AIP-C01 Practice Test 10 — 10 Questions

Question 1

Which AWS service is best used to store and version control your prompt templates for Bedrock?

Accepted Answer

Amazon Bedrock Prompt Management. Amazon Bedrock Prompt Management is the correct answer because it is a purpose-built feature that lets you create, store, version, and reuse prompt templates natively within Bedrock. It provides versioning, aliases, and variables so you can iterate on prompts and roll back to previous versions without external tooling. This integrates directly with Bedrock's InvokeModel and agent APIs, making it the most operationally efficient choice for prompt lifecycle management.

Answer

AWS Systems Manager Parameter Store

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AWS CodeCommit

Answer

Amazon S3

Question 2

You are integrating a SageMaker JumpStart model via the InvokeEndpoint API. The model is failing to process images due to a schema mismatch. Which format does the JumpStart image input expect by default for standard vision models?

Accepted Answer

Base64 encoded string in the JSON input field. SageMaker JumpStart vision models (e.g., image classification, object detection) expect the input image to be Base64-encoded and placed in the JSON payload under the appropriate input field (commonly 'image' or 'data'). The InvokeEndpoint API sends JSON, so raw binary cannot be transmitted directly. The model's inference container decodes the Base64 string back to bytes before preprocessing.

Answer

A URL pointing to an S3 object

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Multi-part form data

Answer

Raw binary stream in the request body

Question 3

What is the primary role of an Embedding Model in a RAG pipeline?

Accepted Answer

To convert text into numerical vectors for semantic similarity search.. Embedding models convert text into numerical vectors that represent the semantic meaning of the content for vector search.

Answer

To act as a firewall for the RAG system.

Answer

To compress large files for storage.

Answer

To generate human-readable summaries.

Question 4

Which TWO of the following actions can help mitigate the risk of 'prompt injection' in a Generative AI application?

Accepted Answer

Sanitize user input before passing it to the model. Option D is correct because sanitizing user input before passing it to the model removes or neutralizes embedded instructions, escape characters, and other adversarial payloads that attackers use to hijack the prompt, directly reducing the attack surface for prompt injection. Option E is correct because Amazon Bedrock Guardrails provides configurable input filtering (including denied topics, content filters, and prompt-attack/word filters) that detects and blocks malicious or manipulative prompts before they reach the foundation model, adding a dedicated defense layer against prompt injection. Option A is not correct because disabling the chat interface eliminates the application's functionality rather than mitigating the risk while preserving legitimate use. Option B is not correct because temperature 0.0 only makes model output more deterministic; it does not prevent an injected instruction from being followed. Option C is not correct because using a more powerful foundation model does not inherently provide prompt-injection protection and may even increase susceptibility to sophisticated adversarial prompts.

Answer

Disable the chat interface

Answer

Set the temperature to 0.0

Answer

Use the most powerful foundation model available

Question 5

Which TWO approaches help optimize the costs of using Amazon Bedrock?

Accepted Answer

Purchasing Provisioned Throughput for predictable workloads. Option C is correct because purchasing Provisioned Throughput for predictable workloads lets you reserve model units at a fixed hourly rate, which is more cost-effective than on-demand pricing when usage is steady and high, and it also provides consistent throughput. Option D is correct because selecting a smaller, more efficient model for simple tasks reduces the per-token cost, since Amazon Bedrock charges based on the model used and smaller models are significantly cheaper than larger ones for tasks that don't require advanced capabilities. Option A is incorrect because using the most expensive model for all tasks unnecessarily increases costs when cheaper models could suffice. Option B is incorrect because disabling all logging does not reduce Bedrock inference costs and may violate compliance or operational requirements. Option E is incorrect because running every request as a separate job adds overhead and does not optimize costs compared to batching or using appropriate model selection.

Answer

Using the most expensive model for all tasks

Answer

Disabling all logging

Answer

Running every request as a separate job

Question 6

A developer needs to monitor their Bedrock application for potential abuse. Which TWO features should they utilize?

Accepted Answer

Amazon CloudWatch metrics (e.g., 5xx error rates). Option A, Amazon CloudWatch metrics such as 5xx error rates, is correct because CloudWatch provides the runtime observability needed to detect abuse-driven failures and anomalies in a Bedrock application, letting the developer alarm on elevated error rates and other service metrics. Option E, AWS CloudTrail to monitor for unusual API calling patterns, is correct because CloudTrail records Bedrock API activity (e.g., InvokeModel calls) and can be analyzed to spot suspicious invocation volumes, unexpected principals, or anomalous call patterns indicative of abuse. Option B, AWS Cost Explorer, only shows aggregated spending trends and cannot identify abusive request patterns or errors. Option C, Amazon Simple Notification Service, is merely a notification delivery mechanism and does not itself detect or monitor abuse. Option D, AWS Trusted Advisor performance checks, focuses on general best-practice and service-limit guidance rather than application-level abuse detection.

Answer

AWS Cost Explorer to track daily spending

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Amazon Simple Notification Service

Answer

AWS Trusted Advisor performance checks

Question 7

Which THREE of these are valid methods for monitoring a Bedrock-based application?

Accepted Answer

AWS CloudTrail for API calls. Option B is correct because AWS CloudTrail captures Bedrock API activity (e.g., InvokeModel, Converse calls) as management and data events, enabling auditing of who called which model and when. Option C is correct because application-level logging lets you record request/response metadata, latency, token usage, and errors from your own code, which is essential for monitoring Bedrock-based applications. Option E is correct because Amazon CloudWatch Metrics provides runtime metrics such as invocation counts, latency, and error rates for Bedrock, and can drive alarms and dashboards. Option A is not valid because Bedrock does not expose model training logs to customers; it is a managed service and training data/logs are not directly accessible. Option D is not valid because manual binary inspection is not a monitoring method for a Bedrock application and provides no operational telemetry.

Answer

Direct access to model training logs

Answer

Manual binary inspection

Question 8

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 9

When fine-tuning a model on Amazon Bedrock, which THREE security considerations are critical?

Accepted Answer

Limiting IAM permissions for the fine-tuning job. Option B is correct because fine-tuning jobs on Amazon Bedrock should run under a scoped IAM role/service role that grants only the permissions needed (e.g., s3:GetObject on the specific training bucket and bedrock:CreateModelCustomizationJob), following least privilege to limit blast radius. Option C is correct because training data stored in Amazon S3 should be encrypted at rest with AWS KMS (SSE-KMS), and Bedrock requires access to the KMS key to read the dataset, protecting sensitive training content. Option E is correct because datasets used for fine-tuning must be reviewed and scrubbed of personally identifiable information (PII) to avoid embedding sensitive data into model weights and to comply with privacy regulations. Option A is wrong because using the root user violates AWS best practices; root should not be used for workloads and lacks granular, auditable permissions. Option D is wrong because making the training dataset publicly readable exposes proprietary or sensitive data to anyone and is a severe security misconfiguration.

Answer

Using a root user account to launch the training job

Answer

Making the training dataset publicly readable

Question 10

A developer needs to measure the quality of a RAG application's responses. Which tool within Amazon Bedrock facilitates this?

Accepted Answer

Amazon Bedrock Model Evaluation.. Amazon Bedrock Model Evaluation allows users to evaluate their models or RAG pipelines against custom or built-in datasets.

Answer

Amazon CloudWatch Metrics.

Answer

AWS Glue Quality Rules.

Answer

Amazon SageMaker Model Monitor.