AIF-C01 Practice Questions

Question 1

A financial services company is deploying a fraud detection model using Amazon SageMaker. They need to ensure that inference requests are encrypted in transit and that the model endpoint is only accessible from within a VPC. What is the MOST secure way to achieve this?

Accepted Answer

Deploy the model endpoint in a VPC with an interface VPC endpoint (AWS PrivateLink) and configure the model to require HTTPS. It combines two critical security controls: deploying the SageMaker endpoint within a VPC using an interface VPC endpoint (AWS PrivateLink) ensures that all traffic stays within the AWS network and never traverses the public internet, while configuring the model to require HTTPS enforces encryption in transit. This architecture eliminates exposure to the internet and meets the requirement for encrypted inference requests and VPC-only access.

Answer

Enable public endpoint with TLS and restrict access using a VPN

Answer

Use SageMaker Model Monitor to detect anomalies in request data

Answer

Create a SageMaker endpoint in a private subnet without an internet gateway, and use AWS PrivateLink

Question 2

A company uses Amazon Bedrock with a third-party foundation model. They are concerned about the third-party provider accessing their data. What should they review to understand data handling practices?

Accepted Answer

The third-party model provider's data privacy and handling documentation within AWS Bedrock's service description. AWS Bedrock's documentation includes data handling and privacy information for each third-party provider. The service agreement and data privacy page explain that AWS does not use customer data to improve models, and what third parties may do.

Answer

AWS Artifact reports for SOC and PCI compliance

Answer

AWS CloudTrail logs for model invocation

Answer

Amazon SageMaker Model Registry metadata

Question 3

A company uses Amazon Macie to discover sensitive data in an S3 bucket containing training datasets. The bucket policy currently prohibits access from external accounts. Which TWO steps are necessary to allow a cross-account SageMaker training job to access this bucket while maintaining security?

Accepted Answer

Add a bucket policy that grants the SageMaker execution role from the other account s3:GetObject and s3:ListBucket permissions. For cross-account access, the bucket policy must grant the required permissions to the SageMaker execution role (from the other account), and the SageMaker execution role must have an IAM policy that allows it to access the S3 bucket. Macie doesn't control access. VPC endpoints are optional.

Answer

Configure Macie to automatically grant access to the SageMaker execution role

Answer

Create a VPC endpoint for S3 and associate it with the SageMaker VPC

Answer

Remove the bucket policy that prohibits external access

Question 4

A hospital uses an AI system to prioritize patients for organ transplant based on predicted survival rates. The system was trained on historical data that includes socioeconomic factors. A review reveals that the system systematically assigns lower priority to patients from lower-income neighborhoods, even when medical urgency is similar. The hospital's ethics board demands an immediate remedy. The data science team is small and must act quickly. What should the hospital do to address this fairness issue most effectively?

Accepted Answer

Retrain the model with only medically relevant features, after removing socioeconomic factors and correlated proxies. The best course is to retrain the model using only medically relevant features, removing socioeconomic factors and correlated proxies. This directly addresses the source of bias. Adding a penalty for low-income patients is artificial and may not reflect medical reality. Relying solely on human review delays the issue and introduces potential inconsistency. Using a different model without data changes may not eliminate bias.

Answer

Discontinue the AI system and have all prioritization done by a human committee

Answer

Apply a re-weighting penalty to boost priority for low-income patients

Answer

Use a different model type, such as a random forest instead of gradient boosting, on the same data

Question 5

A company uses Amazon Bedrock Agents to process user requests that involve multiple steps, such as checking inventory and placing an order. The Agent sometimes fails to complete the workflow because it makes incorrect assumptions about the order of steps. What is the MOST effective way to guide the Agent's reasoning?

Accepted Answer

Include explicit step‑by‑step instructions in the Agent's prompt or instruction template. Amazon Bedrock Agents rely on the instructions provided in the agent's prompt or instruction template to orchestrate multi-step workflows. By explicitly including step-by-step instructions, you guide the agent's reasoning and reduce incorrect assumptions about the order of operations, directly addressing the failure to complete workflows.

Answer

Implement the entire workflow in a single Lambda function and bypass the Agent's reasoning

Answer

Switch to a larger foundation model in the Agent configuration

Answer

Add more action groups to cover every possible step

Question 6

A data science team is deploying a model using Amazon SageMaker. They need to monitor the model for bias after it is deployed. Which AWS service or feature should they use?

Accepted Answer

Amazon SageMaker Clarify. Amazon SageMaker Clarify provides bias detection and explainability for ML models, both during training (pre-deployment) and inference (post-deployment).

Answer

Amazon Bedrock Guardrails

Answer

Amazon SageMaker Model Monitor

Answer

AWS CloudTrail

Question 7

A company is building a chatbot using Amazon Bedrock. They want to provide up-to-date information from a continuously changing database. Which TWO services can be used as a data source for a Bedrock knowledge base? (Select TWO.)

Accepted Answer

Amazon Kendra. Amazon Bedrock knowledge bases can directly ingest data from Amazon S3, which is a supported data source for indexing documents. Amazon Kendra is also a supported data source, allowing Bedrock to leverage existing Kendra indexes for retrieval-augmented generation (RAG). Both services integrate natively with Bedrock knowledge bases to provide up-to-date information from continuously changing data.

Answer

Amazon RDS for MySQL

Answer

Amazon DynamoDB

Answer

AWS Glue

Question 8

A healthcare startup uses Amazon SageMaker to train a model predicting patient readmission. They need to ensure the model's predictions do not discriminate based on protected attributes like age or race. Which SageMaker feature allows them to monitor and mitigate bias during training?

Accepted Answer

SageMaker Clarify. SageMaker Clarify is the correct choice because it is specifically designed to detect and mitigate bias in machine learning models. It provides built-in capabilities to analyze training data and model predictions for bias against protected attributes such as age or race, and can generate bias reports and suggest mitigation strategies during training.

Answer

SageMaker Model Monitor

Answer

SageMaker Autopilot

Answer

SageMaker Debugger

Question 9

A company uses Amazon Bedrock with a custom model that was trained on data subject to GDPR. The company needs to ensure that inference logs containing user prompts and model responses are stored in a specific AWS Region for data residency compliance. How should they configure Bedrock model invocation logging?

Accepted Answer

Enable model invocation logging and select an S3 bucket in the required region. Bedrock model invocation logging allows you to specify the S3 bucket and CloudWatch Logs group where logs are stored. By choosing an S3 bucket in the desired region and enabling CloudWatch Logs in that region, you can meet data residency requirements.

Answer

Configure a Bedrock Guardrail to log all invocations to a CloudWatch Logs group in the required region

Answer

Use AWS CloudTrail to log all API calls and store them in the required region

Answer

Use AWS Global Accelerator to route traffic to the required region

Question 10

A data science team is using Amazon SageMaker to build a model. They want to ensure that only authorized users can deploy models to production. Which TWO methods can they use to enforce this?

Accepted Answer

Use SageMaker Model Registry to require approval before deployment.. SageMaker Model Registry allows you to set up an approval workflow for model versions. By requiring explicit approval before a model can be deployed to production, you enforce a governance gate that prevents unauthorized or unverified models from being used in production endpoints.

Answer

Enable multi-factor authentication (MFA) for all AWS accounts.

Answer

Use AWS CloudTrail to audit deployment actions.

Answer

Use Amazon GuardDuty to monitor for unauthorized deployment.

Question 11

A company uses Amazon Bedrock to generate product descriptions. They need to ensure outputs do not contain offensive language. Which service should they integrate to filter content?

Accepted Answer

Bedrock Guardrails. Amazon Bedrock Guardrails is the correct choice because it is specifically designed to enforce content policies for foundation model outputs, including filtering for offensive language, hate speech, and other harmful content. It integrates directly with Bedrock to apply customizable safety filters and deny topics without requiring additional services or custom code.

Answer

Amazon Comprehend

Answer

Amazon Rekognition

Answer

AWS WAF

Question 12

A company is using Amazon Bedrock to generate content for a marketing application. The company wants to ensure that the model does not generate content that violates the company's brand guidelines, which prohibit certain keywords and tones. Which TWO features should the company use to enforce these guidelines? (Choose two.)

Accepted Answer

Create a prompt template that instructs the model to adhere to brand guidelines and avoid prohibited keywords.. Prompt engineering allows the company to embed brand guidelines directly into the instruction given to the model, effectively steering the output away from prohibited keywords and tones. Option C is correct because Amazon Bedrock Guardrails provides a managed, policy-based mechanism to define custom deny topics and content filters that can block or mask unwanted content at inference time, enforcing brand guidelines without manual intervention.

Answer

Enable Amazon CloudWatch Logs to capture model output and manually review.

Answer

Use AWS IAM policies to restrict the model's output to only approved words.

Answer

Encrypt the model responses using AWS KMS to prevent unauthorized viewing.

Question 13

A healthcare company uses Amazon SageMaker to train a model on patient data. To meet HIPAA compliance, they must ensure training data is encrypted at rest and in transit. Additionally, the training job should not have internet access. Which combination of actions should the company take?

Accepted Answer

Specify a KMS key for SageMaker's VolumeKmsKeyId and configure the training job to run in a VPC without a public internet route. Using KMS encryption keys and configuring the training job to run in a VPC without a public internet route satisfies both encryption and network isolation requirements.

Answer

Use AWS Certificate Manager (ACM) to issue a certificate for the training job and enable network isolation

Answer

Enable SageMaker data capture and use a private VPC with an internet gateway

Answer

Store training data in an encrypted S3 bucket and use a SageMaker notebook instance inside a VPC

Question 14

A developer wants to create an agent using Amazon Bedrock Agents that can call an external API to check inventory levels. What must be defined in the agent configuration to enable this API call?

Accepted Answer

An action group. Action groups define the set of actions (API calls) that an agent can perform. Each action group specifies the API schema and optionally a Lambda function for execution.

Answer

A knowledge base

Answer

A guardrail

Answer

A prompt template

Question 15

A data scientist at a retail company is tasked with building a model to predict customer churn. The dataset contains 100,000 records with features such as age, purchase history, customer support interactions, and a binary label indicating whether the customer churned in the past. The team needs a model that can be deployed for real-time inference with low latency. They have limited time and want to use a built-in algorithm from Amazon SageMaker that is optimized for classification tasks. Which approach should they take?

Accepted Answer

Use Amazon SageMaker XGBoost algorithm. Amazon SageMaker's built-in XGBoost algorithm is optimized for classification tasks like binary churn prediction, supports real-time inference with low latency via SageMaker endpoints, and can handle the dataset size of 100,000 records efficiently. It is a supervised learning algorithm that directly uses the binary label for training, making it the correct choice for this scenario.

Answer

Use Amazon SageMaker PCA algorithm

Answer

Use Amazon SageMaker K-Means algorithm

Answer

Use Amazon SageMaker BlazingText algorithm

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