Rolling 7-day practice signal

Hardest AWS Generative AI Developer - Professional questions this week

These are the public diagnostic questions people are missing most often. Use the list as a weekly study loop: review the concept, then retake the diagnostic.

Weekly review loop

  1. Step 1Start with the most-missed topic and read the short answer explanation.
  2. Step 2Open the full breakdown for the concept before moving to the next topic.
  3. Step 3Retake the diagnostic and email the score report if you want the study plan.
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Live weekly data is still filling in, so this page is showing the current high-signal review set.

Weekly report

Send the current weak-area list to yourself, then use the links as a study queue before your next diagnostic attempt.

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#1 most missed

Bedrock & Knowledge Base

Foundation Model Integration, Data Management, and Compliance
Review
high-signal topic

A developer is building a customer support chatbot on Amazon Bedrock. It must handle multi-turn conversations, ground answers in product documentation stored in S3, never mention competitor products, minimize latency, and keep inference cost under $500/month for 10,000 conversations/day. Which architecture best satisfies all of these requirements?

Correct answer
A. Create a Bedrock Knowledge Base with an OpenSearch Serverless vector store over the S3 documentation using Claude 3 Haiku for generation, and configure Bedrock Guardrails with a denied topics policy to block competitor mentions

A Knowledge Base backed by OpenSearch Serverless performs semantic retrieval over the S3 docs, and Haiku gives the lowest latency and cost among Bedrock models, fitting the budget. Guardrails' denied topics policy is the managed way to reliably suppress competitor mentions. Fine-tuning with provisioned throughput is costly overkill for grounding in existing docs. Pasting all documentation into the prompt blows past context limits and inflates per-request cost. Relying only on a system-prompt instruction to suppress competitor mentions is unreliable compared to a dedicated guardrail, and a relational lookup skips semantic search entirely.

#2 most missed

Bedrock Agents & Action Groups

Implementation and Integration
Review
high-signal topic

A financial services company is building an AI assistant using Amazon Bedrock Agents that can check account balances, transfer funds between accounts, and generate monthly spending reports. The assistant must verify the user's identity before performing any financial transaction but should allow spending report generation without additional verification. The developer needs to implement this with the least operational overhead. Which architecture should the developer implement?

Correct answer
C. Create a single Bedrock Agent with three action groups. Configure the transfer and balance action groups' Lambda functions to call Amazon Cognito for identity verification before processing, while the report generation action group skips verification.

A single Bedrock Agent with separate action groups provides clean separation of concerns. Each action group has its own Lambda function and OpenAPI schema, allowing the developer to implement identity verification in the transaction-related Lambda functions while keeping the report generation Lambda simple. This is the least operational overhead approach that properly enforces security at the execution layer.

#3 most missed

Guardrails & Responsible AI

AI Safety, Security, and Governance
Review
high-signal topic

A healthcare company is deploying a patient-facing GenAI application using Amazon Bedrock. The application must: (1) never provide medical diagnoses, (2) redact any patient PII (Social Security numbers, medical record numbers) from both inputs and outputs, (3) block discussions about self-harm, and (4) log all guardrail interventions for compliance auditing. The developer has configured Bedrock Guardrails with content filters, denied topics, and sensitive information filters. Which additional configuration is required to meet ALL compliance requirements?

Correct answer
B. Enable CloudWatch logging on the guardrail and configure a sensitive information filter with regex patterns for medical record numbers, since SSN detection is built-in as a PII type

Bedrock Guardrails' sensitive information filters support built-in PII types (including SSN) and custom regex patterns for organization-specific identifiers like medical record numbers. The denied topics policy handles blocking medical diagnoses and self-harm discussions. Content filters handle harmful content categories. Enabling CloudWatch logging on the guardrail captures all intervention events for compliance auditing. This configuration addresses all four requirements natively.

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