AIP-C01 Implementation And Integration Practice Test 3 — 15 Questions

Question 1

When invoking a Bedrock model via the API, which HTTP status code indicates that the request was processed, but the model has reached its rate limit?

Accepted Answer

429 Too Many Requests. The 429 Too Many Requests status code is the standard HTTP response for rate limiting, indicating the request was valid but the model's throughput quota has been exceeded. In Amazon Bedrock, this occurs when you surpass the provisioned throughput or the default quota for a model, and the service throttles the request. The API returns 429 to signal that the client should back off and retry later, often with a Retry-After header. This is distinct from client errors (4xx) or server errors (5xx) because the request itself is well-formed and could succeed if not for the rate limit.

Answer

500 Internal Server Error

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400 Bad Request

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200 OK

Question 2

Which THREE configurations must be set correctly within a Knowledge Base 'Data Source' for Bedrock to ingest documents from Amazon S3?

Accepted Answer

IAM Role with S3 read permissions. Option A is correct because the Knowledge Base data source requires an IAM service role that grants Amazon Bedrock permission to read the source objects from Amazon S3 (e.g., s3:GetObject and s3:ListBucket), without which ingestion fails. Option D is correct because the chunking strategy (default, fixed-size, hierarchical, semantic, or no chunking) is a required data source configuration that determines how documents are split before embedding. Option E is correct because the S3 bucket path (the s3:// URI prefix) tells the data source exactly which bucket and prefix to crawl for documents. Option B is not part of the data source configuration—the model ID is selected when creating or querying the Knowledge Base, not when defining the S3 data source. Option C is irrelevant; there is no 'database write timeout' setting in a Bedrock S3 data source configuration.

Answer

Model ID of the generative model

Answer

Database write timeout

Question 3

You are implementing a multi-agent system. Which method is most effective for ensuring the agents do not loop infinitely when coordinating tasks?

Accepted Answer

Implementing a maximum iteration count. A maximum iteration count (or recursion/step limit) is the standard guardrail in agent orchestration frameworks like LangChain, LangGraph, and AWS Bedrock Agents to bound execution. When agents can call each other or themselves, a hard cap on turns prevents runaway loops that burn tokens and never terminate. Once the cap is hit, the orchestrator returns the best partial result or raises a controlled error.

Answer

Caching agent responses

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Increasing the model temperature

Answer

Using a smaller model

Question 4

In SageMaker JumpStart, what is the primary advantage of selecting a 'Provisioned Throughput' option for a deployed model?

Accepted Answer

It ensures dedicated capacity for the endpoint. Provisioned Throughput on a SageMaker JumpStart endpoint reserves dedicated inference capacity measured in Model Units, guaranteeing consistent latency and throughput regardless of other customers' traffic. It decouples your workload from shared on-demand capacity, which is critical for production workloads with steady or predictable traffic. This is the primary advantage over the default on-demand endpoint mode.

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It automates the model fine-tuning process

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It reduces the size of the model weights

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It allows the model to access the internet

Question 5

Which parameter in Bedrock inference controls the deterministic nature of the output?

Accepted Answer

Temperature. Temperature is the Bedrock inference parameter that directly controls the randomness or determinism of the model's output. Setting temperature to 0 makes the model select the highest-probability token at each step, producing near-deterministic and repeatable responses. Higher values (up to 1) flatten the probability distribution, increasing creativity and variability.

Answer

Top_p

Answer

Max_tokens

Answer

Stop_sequences

Question 6

Which THREE features are provided by Amazon Bedrock Guardrails?

Accepted Answer

Content Filtering. Amazon Bedrock Guardrails provides Content Filtering (C), which lets you configure thresholds to block harmful categories such as hate, violence, sexual, insults, and misconduct in both prompts and responses. It also supports Denied Topic Management (D), allowing you to define custom topics with natural-language descriptions that the guardrail blocks when detected in inputs or outputs. Additionally, it offers Sensitive Information Filtering (E), which detects and masks or blocks PII and custom regex-defined sensitive data using the sensitive information policy. Automatic Model Fine-tuning (A) is not a Guardrails feature; fine-tuning in Bedrock is a separate model customization capability. Real-time cost optimization (B) is also not provided by Guardrails, as Guardrails focuses on safety and content controls rather than cost management.

Answer

Automatic Model Fine-tuning

Answer

Real-time cost optimization

Question 7

You are using SageMaker JumpStart and need to enable 'Data Capture' for monitoring model inputs and outputs. Where is the captured data stored?

Accepted Answer

Amazon S3. When you enable Data Capture for a SageMaker endpoint (including JumpStart models), the captured data (inputs and outputs) is stored in an Amazon S3 bucket that you specify. This allows you to later analyze the data for monitoring, debugging, or retraining. CloudWatch Logs is used for logging but not for storing the captured data itself; the data capture feature writes to S3.

Answer

Amazon RDS

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Amazon CloudWatch Logs

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Amazon ElastiCache

Question 8

What is the recommended approach to handle PII in your RAG Knowledge Base to comply with privacy regulations?

Accepted Answer

Redact PII before ingesting documents into the Knowledge Base. You should redact or mask PII at the source before ingestion into the Knowledge Base.

Answer

Filter it during query time only

Answer

Encrypt the Knowledge Base with a master key

Answer

Use a specialized model that doesn't 'read' PII

Question 9

You notice your SageMaker endpoint is underperforming. You identify that the model is CPU-bound during inference. Which action should you take?

Accepted Answer

Change to an instance type optimized for compute. If the model is CPU-bound during inference, the bottleneck is compute, not memory or instance count. Moving to a compute-optimized instance family (e.g., C-series) provides more vCPU per dollar and higher clock speeds, directly relieving the CPU saturation. This is the targeted fix for a CPU-bound workload.

Answer

Reduce the batch size

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Increase memory limit in the container

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Increase the number of instances (horizontal scaling)

Question 10

Which Bedrock API parameter allows you to define a system-level role or persona for the model?

Accepted Answer

system. The 'system' parameter in the Bedrock Converse API (and related APIs) allows you to define a system-level instruction or persona for the model, such as 'You are a helpful customer service agent.' This system prompt is processed separately from user messages and sets the model's behavior, tone, and constraints for the entire conversation. It is the correct parameter for defining a system-level role or persona.

Answer

messages

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top_p

Answer

temperature

Question 11

Which AWS tool enables you to test different models side-by-side using the same prompt?

Accepted Answer

Bedrock Playground. Amazon Bedrock Playground provides a dedicated interface where you can select multiple foundation models (e.g., Anthropic Claude, AI21 Labs Jurassic, Amazon Titan) and run the same prompt against them simultaneously, comparing outputs side-by-side. This is a built-in feature of the Bedrock console designed specifically for model evaluation and prompt experimentation. It eliminates the need to write code or manage infrastructure, allowing quick assessment of model performance, latency, and response quality.

Answer

Amazon SageMaker Studio

Answer

AWS Cloud9

Answer

AWS Lambda Console

Question 12

When designing a prompt for a coding assistant, which THREE pieces of information should be included to get the best results?

Accepted Answer

Frameworks or libraries to be used. Option A (frameworks or libraries to be used) is correct because specifying the exact frameworks or libraries (e.g., React, Django, Spring Boot) constrains the assistant to generate idiomatic code and APIs that match the intended stack, avoiding incompatible or generic solutions. Option B (the programming language) is correct because the language (e.g., Python, TypeScript, Go) determines syntax, standard library usage, and runtime behavior, so naming it prevents the assistant from guessing and producing code in the wrong language. Option D (specific formatting requirements) is correct because stating formatting constraints such as PEP 8, ESLint rules, indentation style, or output structure ensures the generated code conforms to project standards and is directly usable. Option C (the AWS account ID) is not relevant to prompt design for a coding assistant, since it is an environment-specific identifier that does not influence code generation. Option E (the model's internal parameter count) is not something a prompt author controls or needs to specify, as it describes the underlying model architecture rather than the task requirements.

Answer

The AWS account ID

Answer

The model's internal parameter count

Question 13

Which THREE techniques are commonly used to mitigate hallucinations in RAG applications?

Accepted Answer

Adding 'I don't know' as a fallback response. Option A is correct because explicitly allowing an 'I don't know' fallback gives the model a safe, grounded escape when retrieved context lacks the answer, reducing the incentive to fabricate. Option C is correct because requiring citations forces the model to tie each claim to specific retrieved passages, making unsupported statements detectable and discouraging invented facts. Option D is correct because instructing the model to answer only from the provided context constrains generation to the retrieved evidence, which is the core grounding technique in RAG. Option B is not appropriate because a longer output token limit only allows more text and does not improve factual grounding, and can even increase drift. Option E is not appropriate because raising the inference temperature increases randomness and creativity, which typically worsens hallucination rather than mitigating it.

Answer

Using the longest possible output token limit

Answer

Increasing the inference temperature

Question 14

You are monitoring your Bedrock usage costs. You notice a spike in costs despite the number of requests remaining steady. What is the most likely cause?

Accepted Answer

The token count per request has increased. The most likely cause of increased Bedrock costs with steady request volume is an increase in the token count per request. Bedrock pricing is based on the number of input and output tokens processed, so if each request now uses more tokens (e.g., longer prompts or responses), costs will rise even if the number of requests remains constant.

Answer

The number of users has increased

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The model has been upgraded to a newer version

Answer

The regional data transfer cost has increased

Question 15

When using the Bedrock Converse API, how do you provide multiple documents to the model for consideration?

Accepted Answer

Include them in the 'document' field within the messages array. The Bedrock Converse API allows you to include documents directly in the messages array using the 'document' field. This enables the model to consider multiple documents in a single request without needing a Knowledge Base or S3 URLs.

Answer

Upload them to S3 and include the URL in the system message

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They must be indexed in a Knowledge Base first

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Attach them as multi-part form data in the HTTP request