AIF-C01 Practice Test 11 — 15 Questions

Question 1

A company wants to build a chatbot that responds to customer queries using a foundation model. They need low latency and want to avoid managing infrastructure. Which AWS service should they use?

Accepted Answer

Amazon Bedrock. Amazon Bedrock is a fully managed service that provides access to foundation models (FMs) from leading AI providers via a simple API, eliminating the need to manage underlying infrastructure. It is designed for building generative AI applications like chatbots with low latency, as it handles model hosting, scaling, and inference optimization automatically. This makes it the ideal choice for the company's requirement of low-latency responses without infrastructure management.

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Amazon EC2

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AWS Lambda

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Amazon SageMaker

Question 2

A data science team is developing a credit scoring model and wants to ensure it meets fairness requirements. They measure the model's disparate impact and find it exceeds the 80% rule (adverse impact ratio >0.8). Which THREE actions should they consider to mitigate this? (Choose three.)

Accepted Answer

Apply a reweighing technique to assign higher weights to underrepresented groups in training. Reweighing, bias mitigation algorithms, and collecting more diverse data are standard techniques to address disparate impact. Removing protected attributes may not eliminate proxy variables, and only adjusting thresholds does not address root causes.

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Remove protected attributes (e.g., gender, race) from the dataset

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Adjust the decision threshold to increase approval rates for the disadvantaged group

Question 3

A company is deploying a large language model (LLM) for customer support. They want to reduce the risk of hallucinations. Which TWO approaches should they implement? (Choose two.)

Accepted Answer

Implement Amazon Bedrock Guardrails to define topics the model should avoid. RAG grounds the model in retrieved documents, reducing hallucinations. Bedrock Guardrails provide content filtering and topic restrictions. Fine-tuning on hallucinated data is counterproductive, and longer prompts can increase hallucinations.

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Use a larger model without any retrieval mechanism

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Fine-tune the model on a dataset of hallucinated examples

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Increase the maximum token length to give the model more room to elaborate

Question 4

A model trained to predict credit risk shows that applicants from a certain zip code are disproportionately rejected, even though income and credit history are comparable. Which type of bias is MOST likely present?

Accepted Answer

Representation bias. Representation bias occurs when the training data over- or under-represents certain groups, leading the model to learn spurious correlations like zip code with creditworthiness.

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Measurement bias

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Historical bias

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Aggregation bias

Question 5

A company is developing a generative AI application for content creation. They want to ensure transparency as per responsible AI guidelines. Which THREE practices should they implement? (Choose three.)

Accepted Answer

Label AI-generated content with a clear disclosure. Transparency involves disclosing AI-generated content, providing disclaimers about capabilities, and documenting model limitations. Monitoring bias is important but separate from transparency; encouraging user trust without disclosure is misleading.

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Encourage users to trust the AI outputs without question

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Monitor the model for bias in production

Question 6

A financial services company uses a foundation model for document analysis. They need to ensure the model does not output sensitive customer information from its training data. What is the most effective mitigation?

Accepted Answer

Implement output filtering using an external service. Output filtering using an external service is the most effective mitigation because it acts as a post-processing layer that can detect and redact sensitive customer information (e.g., PII, account numbers) before the model's response is returned to the user. This approach does not rely on the model's internal training or input modifications, which can be bypassed or incomplete. It provides a robust, policy-driven control that can be updated independently of the model.

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Choose a model that has been fine-tuned on financial data

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Apply data masking before sending input

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Use a private endpoint

Question 7

A company is deploying an LLM for generating marketing copy. They want to reduce the risk of hallucinations and ensure the content is factually accurate. Which TWO approaches should they implement?

Accepted Answer

Use Retrieval-Augmented Generation (RAG) with a verified knowledge base. RAG grounds the model in retrieved facts, and human review provides oversight to catch inaccuracies.

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Use a smaller LLM to reduce the chance of hallucinations

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Disable all guardrails to avoid interference

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Increase the temperature parameter to generate more diverse outputs

Question 8

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.

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

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

Question 9

An AI system is used to screen job applications. The team finds that the model has a higher false positive rate for male applicants than female applicants. Which fairness metrics should they compute to quantify this disparity? (Choose two.)

Accepted Answer

Equalized odds. Equalized odds requires equal true positive and false positive rates across groups. Disparate impact compares selection rates. Demographic parity does not consider error rates; predictive parity compares positive predictive values.

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Individual fairness

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Predictive parity

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Demographic parity

Question 10

An administrator reviews a CloudTrail log entry for a CreateModel API call. Which security concern should they investigate?

Accepted Answer

The model data URL uses HTTP instead of HTTPS. Using HTTP instead of HTTPS for the model data URL exposes the data in transit to potential interception or tampering. CloudTrail logs record the URL as provided, and if it uses HTTP, the data transferred from that URL to SageMaker is unencrypted, violating security best practices for data in transit. This is a direct security concern that should be investigated and remediated by using HTTPS.

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The model name is not encrypted in the log

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The source IP address is an external IP

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The execution role ARN is visible in the log

Question 11

A financial institution uses a machine learning model to approve loan applications. To comply with regulatory requirements, they need to explain individual predictions. Which AWS service and feature combination should they use?

Accepted Answer

Amazon SageMaker Clarify with SHAP values. Amazon SageMaker Clarify provides SHAP-based feature importance for individual predictions, meeting the explainability requirement. SageMaker Model Monitor is for monitoring drift, not explainability.

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AWS Glue DataBrew with correlation analysis

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Amazon QuickSight with ML Insights

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Amazon SageMaker Model Monitor with data quality checks

Question 12

A company uses a text generation model to produce legal documents. They want to minimize the environmental impact of training and inference. Which THREE approaches should they consider?

Accepted Answer

Train the model on a smaller but representative dataset. Environmental impact can be reduced by using efficient hardware, smaller models, and managed services that optimize resource usage. These three directly address compute efficiency.

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Store all training data in multiple AWS Regions for redundancy

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Perform all training on on-demand instances to ensure consistent performance

Question 13

A company builds an AI system that generates medical diagnoses. To ensure patient safety and allow oversight, the company wants a human to review all high-risk predictions before they are acted upon. Which AWS service should they use?

Accepted Answer

Amazon Augmented AI (A2I). Amazon Augmented AI (A2I) enables human review workflows for ML predictions, allowing you to define conditions that trigger human review. SageMaker Ground Truth is for data labeling, not review workflows.

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AWS Lambda

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Amazon SageMaker Ground Truth

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AWS Step Functions

Question 14

A healthcare startup is deploying an AI model to assist with diagnosis. They want to comply with the EU AI Act, which classifies medical AI as high-risk. Which THREE requirements must they fulfill? (Choose three.)

Accepted Answer

Provide technical documentation and logs for traceability. The EU AI Act for high-risk systems requires risk management, human oversight, and transparency including documentation. Data minimization is a GDPR principle, not specific to the AI Act; automated decisions without human review are not allowed for high-risk.

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Minimize the amount of training data to only necessary data

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Allow the model to make final decisions without human review

Question 15

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.

Answer

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