias typically leads to underfitting. Option C is correct because high bias means the model makes overly simplistic assumptions about the data, so it fails to capture the underlying pattern and systematically underfits both training and test data. Option E is correct because high variance means the model is overly sensitive to the particular training samples, fitting noise as well as signal, which produces excellent training performance but poor generalization—the hallmark of overfitting. Option A is wrong because high bias leads to underfitting, not overfitting; overfitting is associated with high variance. Option B is wrong because high variance models are highly sensitive to changes in training data, not insensitive. Option D is wrong because increasing model complexity typically increases variance (and decreases bias), not decreases variance.

Answer

High bias typically leads to overfitting

Answer

High variance models are insensitive to changes in training data

Answer

Increasing model complexity typically decreases variance

Question 14

A research organization is using Amazon SageMaker Studio to collaborate on building machine learning models. The security policy requires that all data and code remain within a VPC and cannot be accessed from the public internet. Additionally, the organization wants to enforce that only approved base images are used for the Studio environment. How should the organization configure SageMaker Studio to meet these requirements?

Accepted Answer

Configure the Studio domain in VPC Only mode and use a SageMaker Studio lifecycle configuration to restrict the list of available base images to those in a private Amazon ECR repository.. Configuring the SageMaker Studio domain in VPC Only mode ensures that all data and code remain within the VPC and cannot be accessed from the public internet. Additionally, using a lifecycle configuration script to restrict available base images to those in a private Amazon ECR repository enforces the policy that only approved base images are used, as the lifecycle configuration can modify the Jupyter Server settings to limit the image registry.

Answer

Use an AWS Transit Gateway to connect the VPC, and enforce HTTPS for all traffic.

Answer

Configure the Studio domain to disable direct internet access, and let users choose any base image from the SageMaker public registry.

Answer

Use AWS CloudFormation to create a Studio domain in a VPC and rely on individuals to use approved images.

Question 15

A company uses Amazon SageMaker to build and deploy models. They want to enforce compliance that all model endpoints are encrypted in transit and use least privilege access. Which THREE steps should they take? (Choose THREE.)

Accepted Answer

Configure the SageMaker endpoint to use a custom SSL certificate via AWS Certificate Manager. Configuring a SageMaker endpoint to use a custom SSL certificate from AWS Certificate Manager (ACM) ensures that all data transmitted between clients and the endpoint is encrypted in transit using TLS. This enforces the compliance requirement for encryption in transit by replacing the default SageMaker certificate with a customer-managed certificate, which can be validated and rotated as needed.

Answer

Enable AWS CloudTrail to log all endpoint invocations

Answer

Disable root access on the SageMaker notebook instances

Question 16

A company wants to use Amazon Bedrock to translate customer emails from English to Spanish. The emails contain occasional personal names and addresses. Which Guardrail configuration should be applied to protect customer privacy?

Accepted Answer

PII detection. PII detection in Bedrock Guardrails can identify and redact personally identifiable information such as names and addresses. Content filtering, topic denial, and grounding check do not specifically target PII.

Answer

Grounding check

Answer

Topic denial

Answer

Content filtering

Question 17

A financial services company uses Amazon SageMaker to train models with sensitive customer data. They must ensure that no data leaves a specific AWS Region due to data residency regulations. The training data is in S3. Which architecture meets this requirement while minimizing data transfer?

Accepted Answer

Use S3 VPC endpoints and place SageMaker training job in a private subnet with no internet access. Using a VPC with S3 VPC endpoints ensures data stays within the AWS network and does not traverse the internet. Data remains in the same Region because S3 endpoints are Regional.

Answer

Place SageMaker training job in a private subnet with a NAT gateway and route traffic through the internet

Answer

Configure S3 Transfer Acceleration and use a public SageMaker training job

Answer

Use AWS Glue to copy data to an EBS volume attached to the training instance

Question 18

A company wants to use a third-party foundation model from Amazon Bedrock but is concerned about data privacy because the model provider might store prompts and responses. How should they address this concern?

Accepted Answer

Review the third-party model provider's data handling policy and choose a model that does not retain data. Each third-party model provider in Bedrock has its own data handling policies. Customers should review those policies and can choose models that do not store data or use Bedrock features like Guardrails to redact sensitive data. However, the direct action is to review the provider's policy and select a model that meets privacy requirements.

Answer

Enable Amazon Bedrock model invocation logging to capture all interactions

Answer

Use Amazon Macie to scan prompts before they are sent

Answer

Use AWS KMS to encrypt the prompts and responses before sending to the model

Question 19

A machine learning team wants to detect bias in a deployed model's predictions on new data. They use Amazon SageMaker. Which service should they use to generate bias reports after deployment?

Accepted Answer

Amazon SageMaker Clarify. Amazon SageMaker Clarify provides bias detection and explainability for ML models, both during training and after deployment. SageMaker Model Monitor detects data drift but not bias. SageMaker Debugger is for training debugging. SageMaker Role Manager is for managing IAM roles.

Answer

Amazon SageMaker Debugger

Answer

Amazon SageMaker Model Monitor

Answer

Amazon SageMaker Role Manager

Question 20

Which AWS service is used as a vector store in Amazon Bedrock Knowledge Bases for storing and retrieving embeddings?

Accepted Answer

Amazon OpenSearch Serverless. Amazon Bedrock Knowledge Bases support several vector stores, including Amazon OpenSearch Serverless, Aurora pgvector, Pinecone, and MongoDB Atlas. OpenSearch Serverless is the AWS-native managed solution frequently used for this purpose.

Answer

Amazon DynamoDB

Answer

Amazon Redshift

Answer

Amazon S3