Question 1

A smart-home device manufacturer wants to use a Large Language Model (LLM) to identify usage trends from millions of daily device logs. Each log contains a specific device ID, the user's exact GPS coordinates, and a timestamped list of voice commands. Privacy regulations require that the manufacturer must not share granular location data or linkable user IDs with the analytics model, but the model still needs to understand regional usage patterns (e.g., "users in Northern Europe use heating more often").Which data pipeline architecture effectively prepares this data for the model while maintaining regulatory compliance?

Options :

A : Ingest the raw logs into an Amazon S3 bucket and use Amazon Athena to create a view that filters out columns containing Device IDs. Point the foundation model to the S3 bucket backing the Athena view and trust the model's system prompt to ignore any remaining location data.

B : Store the raw logs in Amazon S3 and enable S3 Object Lock in compliance mode to prevent modification. Use Amazon SageMaker Data Wrangler to feed the raw data directly into the model for fine-tuning, ensuring that the training job is run within a VPC without internet access.

C : Create an AWS Lambda function that hashes the Device ID and generalizes the GPS coordinates to a city or region level. Write these transformed logs to a separate "Analytics" S3 bucket and grant the foundation model access only to this secondary bucket for ingestion.

D : Implement a sidecar container in the ingestion pipeline that encrypts the GPS coordinates using asymmetric encryption. Pass the encrypted logs to the foundation model and provide the private key only within the RAG retrieval logic so the model can decrypt locations on-the-fly for regional analysis.

Answer: C

Question 2

A legal tech company uses Amazon Bedrock to extract specific liability clauses from uploaded contracts. Users have reported instances where the model "hallucinates" clauses that do not exist in the source text.Which mechanisms should the developer implement to verify accuracy and reduce these hallucinations?

Options :

A : Define a JSON Schema representing the expected liability data structure and include it in the model instructions to enforce constrained, deterministic output.

B : Implement a post-processing workflow that calculates the semantic similarity score between the generated response and the source text, rejecting responses that fall below a defined confidence threshold.

C : Configure Amazon Bedrock Guardrails with a "Sensitive Information" filter to redact names and addresses found in the contract clauses.

D : Increase the inference temperature parameter to 0.9 and enable Top-P sampling to broaden the diversity of the generated clauses.

E : Use Amazon CloudWatch to monitor the InvocationLatency and ModelTokenCount metrics to identify anomalies in the model's processing speed.

Answer: A,B

Question 3

A news aggregator uses a RAG architecture with Amazon OpenSearch Service (Provisioned) as the vector store. The system ingests breaking news in real-time, performing thousands of vector insertions per minute.Users are reporting that the "Related Articles" search is becoming increasingly slow (high latency) during these ingestion spikes. The OpenSearch cluster metrics show high CPU utilization and high JVM memory pressure.The index is configured with the standard settings for k-NN search.Which index configuration change should the developer apply to improve search performance under heavy ingestion load?

Options :

A : Change the vector data type from float32 to binary to reduce the storage footprint.

B : Switch the index engine from nmslib to faiss and enable the ivf (Inverted File) algorithm.

C : Enable the "Force Merge" operation to run automatically every minute to reduce the number of segments in the index.

D : Increase the refresh_interval setting of the OpenSearch index (e.g., from 1s to 30s or 60s).

Answer: D

Question 4

A media streaming company currently runs a self-managed vector database on Amazon EC2 instances for their content recommendation system. The database stores 150 million video embeddings and handles approximately 50,000 similarity searches per day during peak hours, with search activity dropping to near zero during off-peak nights.The infrastructure team spends significant time managing database scaling, performing backups, applying security patches, and monitoring cluster health. Monthly costs include $8,000 for always-on EC2 instances plus operational overhead. The CTO wants to reduce infrastructure management burden and optimize costs for the variable workload pattern.Which migration strategy addresses these requirements MOST cost-effectively?

Options :

A : Deploy Amazon OpenSearch Serverless with vector engine capabilities. Configure auto-scaling policies to scale compute capacity down to minimum during off-peak hours while maintaining search availability.

B : Containerize the existing vector database using Amazon ECS on AWS Fargate. Implement scheduled scaling policies to stop tasks during zero-activity periods and restart them before peak traffic begins.

C : Migrate the vector database to Amazon RDS for PostgreSQL with pgvector extension. Enable Aurora Serverless v2 auto-scaling to reduce costs during low-traffic periods while maintaining managed database benefits.

D : Migrate embeddings to Amazon S3 vector buckets with vector indexes enabled. Configure S3 Lifecycle policies for index management and leverage the pay-per-request pricing model that eliminates costs during zero-activity periods.

Answer: D

Question 5

A fintech company is launching a GenAI-powered "Spending Analyzer" feature. The application uses a Large Language Model (LLM) on Amazon Bedrock to explain transaction history to users. The project has two critical governance requirements:Compliance: The model must strictly refuse to answer any questions related to "Tax Advice" or "Legal Liability," as the company is not licensed to provide these services.Agility: The non-technical Product Team needs the ability to update the "Persona" and "Output Format" (e.g., switching from paragraphs to bullet points) of the analyzer weekly without requiring the engineering team to deploy new application code.Which combination of AWS GenAI features should the developer implement to meet these requirements? (Choose two)

Options :

A : Fine-tune the Foundation Model using Amazon SageMaker JumpStart on a dataset of "Refusal" examples. Train the model to recognize tax-related questions and output a standard disclaimer.

B : Implement Amazon Bedrock Prompt Management. Create a parameterized prompt template that defines the System Prompt (Persona) and output structure. Grant the Product Team permissions to iterate, test, and version the prompt within the Bedrock console.

C : Implement a post-processing AWS Lambda function. Use a keyword list (e.g., "tax", "audit", "lawyer") to scan the generated text. If a restricted keyword is found, overwrite the response with a hardcoded error message.

D : Use AWS Systems Manager Parameter Store to hold the System Prompt string. Update the backend Lambda function to fetch the latest prompt version on every invocation. Give the Product Team access to the Systems Manager console to edit the string.

E : Create an Amazon Bedrock Guardrail. Configure a "Denied Topics" policy that explicitly defines "Tax Advice" and "Legal Advice" with description examples. Attach this guardrail to the model invocation to intercept and block restricted queries.

Answer: B,E

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