AIF-C01 Practice Test 28 — 15 Questions

Question 1

What is the main purpose of a system prompt in a large language model?

Accepted Answer

To define the high-level instructions that set the model's behavior and persona. The system prompt in a large language model (LLM) defines the high-level instructions that set the model's behavior, persona, and constraints for the entire conversation. Unlike user prompts, which are task-specific, the system prompt establishes the model's role (e.g., 'You are a helpful assistant') and governs how it interprets all subsequent interactions, ensuring consistent and aligned outputs.

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To increase the temperature for more creative responses

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To provide an example of the desired output format

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To list all possible tokens to be used in the response

Question 2

Which Amazon Bedrock feature allows you to invoke a model and receive the response token by token as it is generated, reducing perceived latency for the end user?

Accepted Answer

Streaming responses. Streaming responses in Amazon Bedrock allow the model to send back partial results token by token as they are generated, rather than waiting for the entire response to be complete. This reduces perceived latency for end users by enabling them to see the output incrementally, which is especially important for real-time applications like chatbots or interactive assistants.

Answer

Model invocation API

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Provisioned throughput

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Batch inference

Question 3

Which TWO practices help ensure responsible AI when deploying generative AI applications? (Select TWO.)

Accepted Answer

Implement guardrails to filter harmful or inappropriate content. Implementing guardrails (e.g., content filtering) to prevent harmful outputs (D) and continuously monitoring outputs for bias and drift (E) are essential responsible AI practices. Deploying without filters (A) is unsafe, increasing model size (B) reduces interpretability and doesn't address responsibility, and using only synthetic data (C) can introduce bias and is impractical. The correct answers are D and E.

Answer

Deploy the model without any content filters to maximize creativity

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Increase model size to improve accuracy at the expense of interpretability

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Use only synthetic data for training to avoid privacy issues

Question 4

A company wants to encrypt training data stored in Amazon S3 and model artifacts in Amazon SageMaker using customer-managed keys. Which TWO AWS services or features should they use? (Select TWO.)

Accepted Answer

AWS Key Management Service (KMS). AWS KMS allows creation of customer-managed keys. SageMaker can be configured to use a KMS key to encrypt model artifacts and the ML storage volume. S3 also integrates with KMS for server-side encryption.

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Amazon Macie

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AWS Certificate Manager (ACM)

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AWS CloudHSM

Question 5

A company wants to build a system that automatically routes support tickets to the appropriate department based on the text description. They have labeled historical data. Which THREE AWS services could be used to implement this solution? (Select THREE.)

Accepted Answer

Amazon Comprehend. Amazon Comprehend is a natural language processing (NLP) service that can analyze text to extract insights such as sentiment, key phrases, and custom classification. By training a custom classifier on labeled historical support ticket data, Comprehend can automatically route tickets to the appropriate department based on the text description, making it a direct fit for this use case.

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Amazon Rekognition

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Amazon Polly

Question 6

What is the primary role of the self-attention mechanism in the Transformer architecture?

Accepted Answer

To allow each token to attend to all other tokens in the sequence, capturing long-range dependencies. The self-attention mechanism allows each token in the input sequence to directly attend to every other token, computing a weighted sum of all token representations. This enables the model to capture long-range dependencies and contextual relationships regardless of distance, which is the fundamental innovation of the Transformer architecture over recurrent or convolutional models.

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To process tokens in parallel by alternating attention and feed-forward layers

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To reduce the vocabulary size by mapping tokens to embeddings

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To generate the next token one at a time in an autoregressive manner

Question 7

A developer is using Amazon Bedrock to create a chatbot. They want to ensure the bot does not generate toxic or offensive content. Which feature should they enable?

Accepted Answer

Enable content filtering on the Bedrock model.. Amazon Bedrock provides built-in content filtering capabilities that can be enabled at the model invocation level to automatically detect and block toxic or offensive content in both input prompts and generated responses. This feature uses predefined safety filters (e.g., hate, insults, sexual content, violence) and is the most direct and managed way to prevent harmful outputs without requiring custom development.

Answer

Use careful prompt engineering to avoid toxic responses.

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Fine-tune the model on a dataset of safe responses.

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Implement external response validation using a third-party API.

Question 8

A company uses Amazon Bedrock Agents to build an agent that interacts with users through a chat interface. The agent is configured with a knowledge base containing product documentation. Sometimes the agent fails to answer simple questions like 'What is your return policy?' and instead says it cannot find the answer. The knowledge base does contain the return policy. What is the most likely reason?

Accepted Answer

Simplify and clarify the agent's instruction prompt to emphasize knowledge base usage. The agent's instruction prompt might be too complex or not explicitly directing the agent to use the knowledge base. Simplifying the prompt to clearly instruct the agent to first search the knowledge base can resolve the issue. Increasing timeout or adding more data is unnecessary. A stronger model may help but is not the root cause.

Answer

Increase the agent's maximum timeout for processing

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Use a more powerful foundation model for reasoning

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Add more documents to the knowledge base

Question 9

A company deploys an Amazon Bedrock agent that uses a knowledge base with sensitive financial documents. The security team requires that all data retrieval queries be logged for auditing, and that the LLM responses do not contain any personally identifiable information (PII). What combination of services should the company use?

Accepted Answer

Enable Amazon CloudWatch Logs for the Bedrock agent and use Amazon Comprehend to redact PII from responses.. Amazon CloudWatch Logs can capture query logs for the Bedrock agent, and Amazon Comprehend can detect and redact PII from responses. Option A is wrong because GuardDuty is for threat detection, not PII redaction. Option B is wrong because AWS Config is for resource compliance, not logging queries. Option D is wrong because Macie is for data discovery in S3, not real-time PII redaction in responses.

Answer

Enable AWS CloudTrail for API logging and use Amazon GuardDuty to detect PII in responses.

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Use AWS Config to monitor Bedrock resource configurations and apply an IAM policy to restrict PII.

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Use Amazon S3 server access logs for the knowledge base and enable Amazon Macie to redact PII.

Question 10

A company wants to build a customer service chatbot that answers questions about their internal policy documents. The documents are updated monthly, and the team cannot afford to retrain a model each time. Which approach is MOST appropriate?

Accepted Answer

Use Retrieval-Augmented Generation (RAG) with the policy documents indexed in a vector store. RAG (Retrieval-Augmented Generation) allows the LLM to retrieve relevant document sections at inference time, so knowledge stays current without retraining. The other options either require expensive retraining for each update or lack document grounding.

Answer

Train a custom model from scratch on the policy documents each month

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Use a larger foundation model with a longer context window and paste all documents into each prompt

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Fine-tune a base LLM on the policy documents monthly

Question 11

A data scientist is building a text classification system using Amazon Bedrock. They want to evaluate different foundation models for accuracy and latency. Which TWO approaches are appropriate for comparing models? (Select TWO.)

Accepted Answer

Invoke several models with a sample set of queries and compare the outputs manually. Invoking several models with a sample set of queries and comparing outputs manually allows for direct, qualitative assessment of response quality and latency under realistic conditions. This approach is practical for initial model selection in Amazon Bedrock, where you can test multiple FMs via the InvokeModel API without committing to a single model.

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Build a custom model from scratch that combines the outputs of all models

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Read the model documentation and assume the one with the largest parameter count is best

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Select the model with the lowest cost per token without testing

Question 12

Which prompt engineering technique involves providing the model with a few examples of desired input-output pairs before asking it to complete a new instance?

Accepted Answer

Few-shot prompting. Few-shot prompting (Option D) is the correct technique because it explicitly involves providing the model with a few examples of desired input-output pairs before asking it to complete a new instance. This helps the model understand the pattern, format, or task from the examples, improving performance on tasks like classification, translation, or formatting without requiring fine-tuning.

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Zero-shot prompting

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Chain-of-thought prompting

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System prompting

Question 13

An ML team notices that the training accuracy is 99% but validation accuracy is only 72%. Which concept best describes this situation?

Accepted Answer

Overfitting. The model achieves 99% accuracy on training data but only 72% on validation data, which is a classic symptom of overfitting. Overfitting occurs when the model learns noise and specific patterns in the training set too well, failing to generalize to unseen data. This is often caused by excessive model complexity, such as too many layers in a neural network or too deep a decision tree, relative to the amount of training data.

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Cross-validation error

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Bias-variance tradeoff

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Underfitting

Question 14

A financial services company uses Amazon SageMaker Feature Store to manage features for machine learning models. The compliance auditor requires that all changes to feature definitions are logged and that feature data is immutable once written. Which TWO approaches should the team implement? (Choose two.)

Accepted Answer

Enable AWS CloudTrail for SageMaker Feature Store API calls.. Enabling AWS CloudTrail for SageMaker Feature Store API calls provides a detailed audit log of all operations, including changes to feature definitions (e.g., CreateFeatureGroup, UpdateFeatureGroup). This satisfies the compliance requirement for logging all changes. Option C is correct because enabling feature group versioning in SageMaker Feature Store allows you to track and manage changes to feature definitions over time, ensuring a historical record of modifications.

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Use SageMaker Feature Store offline store with record identifier and event time.

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Implement feature store online store with TTL to automatically expire data.

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Use AWS Config to track changes to Feature Store resources.

Question 15

A practitioner is using Amazon Bedrock to invoke Anthropic Claude for a text generation task. They need the model to output a JSON object with specific keys, and they have observed that the model occasionally produces malformed JSON. Which parameter adjustment is MOST likely to improve JSON formatting consistency?

Accepted Answer

Decrease the temperature parameter. Decreasing the temperature parameter reduces the randomness of the model's output, making it more deterministic and less likely to deviate from the expected JSON structure. Lower temperature values (e.g., 0.1–0.3) encourage the model to choose higher-probability tokens, which improves formatting consistency for structured outputs like JSON.

Answer

Increase the top_k parameter

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Increase the max_tokens parameter

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Add a stop sequence of '}'