Medium — AIP-C01 Practice Quiz

Question 1

A developer is building a Bedrock Agent that lets employees check the status of internal support tickets by asking natural-language questions. The developer defines an action group backed by a Lambda function and provides an OpenAPI schema describing a getTicketStatus operation with a required ticketId string parameter. During testing, the agent correctly identifies user intent but the Lambda receives an empty parameter list and cannot resolve the ticket. What is the MOST likely cause and correct fix?

Accepted Answer

The OpenAPI schema is missing accurate descriptions and the required marker for the ticketId parameter, so the agent cannot map user input to the parameter; add clear parameter descriptions and mark ticketId as required in the schema.

Answer

The Lambda function lacks a resource-based policy allowing bedrock.amazonaws.com to invoke it; add the lambda:InvokeFunction permission for the agent.

Answer

The agent must use Provisioned Throughput to pass parameters to action groups; purchase a provisioned model unit and re-associate the agent.

Answer

Action group parameters can only be passed when Return of Control is enabled; switch the action group to return control to the client application.

Question 2

A developer is building a Bedrock Agent for a travel booking company. The agent must be able to call three business functions: search flights, check baggage policy, and cancel a reservation. The developer has written a Lambda function that implements all three operations. During testing, the agent frequently invokes the wrong operation or passes incomplete parameters to the Lambda function. The action group is configured with an OpenAPI schema, but the operation descriptions are terse (e.g., 'searches flights') and parameters lack descriptions. What is the MOST effective change to improve the agent's tool selection and parameter extraction accuracy?

Accepted Answer

Enrich the OpenAPI schema with detailed operation summaries/descriptions and clear parameter descriptions, including required flags and expected formats.

Answer

Split the single Lambda function into three separate Lambda functions, one per operation, to reduce ambiguity.

Answer

Switch the agent to a larger foundation model with a bigger context window so it can reason over more tokens.

Answer

Enable return of control so the application, rather than the agent, decides which operation to call.

Question 3

A retail company is building a customer-service application that uses a foundation model through the Bedrock Converse API. Before each user message reaches the model, the team must normalize product SKUs (strip whitespace, uppercase them, and expand known abbreviations) and inject the current promotional catalog into the system prompt. The catalog changes several times per day and is fetched from a DynamoDB table. The team wants the cleanest, most maintainable architecture that keeps this logic out of the client and applies it consistently to every request. Which approach best meets these requirements?

Accepted Answer

Place an AWS Lambda function behind Amazon API Gateway that performs SKU normalization, reads the current catalog from DynamoDB, assembles the system prompt, and then calls the Bedrock Converse API before returning the model response to the client.

Answer

Embed the normalization rules and catalog directly in the client application and pass the fully assembled prompt to the Converse API, refreshing the catalog on the client every few minutes.

Answer

Use a static managed prompt version in Bedrock Prompt Management that contains the catalog, and have the client call the Converse API directly using that prompt version.

Answer

Configure a Bedrock Guardrail with a word policy to normalize the SKUs and store the catalog as a denied-topics list so it is injected automatically at inference time.

Question 4

A financial analytics team is building a Bedrock Agent that lets analysts ask natural-language questions about uploaded CSV files, such as 'calculate the 90-day moving average of these transactions and plot the trend.' The agent must dynamically write and execute Python to perform statistical computations and generate charts on the uploaded data, without the team pre-building an action group for every possible calculation. Which approach best satisfies this requirement?

Accepted Answer

Enable the code interpreter capability on the Bedrock Agent so it can generate and run Python code in a sandboxed environment against user-uploaded files.

Answer

Create an action group backed by a Lambda function that hard-codes moving-average and charting logic, and expand it with new functions as analysts request them.

Answer

Attach a Bedrock Knowledge Base containing the CSV files so the agent can retrieve and summarize numeric values through semantic search.

Answer

Use the Converse API with tool use to return the raw CSV rows to the model and let the foundation model perform the arithmetic directly in its response.

Question 5

A financial services company builds a customer-support chatbot on Amazon Bedrock Agents. They attach a Bedrock Guardrail that blocks requests for individualized investment advice. When a customer asks 'Which specific stocks should I buy right now?', the guardrail intervenes and blocks the input. The product team wants the chatbot to respond with a compliant, branded message that redirects the user to a licensed advisor, rather than returning the generic default block message. Which approach correctly customizes this behavior?

Accepted Answer

Configure the 'blockedInputMessaging' property on the guardrail so the intervention returns the custom compliant message directly to the user.

Answer

Modify the agent's orchestration prompt template to detect investment-advice questions and rewrite the response after the guardrail blocks the input.

Answer

Set a custom denied-topic policy in the Lambda action group so the function generates the branded redirect text before invocation.

Answer

Disable the guardrail and instead add a few-shot example in the system prompt instructing the model to refuse investment-advice questions politely.

Question 6

A retail company is building a customer-support application using Amazon Bedrock. Instead of a full agent, they want a synchronous request/response flow where an API Gateway REST endpoint invokes a Lambda function that calls Bedrock. The application prompt includes a large, static 4,000-token product catalog reference section that is identical across all customer requests, followed by a short dynamic user question. The team observes that per-request latency and input-token costs are unnecessarily high because the large static context is re-processed on every InvokeModel call. Which approach most directly reduces both cost and latency for the repeated static portion?

Accepted Answer

Enable prompt caching on the Converse/InvokeModel request by marking the static product catalog section with a cache checkpoint so it is reused across invocations.

Answer

Move the entire prompt construction into a Bedrock Agent action group so the orchestration layer deduplicates repeated context automatically.

Answer

Store the product catalog in a Bedrock Knowledge Base and retrieve it with RetrieveAndGenerate on every request to avoid sending the tokens.

Answer

Increase the Lambda memory allocation and enable Lambda SnapStart to reduce the time spent processing the large prompt.

Question 7

A financial advisory startup builds a customer support assistant using Amazon Bedrock Agents. Users interact across multiple sessions over several days, and the business requires the agent to remember key facts from earlier conversations (such as a customer's stated risk tolerance) so that follow-up sessions feel continuous. The team already uses session state for within-conversation context, but that context is lost once a session ends. Which approach most directly satisfies the requirement with the least custom development?

Accepted Answer

Enable Amazon Bedrock Agents memory retention, which summarizes and persists session information across multiple sessions for a configured retention period

Answer

Increase the foundation model's context window and pass the full transcript of every prior session in each new request

Answer

Store each session's raw messages in an S3 bucket and re-ingest them into the agent's knowledge base after every conversation

Answer

Rely on the agent's default session state, which automatically carries over across separate user sessions

Question 8

A financial services company is building a customer support assistant using Amazon Bedrock Agents. The workflow requires three specialized capabilities: a billing-inquiry agent that queries an internal API, a fraud-detection agent that runs risk scoring, and a general-FAQ agent backed by a knowledge base. A single request may need to route to one or coordinate across multiple of these specialists depending on the customer's intent. The team wants to minimize custom orchestration code in Lambda and let Bedrock handle intent routing and delegation between the specialized agents. Which approach best meets these requirements?

Accepted Answer

Configure a Bedrock Agents multi-agent collaboration setup with a supervisor agent that routes and delegates requests to the three collaborator agents.

Answer

Build a single Bedrock Agent with three action groups and write a Lambda orchestrator that inspects the model output and manually invokes the correct action group in sequence.

Answer

Deploy the three agents independently and use an AWS Step Functions Choice state to inspect keywords in the user prompt and invoke the matching agent.

Answer

Create one Bedrock Agent and encode all billing, fraud, and FAQ logic into a single large prompt template with few-shot examples for each intent.

Question 9

A developer is building a customer-support application that uses the Amazon Bedrock InvokeModel API directly (not Bedrock Agents). Support requests arrive in six languages and must be classified into one of four ticket categories. The team maintains a prompt template stored in Bedrock Prompt Management with a placeholder for the incoming message. Business analysts frequently add new labeled examples to improve classification accuracy, and they want these updates to take effect without requiring a code deployment. Which approach best satisfies this requirement while keeping the classification consistent?

Accepted Answer

Store the few-shot examples as variables within a versioned prompt in Bedrock Prompt Management, publish a new version when examples change, and reference the prompt by its version identifier at runtime.

Answer

Hardcode the few-shot examples in the application source and redeploy the Lambda function each time analysts submit new examples.

Answer

Fine-tune the foundation model on the labeled examples every time analysts add new ones so the examples are baked into the model weights.

Answer

Place the examples in the model's inferenceConfig stopSequences field so the model uses them during generation without code changes.

Question 10

A retail company is building a returns-processing assistant. The workflow requires: (1) validating an order number against a fulfillment API, (2) checking return eligibility based on a business-rules engine, and (3) generating a refund confirmation. The order in which these steps run can vary depending on customer input, and the LLM must decide which tool to invoke next based on intermediate results. The team wants to minimize the amount of custom orchestration code they maintain while still allowing the model to reason dynamically about which action to take. Which implementation approach best fits these requirements?

Accepted Answer

Build a Bedrock Agent with action groups mapped to Lambda functions for the fulfillment API and rules engine, and let the agent's orchestration handle tool selection dynamically.

Answer

Implement a Step Functions Standard workflow with explicit state transitions for each of the three steps and a Choice state to branch on results.

Answer

Use a single Lambda function that hardcodes the three API calls in sequence and passes intermediate results into successive InvokeModel calls.

Answer

Deploy an API Gateway endpoint that invokes InvokeModel once with a large system prompt describing all three steps and expects the model to call all APIs itself.

Question 11

A developer is building a customer support assistant that must call three internal REST APIs (order lookup, refund status, shipping tracker) based on user intent. The team wants full control over the orchestration loop in their own Lambda code: they will parse the model's tool requests, invoke the APIs themselves, and feed results back to the model in a loop they manage. They do not want AWS to manage the reasoning-and-invocation cycle or maintain agent configuration state. Which Bedrock integration approach best fits these requirements?

Accepted Answer

Use the Converse API with the toolConfig parameter, inspecting stopReason 'tool_use' and appending toolResult content blocks in an application-controlled loop

Answer

Create a Bedrock Agent with three action groups mapped to the REST APIs and let the agent runtime orchestrate tool selection

Answer

Use InvokeModel with a single prompt that concatenates all three API responses ahead of time so no tool calling is needed

Answer

Configure a Bedrock Knowledge Base over the API documentation and rely on retrieval to answer order and refund questions

Question 12

A developer is building a customer support automation feature. The workflow must: (1) look up an order status from an internal REST API, (2) if the order is delayed, check a shipping-carrier API for updated ETA, and (3) draft a personalized apology email. The exact sequence of API calls varies depending on the order state and the customer's question, which is free-form natural language. The team wants the foundation model itself to decide which tools to call and in what order, with minimal custom orchestration code. Which implementation approach best fits these requirements?

Accepted Answer

Use Amazon Bedrock Agents with two action groups (order lookup and carrier ETA) backed by Lambda functions, letting the agent's orchestration reason over the user request to invoke tools dynamically.

Answer

Build an AWS Step Functions state machine with explicit Choice states that hard-code the branching logic between the order API, carrier API, and the email-generation Bedrock call.

Answer

Use a single Bedrock Converse API call with the full order and carrier data pre-fetched and injected into the prompt, so the model generates everything in one turn.

Answer

Deploy a Lambda function that parses the customer message with regex to determine which API to call, then invokes Bedrock only for the final email text.

Question 13

A financial services company is building a loan pre-approval system. The workflow is fully deterministic: validate applicant data, call a credit-scoring microservice, run a fixed set of eligibility rules, then generate a personalized rejection or approval letter using a Bedrock foundation model. The sequence of steps never changes, and the compliance team requires that every step be auditable and reproducible with explicit branching logic. Which implementation approach best fits these requirements?

Accepted Answer

Use AWS Step Functions to orchestrate the fixed sequence, invoking Lambda functions for validation, credit scoring, and eligibility rules, and calling Bedrock only for the final letter-generation step.

Answer

Build a Bedrock Agent with action groups for each step and let the agent's LLM-based orchestration decide the order of validation, scoring, and rule evaluation at runtime.

Answer

Use a single large prompt that instructs the foundation model to perform validation, credit scoring, eligibility evaluation, and letter generation in one InvokeModel call.

Answer

Implement the entire workflow inside one Lambda function using sequential synchronous calls with no external orchestration service.

Question 14

A media analytics company needs to generate summaries for a backlog of 4 million archived news articles using Amazon Bedrock. There is no user-facing latency requirement — the job can run overnight or over several days. The team wants to minimize cost per token while processing this large, static dataset. Which approach best meets these requirements?

Accepted Answer

Use the Bedrock batch inference (CreateModelInvocationJob) feature, providing input records in Amazon S3 and retrieving results from S3 after the job completes.

Answer

Invoke the InvokeModel API in a parallelized loop with hundreds of concurrent threads to process articles as fast as possible.

Answer

Purchase Provisioned Throughput with a long-term commitment and stream each article through the model in real time.

Answer

Use the InvokeModelWithResponseStream API so results return incrementally, reducing the total tokens billed.

Question 15

A media analytics company runs a nightly job that summarizes approximately 500,000 news articles collected during the day. The results are consumed by an internal dashboard the next morning, so there is no real-time requirement — the only constraint is that all summaries must be ready before 7:00 AM. The team currently sends each article through on-demand InvokeModel calls in a tightly parallelized loop, but they are hitting frequent ThrottlingExceptions and their monthly Bedrock bill has become the largest line item in their AWS spend. Which approach best reduces cost while eliminating throttling for this workload?

Accepted Answer

Submit the 500,000 articles as a Bedrock batch inference job that writes outputs to Amazon S3, scheduled to complete overnight.

Answer

Purchase Provisioned Throughput with a 6-month commitment to guarantee dedicated capacity for the nightly run.

Answer

Keep on-demand InvokeModel but add exponential backoff with jitter and request a service quota increase for tokens per minute.

Answer

Switch to the Converse API with streaming responses to lower per-request latency during the parallel loop.

Question 16

A financial services company must maintain a complete, tamper-evident audit trail of every Amazon Bedrock model invocation for regulatory review. Compliance auditors need to know which IAM principal invoked which model, from which source IP, and at what time — for both console and API activity. The security team already has CloudTrail enabled capturing management events, but during a test they cannot find any records of InvokeModel calls made by the application. What is the correct action to ensure these invocations are auditable?

Accepted Answer

Enable CloudTrail data events for Amazon Bedrock so that InvokeModel and InvokeModelWithResponseStream API activity is logged with the calling principal, source IP, and timestamp.

Answer

Enable Bedrock model invocation logging to CloudWatch Logs and S3, which will automatically populate CloudTrail with the missing InvokeModel records.

Answer

Enable AWS Config with the Bedrock managed rules to record each model invocation as a configuration item for audit review.

Answer

Attach a resource-based policy to the foundation model that forces all InvokeModel calls to be recorded as CloudTrail management events.

Question 17

A financial services company runs a customer-facing chatbot on Amazon Bedrock protected by a Bedrock Guardrail. The security team wants to be automatically alerted whenever the guardrail blocks a high volume of user inputs within a short window, since a spike may indicate a coordinated jailbreak or prompt-injection attack. They need a near-real-time detection mechanism without building a custom log-parsing pipeline. Which approach best meets this requirement?

Accepted Answer

Enable Bedrock model invocation logging to CloudWatch Logs, create a metric filter that matches guardrail intervention events, and configure a CloudWatch alarm with an SNS notification when the metric crosses a threshold.

Answer

Enable CloudTrail data events for Bedrock and configure a CloudTrail Insights rule to notify the security team when API call patterns deviate from normal.

Answer

Increase the guardrail content filter strength to HIGH so that blocked requests automatically trigger an email to the security team.

Answer

Stream all Bedrock invocation logs to Amazon S3 and run a scheduled AWS Glue crawler each hour to detect elevated guardrail block counts.

Question 18

A developer is building an internal document-review assistant that lets employees upload a PDF contract and ask questions about its contents in a single request. The team wants to use the Amazon Bedrock Converse API with an Anthropic Claude model. They need the raw document bytes to be passed directly to the model in the conversation turn without first extracting or embedding the text themselves. Which approach correctly implements this using the Converse API?

Accepted Answer

Include a document block in the user message's content array with the file format and raw bytes as a source, so the Converse API sends the document natively to the model.

Answer

Base64-encode the PDF and place it inside a plain text content block, then instruct the model in the system prompt to decode and read it.

Answer

Upload the PDF to an S3 bucket and pass only the S3 URI string in a text content block, relying on the model to fetch the object at inference time.

Answer

Convert the PDF to an image content block per page and send them, since the Converse API only accepts image inputs, not documents.

Question 19

A developer is building a customer-support chatbot on a Node.js backend using the Amazon Bedrock Converse API with Anthropic Claude. Each browser session is independent, and the backend is a stateless Lambda function behind API Gateway. During testing, the model loses awareness of earlier turns in the same conversation—it cannot reference details the user mentioned two messages ago. The developer confirmed the model itself supports multi-turn context. What is the correct way to preserve conversational context across turns?

Accepted Answer

Persist the full ordered list of user and assistant turns externally (e.g., DynamoDB keyed by session ID) and pass the complete messages array on every Converse request.

Answer

Enable the Bedrock Agents memory feature on the Converse API call so the service automatically retains session history.

Answer

Increase the maxTokens inference parameter so the model has enough room to remember previous turns.

Answer

Set a sessionId field in the Converse API request so Bedrock correlates and stores the prior messages server-side.

Question 20

A developer is building a customer-support classification service using the Bedrock Converse API with Anthropic Claude. The service must classify incoming messages into one of five fixed categories and return only the category label. During testing, the model occasionally adds explanatory sentences, uses category names not in the approved list, or varies the label casing. The team wants the most reliable way to enforce consistent, constrained output while keeping the solution simple and portable across supported Converse-compatible models. What should the developer do?

Accepted Answer

Provide a system prompt that defines the exact allowed labels and output format, and include several few-shot input/output examples in the messages array demonstrating the required response.

Answer

Increase the temperature and top_p inference parameters so the model explores more completions and eventually settles on the correct label.

Answer

Switch from the Converse API to the legacy InvokeModel API because only it supports deterministic classification output.

Answer

Attach a Bedrock Guardrail with a denied-topics policy listing the five categories to force the model to respond with a valid label.

Question 21

A developer is building a customer-support assistant using Amazon Bedrock's Converse API with an Anthropic Claude model. The assistant must call an internal inventory microservice when a user asks about product availability. The developer defines a tool specification with an input schema and passes it in the toolConfig. During testing, the model returns a response, but the application must know when the model wants to invoke the inventory tool versus when it is producing a final answer. How should the application determine that the model is requesting a tool invocation?

Accepted Answer

Inspect the response's stopReason field for the value 'tool_use' and read the toolUse block (with toolUseId, name, and input) from the returned content.

Answer

Parse the assistant's text output for a JSON code block and manually extract the function name and arguments using a regular expression.

Answer

Enable Bedrock Guardrails on the request so that any tool call is automatically routed to the configured Lambda function before returning.

Answer

Configure a Bedrock Agent action group, since the Converse API cannot signal tool-use intent on its own.

Question 22

A GenAI team runs a customer-facing summarization service on Amazon Bedrock using Anthropic Claude on-demand throughput in us-east-1. During regional promotional events, request volume spikes 4x for a few hours, and the team begins receiving ThrottlingExceptions because they exceed the on-demand requests-per-minute quota in that single region. The workload has no data residency restrictions, and the team wants to absorb these bursts without committing to a fixed hourly cost or re-architecting their application code. What is the MOST cost-effective way to increase available throughput during these spikes?

Accepted Answer

Switch to a cross-region inference profile so Bedrock automatically distributes on-demand invocations across multiple regions, raising the effective request capacity without provisioned commitments.

Answer

Purchase Provisioned Throughput with a 1-month commitment sized to the 4x peak so capacity is always reserved for the events.

Answer

Move the entire workload to Bedrock batch inference to avoid on-demand quotas during the spikes.

Answer

Request a permanent Service Quotas increase to 4x the baseline requests-per-minute limit in us-east-1.

Question 23

A media company runs a document summarization service on Amazon Bedrock using Claude on-demand throughput in us-east-1. During peak evening hours, the service processes bursts of user requests and increasingly receives ThrottlingException errors, causing dropped summaries. The workload is latency-sensitive (users wait for results interactively) and cannot tolerate the multi-hour delay of asynchronous processing. Traffic is unpredictable and spiky, so committing to a fixed hourly rate is undesirable. The team wants to increase available throughput headroom with minimal architectural change. Which approach BEST addresses the throttling while fitting the workload's constraints?

Accepted Answer

Switch the invocations to use a Bedrock cross-region inference profile so requests are automatically distributed across multiple regions, increasing aggregate on-demand throughput capacity.

Answer

Migrate the workload to Bedrock batch inference to avoid on-demand throttling limits during peak hours.

Answer

Purchase Provisioned Throughput with a 1-month commitment sized to peak traffic to guarantee capacity.

Answer

Reduce the max_tokens parameter on each request to lower per-request token consumption and stay under the throttling limit.

Question 24

A retail company runs a customer-facing product recommendation feature on Amazon Bedrock using Anthropic Claude models via on-demand throughput. During a flash sale, traffic to the recommendation service spikes 5x for roughly 3 hours, and users begin receiving ThrottlingException errors even though the average daily volume is well within the account's default quotas. The team wants to absorb these short, unpredictable bursts without committing to a fixed hourly cost and with minimal code changes. Which approach best meets these requirements?

Accepted Answer

Configure a cross-region inference profile so requests are automatically distributed across multiple regions, aggregating on-demand quotas to absorb the burst.

Answer

Purchase Provisioned Throughput with a 1-month commitment sized for the peak burst so capacity is always reserved.

Answer

Switch the recommendation calls to the Bedrock batch inference API to smooth out the traffic spike over several hours.

Answer

Move the workload to a smaller distilled model so each request consumes fewer tokens during peak periods.

Question 25

A data engineering team is preparing a proprietary corpus of 2 million customer support transcripts to fine-tune a foundation model on Amazon Bedrock. During exploratory analysis they discover that roughly 30% of the transcripts are near-duplicates (repeated boilerplate greetings, canned responses, and copy-pasted templates), and a subset contains full credit card numbers embedded in free-text fields. The team wants the fine-tuned model to generalize well and wants to minimize compliance risk before the customization job runs. Which data preparation approach best addresses both concerns?

Accepted Answer

Deduplicate the transcripts before training and apply PII detection/redaction (e.g., Amazon Comprehend or Macie-driven workflows) to remove or mask credit card numbers, then use the cleaned corpus for the customization job.

Answer

Run the fine-tuning job on the raw dataset and rely on Bedrock Guardrails at inference time to block credit card numbers in outputs, since deduplication is handled automatically by the training service.

Answer

Keep all near-duplicates because higher record volume always improves fine-tuning quality, and encrypt the S3 bucket with SSE-KMS so the credit card numbers are protected at rest during training.

Answer

Split the dataset so credit card records are used only for validation rather than training, and leave the duplicates in the training split to preserve realistic frequency distributions.