AIP-C01 Practice Test 3 — 30 Questions

Question 1

An enterprise is planning to implement generative AI with strict compliance requirements. Which THREE practices should the developer include to ensure data privacy and regulatory compliance?

Accepted Answer

Enable Amazon Bedrock Guardrails to filter PII. Proper compliance involves encryption, access control (IAM), and the use of Guardrails for content moderation.

Answer

Disable all CloudWatch logging to prevent data leakage

Answer

Store all prompts in plain text in S3 for auditing

Question 2

A team is preparing data for fine-tuning a custom model on Amazon Bedrock. Which TWO of the following are best practices for high-quality training datasets?

Accepted Answer

Provide data in JSONL format with clear prompt-response pairs. Dataset preparation requires high-quality, relevant data and appropriate formatting (JSONL) for successful fine-tuning.

Answer

Include as much raw data as possible, even if noisy

Answer

Use automated AI tools to generate the training data from scratch

Answer

Convert all data into a CSV format

Question 3

You need to ensure that your SageMaker inference endpoint does not exceed a specific monthly budget. Which AWS feature allows you to set up notifications when spending thresholds are crossed?

Accepted Answer

AWS Budgets. AWS Budgets allows you to set custom cost and usage budgets and receive email or SNS alerts when thresholds are approached or exceeded.

Answer

SageMaker Cost Explorer

Answer

CloudWatch Alarms on billing metrics

Answer

AWS Cost and Usage Reports

Question 4

You notice that your SageMaker model endpoint experiences cold starts. Which feature can mitigate this?

Accepted Answer

Enabling Provisioned Concurrency. Provisioned Concurrency maintains a set number of initialized instances, eliminating cold starts for serverless inference.

Answer

Increasing the instance count

Answer

Changing the container image

Answer

Using a smaller model

Question 5

Which THREE factors influence the choice of embedding model for a RAG solution?

Accepted Answer

Supported languages. Language support, embedding dimensions, and maximum input token length are key factors.

Answer

Model's popularity

Answer

Model training cost

Question 6

Which parameter in Bedrock inference controls the diversity of the generated tokens?

Accepted Answer

Top_p. Top_p (also called nucleus sampling) limits the token choices to the top percentage of probability mass, affecting diversity.

Answer

Max_tokens

Answer

Temperature

Answer

Top_k

Question 7

A developer is testing an application and finds that the model output is too verbose. Which TWO of the following parameters or techniques can help control the response length?

Accepted Answer

Reduce the 'max_tokens' parameter.. Max tokens and system instructions are the primary ways to limit verbosity.

Answer

Increase the temperature.

Answer

Enable Guardrails to truncate output.

Answer

Use a different model family.

Question 8

You are deploying a model using SageMaker Multi-Model Endpoints (MME). What is the primary benefit of this strategy for operational efficiency?

Accepted Answer

Reducing the number of instances required. MME allows you to host multiple models on a single set of instances, reducing the total cost by sharing resources.

Answer

Increasing model accuracy

Answer

Lowering training time

Answer

Enabling faster model deployment

Question 9

Which TWO actions should you perform to identify cost savings in an existing SageMaker environment?

Accepted Answer

Review AWS Trusted Advisor recommendations. Analyzing Cost Explorer and using Trusted Advisor are the correct ways to find optimization opportunities.

Answer

Delete all data in S3

Answer

Increase the number of IAM roles

Answer

Deploy models to every region

Question 10

You are using Amazon Bedrock and need to ensure your application respects regional service quotas. Which service allows you to request a quota increase?

Accepted Answer

AWS Service Quotas. AWS Service Quotas is the centralized service for managing and requesting increases for AWS service limits.

Answer

AWS IAM

Answer

Amazon CloudWatch

Answer

AWS Support Center

Question 11

You are building a RAG application. Which metadata field is most useful for filtering results during retrieval?

Accepted Answer

Creation date. Metadata filtering allows you to restrict retrieval to specific document types or categories.

Answer

Vector distance

Answer

Token count

Answer

Model ID

Question 12

Which SageMaker feature allows you to observe model performance drifts over time?

Accepted Answer

SageMaker Model Monitor. SageMaker Model Monitor automatically detects data drift, concept drift, and model quality issues.

Answer

SageMaker Pipelines

Answer

SageMaker Data Wrangler

Answer

SageMaker Clarify

Question 13

Which TWO of the following strategies are best for reducing the cost of SageMaker training jobs?

Accepted Answer

Using Managed Spot Training. Using Managed Spot Training and instance-appropriate types are standard methods for reducing training costs.

Answer

Keeping training clusters running

Answer

Increasing the number of instances

Answer

Enabling detailed logging for every epoch

Question 14

Your team uses SageMaker Pipelines for training. How can you optimize the storage costs of the artifacts generated by these runs?

Accepted Answer

Configuring S3 Lifecycle policies. Implementing an S3 Lifecycle policy automatically moves or deletes artifacts after a certain period, keeping storage costs controlled.

Answer

Disabling versioning on the S3 bucket

Answer

Using smaller instance types

Answer

Deleting logs every day

Question 15

Which AWS service is used to centralize and visualize costs for your various SageMaker projects?

Accepted Answer

AWS Cost Explorer. AWS Cost Explorer allows you to view, analyze, and visualize your AWS spending, including SageMaker usage.

Answer

CloudWatch Dashboards

Answer

SageMaker Studio Dashboard

Answer

AWS Trusted Advisor

Question 16

Which THREE metrics are best used for setting up auto-scaling policies on a SageMaker endpoint?

Accepted Answer

InvocationsPerInstance. InvocationsPerInstance, CPUUtilization, and GPUUtilization are the standard metrics to trigger scaling actions.

Answer

IAMUserLoginCount

Answer

S3RequestLatency

Question 17

Which THREE of the following are valid ways to monitor Amazon Bedrock model usage?

Accepted Answer

AWS CloudTrail logs.. CloudWatch metrics, AWS CloudTrail logs, and Usage billing reports are the primary ways to monitor usage.

Answer

AWS X-Ray service map.

Answer

Amazon S3 bucket logging.

Question 18

Which THREE factors should you consider when tuning your inference endpoint for cost-efficiency?

Accepted Answer

Auto Scaling policies based on custom metrics. Instance type, auto-scaling configuration, and model optimization (like quantization) are critical levers for cost efficiency.

Answer

CloudTrail log retention period

Answer

Number of S3 buckets used

Question 19

A developer is creating a test suite for an LLM application. Which THREE of the following metrics are commonly used to evaluate RAG system performance?

Accepted Answer

Answer Relevance. Faithfulness, answer relevance, and context precision are the key components of RAGAS evaluation.

Answer

Model Training Loss

Answer

Inference Latency

Question 20

Which THREE of the following are valid components of an Amazon Bedrock Agent test plan?

Accepted Answer

Validating the reasoning path of the agent.. Testing reasoning, tool invocation accuracy, and safety constraints are critical components of an agent test plan.

Answer

Verifying the S3 bucket encryption.

Answer

Measuring model training time.