AIP-C01 Practice Test 12 — 15 Questions

Question 1

When invoking models in Bedrock, what does the 'streaming' feature provide?

Accepted Answer

Reduced latency until the first token appears. Streaming in Amazon Bedrock returns the model's output incrementally as tokens are generated, rather than waiting for the full response. The primary benefit is reduced latency until the first token appears, improving perceived responsiveness for interactive applications. It does not change the model's accuracy, context window, or cost.

Answer

Support for larger context windows

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Higher model accuracy

Answer

Lower cost per invocation

Question 2

Which TWO factors are critical when choosing a model for a RAG architecture that requires high grounding accuracy?

Accepted Answer

The model's support for citation and source attribution.. Option C is correct because high grounding accuracy in RAG depends on the model's ability to cite and attribute retrieved sources, letting users verify that answers are anchored in the provided documents rather than hallucinated. Option E is correct because a larger context window allows more retrieved chunks to be included in the prompt, giving the model sufficient evidence to ground its response accurately. Option A is not critical, since the training date affects knowledge freshness but RAG grounding comes from retrieved context, not parametric memory. Option B is irrelevant, as image generation has no bearing on textual grounding accuracy. Option D is not decisive, because parameter count alone does not determine how faithfully a model uses retrieved context.

Answer

The model's training date.

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The model's ability to generate images.

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The number of parameters in the model.

Question 3

A company is building a RAG application using Amazon Bedrock and needs to ensure that the data ingested into the knowledge base is filtered based on user identity. Which approach should the developer use?

Accepted Answer

Use Amazon Bedrock Knowledge Base metadata filtering during the retrieval phase to apply access controls.. Amazon Bedrock Knowledge Bases support metadata filtering during retrieval, allowing the application to pass user identity attributes as filters so only documents the user is authorized to see are retrieved. This enforces access control at the retrieval phase, before the model generates a response. It is the intended mechanism for identity-based filtering in RAG.

Answer

Use Lambda authorizers to filter the retrieved results after they are returned by the model.

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Create separate vector indexes for every user role within the Knowledge Base configuration.

Answer

Implement fine-grained access control using IAM policies on the S3 bucket containing the source data.

Question 4

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 5

Which metric should a developer monitor to ensure the RAG system remains performant as usage increases?

Accepted Answer

Inference Latency.. Latency and Token Consumption (or request count) are key indicators of RAG performance and cost efficiency.

Answer

Vector Database Storage Capacity.

Answer

IAM User Count.

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Model Training Loss.

Question 6

An organization wants to use a model that supports both text and image input. Which family of foundation models should they select in Amazon Bedrock?

Accepted Answer

Claude 3 Sonnet. Amazon Bedrock supports multi-modal models like Claude 3 Sonnet and Claude 3 Opus which handle both text and images.

Answer

Amazon Titan Text Express

Answer

Jurassic-2 Mid

Answer

Cohere Command R

Question 7

When designing a RAG system, why would a developer choose Amazon Titan Text Embeddings over a third-party embedding model?

Accepted Answer

It is optimized for Amazon Bedrock Knowledge Bases, ensuring seamless integration and scalability.. Amazon Titan Embeddings are optimized to work natively with Amazon Bedrock Knowledge Bases and provide high performance and integration benefits.

Answer

It supports multi-modal input processing natively.

Answer

It is the only model that supports English.

Answer

It prevents the model from hallucinating.

Question 8

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

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Using smaller instance types

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Deleting logs every day

Question 9

Which data format is best suited for ingestion into an Amazon Bedrock Knowledge Base?

Accepted Answer

PDF, Markdown, or HTML files stored in Amazon S3.. Amazon Bedrock Knowledge Bases are designed to ingest unstructured text data from documents such as PDF, Markdown, and HTML files stored in Amazon S3. These formats are supported for parsing and chunking into vector embeddings. Storing them in S3 provides a scalable, durable source for ingestion.

Answer

Raw binary executable files.

Answer

Encrypted raw stream data.

Answer

Proprietary database dump files.

Question 10

Which prompt engineering technique involves providing the model with a few examples of desired input-output pairs to guide the generation?

Accepted Answer

Few-shot prompting. Few-shot prompting is the technique of including a small number of example input-output pairs in the prompt to demonstrate the desired task and format, guiding the model to generalize to the new input. This contrasts with zero-shot, where no examples are given. The examples act as in-context learning signals.

Answer

Chain-of-thought

Answer

System priming

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Zero-shot prompting

Question 11

A developer wants to ensure that a generative AI application does not provide medical advice. How should they configure this?

Accepted Answer

Create a Guardrail with a 'Denied Topics' policy for medical advice.. Amazon Bedrock Guardrails allow you to define a 'Denied Topics' policy that explicitly blocks the model from responding to user queries about specific topics, such as medical advice. This is a managed, configurable safeguard that works across models and is enforced at the API level, ensuring consistent behavior. Unlike prompt-based instructions, Guardrails provide a deterministic, policy-driven control that cannot be overridden by user input or model drift.

Answer

Disable the model's knowledge of medical terms.

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Instruct the model via the system prompt only.

Answer

Use a prompt chain to filter out medical terms.

Question 12

Which TWO metrics are critical for monitoring an Amazon SageMaker inference endpoint to ensure operational health?

Accepted Answer

Invocations. Option A (Invocations) is correct because it is a core CloudWatch metric for SageMaker endpoints, counting the number of requests sent to the endpoint's InvokeEndpoint API, which directly reflects traffic volume and whether the endpoint is being used as expected. Option D (ModelLatency) is correct because it measures the interval between when the endpoint receives a request and when it returns the response, making it a key indicator of inference performance and operational health. Together, Invocations and ModelLatency let operators detect traffic anomalies and latency degradation that signal endpoint problems. Option B (NetworkPacketLoss) is not a standard SageMaker endpoint metric and is not surfaced for monitoring inference health. Option C (S3StorageUsage) relates to S3 bucket capacity, not endpoint operation, and option E (IAMUserCount) is an identity metric unrelated to endpoint performance.

Answer

NetworkPacketLoss

Answer

S3StorageUsage

Answer

IAMUserCount

Question 13

An organization is concerned about data residency requirements for their AI models. Where should they look to identify which AWS regions support specific models?

Accepted Answer

The Amazon Bedrock documentation region table.. The Amazon Bedrock documentation includes a region table that lists which foundation models are available in which AWS regions. This is the authoritative source for identifying model availability by region, which is essential for meeting data residency requirements. Organizations should consult this table before architecting their GenAI application to ensure the chosen model runs in an approved region.

Answer

The IAM console.

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The AWS CLI.

Answer

The Amazon S3 bucket settings.

Question 14

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 15

A business requirement is to ensure that the RAG model output is grounded solely in the provided documents. How can this be enforced?

Accepted Answer

Set the model temperature to a very low value (e.g., 0.0) and use clear system instructions.. Setting temperature to a very low value (e.g., 0.0) makes the model's output more deterministic and reduces creative deviation, while clear system instructions explicitly tell the model to answer only from the provided documents. Together, these enforce grounding in the RAG context and minimize hallucination.

Answer

Increase the temperature of the model to 1.0.

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Use a secondary model to re-check the output.

Answer

Disable the model's training data.