AIF-C01 Practice Test 29 — 15 Questions

Question 1

A machine learning engineer notices that the training loss decreases steadily, but the validation loss starts increasing after a few epochs. Which of the following is the MOST likely cause?

Accepted Answer

Overfitting. The scenario describes training loss decreasing while validation loss increases after a few epochs, which is the classic signature of overfitting. The model is memorizing the training data (including noise) rather than learning generalizable patterns, causing it to perform poorly on unseen validation data.

Answer

Learning rate too low

Answer

Underfitting

Answer

Data leakage from validation set into training set

Question 2

A developer is evaluating generative AI models for a code generation task. Which THREE metrics are commonly used for automated evaluation of generated code? (Choose THREE.)

Accepted Answer

BERTScore. Common automated metrics for code generation include BLEU (n-gram overlap), ROUGE (recall-oriented), and BERTScore (contextual similarity). Perplexity is for language modeling, and human evaluation is not automated. Note: While ROUGE and BLEU originate from NLP, they are also used for code generation evaluation.

Answer

Human evaluation score

Answer

Perplexity

Question 3

Which AWS services can be used to build, train, and deploy custom machine learning models? (Choose two.)

Accepted Answer

AWS Deep Learning AMIs. AWS Deep Learning AMIs (C) are pre-configured Amazon Machine Images that include popular deep learning frameworks (TensorFlow, PyTorch, MXNet) and GPU drivers, allowing you to build, train, and deploy custom ML models on EC2 instances. Amazon SageMaker (E) is a fully managed service that provides end-to-end capabilities for building, training, and deploying custom ML models at scale, with built-in algorithms, automatic model tuning, and one-click deployment.

Answer

Amazon Polly

Answer

Amazon Lex

Answer

Amazon Rekognition

Question 4

A developer is using Amazon Bedrock to generate responses from a foundation model and wants to receive the output as a stream to improve user experience. Which TWO statements about streaming responses are correct? (Select TWO.)

Accepted Answer

Streaming reduces perceived latency by displaying partial results as they are generated. Streaming in Amazon Bedrock allows the model to send partial response chunks as they are generated, which reduces the time until the first token is displayed to the user. This improves perceived latency and creates a more responsive user experience, especially for long-form text generation.

Answer

Streaming returns the entire response at once

Answer

Streaming is supported by the InvokeModel API

Answer

Streaming cannot be used with temperature or max_tokens parameters

Question 5

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 6

A company uses Amazon Bedrock Agents to automate order processing. The agent needs to call an internal database to check inventory. Which component should be used to integrate the database query?

Accepted Answer

Action group. Bedrock Agents use action groups to define external tools or APIs. Each action group has an associated AWS Lambda function that performs the actual work (e.g., querying a database). The agent can call the action group via natural language instructions.

Answer

Knowledge base

Answer

Guardrail

Answer

Prompt template

Question 7

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 8

Which TWO of the following are key components of a responsible AI governance framework?

Accepted Answer

Develop and enforce AI ethics policies and standards. A responsible AI governance framework must include the development and enforcement of AI ethics policies and standards to ensure alignment with societal values, fairness, and accountability. These policies guide the design, deployment, and monitoring of AI systems, embedding ethical principles such as transparency, privacy, and non-discrimination into the AI lifecycle. Without such policies, organizations risk deploying AI that violates ethical norms or regulatory expectations.

Answer

Focus solely on compliance with legal regulations

Answer

Minimize human involvement in AI lifecycle decisions

Answer

Deploy AI models as black boxes to avoid scrutiny

Question 9

A financial services company deploys a generative AI chatbot for customer support. They want to prevent the chatbot from generating harmful or misleading information. Which AWS service can help monitor and filter responses?

Accepted Answer

Amazon Augmented AI (A2I) with human review. Amazon Augmented AI (A2I) enables human review of machine learning predictions, making it suitable for monitoring and filtering chatbot responses for harmful or misleading content. Other options: Amazon GuardDuty is a threat detection service, not for content filtering. AWS WAF protects web applications from common web exploits. Amazon Comprehend is a natural language processing service for insights, but not designed for real-time filtering with human review.

Answer

Amazon GuardDuty

Answer

AWS WAF

Answer

Amazon Comprehend

Question 10

A company uses Amazon Bedrock to generate code. They want to ensure the code follows security best practices and does not contain vulnerabilities. Which approach is most effective?

Accepted Answer

Use Amazon CodeGuru Security to review generated code.. Amazon CodeGuru Security reviews code for security vulnerabilities and provides recommendations. Using a model trained on secure code may not be sufficient; WAF is for web traffic; training a custom model requires significant effort and may not catch all issues.

Answer

Implement a post-processing step using AWS WAF.

Answer

Train a custom model on the company’s secure code.

Answer

Use a foundation model trained only on secure code.

Question 11

Which AWS service can be used to create human review workflows for high-risk AI predictions, ensuring a human-in-the-loop?

Accepted Answer

Amazon Augmented AI (A2I). Amazon Augmented AI (A2I) is the service designed to set up human review workflows for AI predictions, enabling human oversight when needed.

Answer

Amazon SageMaker Clarify

Answer

AWS Glue

Answer

AWS Step Functions

Question 12

Which TWO AWS services can be used together to build a chatbot that leverages a foundation model for natural language understanding?

Accepted Answer

Amazon Lex. Amazon Lex provides the conversational interface and natural language understanding (NLU) to interpret user intents and manage dialog, while Amazon Bedrock gives access to foundation models (FMs) for advanced natural language generation and understanding. Together, Lex can route utterances to a Bedrock FM via a Lambda function or direct integration, enabling a chatbot that leverages a pre-trained FM for richer responses.

Answer

Amazon Rekognition

Answer

Amazon Polly

Answer

AWS Glue

Question 13

A developer is using Amazon Bedrock's Claude model to summarize long documents. The developer notices that the summaries sometimes miss key points. Which parameter adjustment is most likely to improve summary completeness?

Accepted Answer

Increase the max_tokens parameter.. Increasing max_tokens allows the model to generate longer outputs, which is essential when summarizing long documents because the summary may need more tokens to capture all key points. If max_tokens is too low, the model truncates the response, potentially omitting important details. This directly addresses the issue of missing key points by providing sufficient output length for a complete summary.

Answer

Increase the top_k parameter.

Answer

Increase the temperature parameter.

Answer

Increase the top_p parameter.

Question 14

A company is implementing an AI governance framework for their machine learning models deployed on Amazon SageMaker. Which THREE actions should they include to manage the model lifecycle effectively? (Select THREE.)

Accepted Answer

Enable Amazon CloudWatch alarms for model accuracy metrics. Model versioning tracks changes, monitoring for drift ensures performance, and a decommissioning process retires outdated models. These are key lifecycle management practices.

Answer

Automatically delete all previous model versions after deployment

Answer

Use AWS CloudTrail to track model inference requests

Question 15

A data scientist observes that a gradient boosting model's performance on the validation set is significantly worse than on the training set. Which adjustment is MOST likely to reduce this gap?

Accepted Answer

Reduce the learning rate and increase the number of estimators. The gap between training and validation performance indicates overfitting. Reducing the learning rate and increasing the number of estimators is a standard regularization technique in gradient boosting: a lower learning rate shrinks the contribution of each tree, forcing the model to learn more slowly and generalize better, while more trees allow the model to still capture complex patterns without overfitting to noise.

Answer

Increase the maximum depth of trees

Answer

Increase the number of features

Answer

Increase the subsample ratio to 1.0