AIF-C01 Practice Test 40 — 15 Questions

Question 1

An ML engineer wants to store training data in a format optimized for linear data scanning and columnar access in SageMaker. Which format is most appropriate?

Accepted Answer

Parquet. Parquet is a columnar storage format optimized for both linear data scanning and columnar access, making it ideal for training data in SageMaker. It reduces I/O by storing data by columns rather than rows, enabling efficient retrieval of specific features during model training.

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JSON

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Image (JPEG/PNG)

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CSV

Question 2

A company wants to build a recommendation system for its e-commerce website. Historical data includes user purchase history, product categories, and ratings. Which AWS service is most suitable for this task?

Accepted Answer

Amazon Personalize. Amazon Personalize is the correct choice because it is a fully managed AWS service specifically designed to build real-time recommendation systems using user-item interaction data, such as purchase history, product categories, and ratings. It uses machine learning to train and deploy personalized recommendation models without requiring you to manage infrastructure or write custom ML code, making it ideal for e-commerce recommendation scenarios.

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Amazon Forecast

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Amazon SageMaker built-in algorithms

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Amazon Comprehend

Question 3

A financial services firm uses Amazon Bedrock to generate investment summaries. They need to prevent the model from generating content containing personally identifiable information (PII) such as social security numbers. Which feature should they configure in Bedrock Guardrails?

Accepted Answer

Sensitive information filters with PII redaction. Bedrock Guardrails include a PII redaction filter that can detect and block or mask PII in model inputs and outputs.

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Contextual grounding checks

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Content filtering with category-based harmful content filters

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Word filters with a custom list of terms

Question 4

A company uses Amazon Bedrock Agents to automate a multi-step customer support workflow. The agent needs to query a customer database and update a ticket system. Which TWO components are required to enable the agent to interact with these external systems?

Accepted Answer

Lambda functions that implement the business logic for each action. Option E is correct because action groups are the Bedrock Agents construct that defines the APIs, operations, and parameters the agent can invoke to interact with external systems such as the customer database and ticket system. Option D is correct because each action group is backed by a Lambda function that contains the business logic to actually query the database and update the ticket system when the agent calls the action. Together, action groups describe what the agent can do and Lambda functions execute it, which is exactly what is needed for these external interactions. Option A is not required because a vector store like Amazon OpenSearch Serverless is used for retrieval-augmented generation over document embeddings, not for transactional database queries or ticket updates. Option B is not required because Bedrock Guardrails filter and constrain content for safety and compliance, not enable external system integration. Option C is not required because Bedrock Knowledge Bases provide managed RAG over data sources for informational retrieval, not the action-based API or database operations needed here.

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A vector store like Amazon OpenSearch Serverless

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Bedrock Guardrails

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Bedrock Knowledge Bases

Question 5

A developer is testing different prompts for a text generation model on Amazon Bedrock. Which parameter controls the randomness of the model's output?

Accepted Answer

temperature. Temperature directly controls the randomness of the model's output by scaling the logits before applying the softmax function. A higher temperature (e.g., 1.5) increases randomness and creativity, while a lower temperature (e.g., 0.1) makes the output more deterministic and focused. In Amazon Bedrock, this parameter is a core setting for text generation models like Anthropic Claude and Amazon Titan.

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top_p

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stop_sequences

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max_tokens

Question 6

A company uses Amazon SageMaker to host a model for fraud detection. The model must be re-evaluated for bias on a monthly basis. Which SageMaker feature can be used to detect bias in a trained model?

Accepted Answer

SageMaker Clarify. SageMaker Clarify is the correct choice because it is specifically designed to detect bias in machine learning models and data. It provides built-in capabilities to evaluate bias metrics (e.g., difference in positive proportions, disparate impact) both before training (pre-training bias) and after training (post-training bias), making it suitable for the monthly re-evaluation requirement.

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SageMaker Debugger

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SageMaker Model Monitor

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SageMaker Autopilot

Question 7

A company is building a content generation application using Amazon Bedrock. They need to ensure that the model does not generate offensive content and also avoids discussing certain prohibited topics. Which TWO Bedrock features should be combined to achieve this?

Accepted Answer

Bedrock Guardrails topic denial. Bedrock Guardrails content filters (C) are the right choice for blocking offensive content because they let you configure thresholds across categories such as hate, violence, sexual, insults, and misconduct, filtering both prompts and model responses. Bedrock Guardrails topic denial (B) is also correct because it lets you define specific prohibited topics with natural-language descriptions and optional example phrases, so the model refuses to discuss them. Together, content filters handle offensiveness while topic denial handles restricted subject matter, which is exactly the two-part requirement. Bedrock Model Evaluation (A) only measures and compares model quality or metrics; it does not block content at runtime. Bedrock Knowledge Bases (D) is for retrieval-augmented generation over your own data, and Bedrock Agents (E) orchestrates multi-step tasks and API calls, neither of which enforces content or topic restrictions.

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Bedrock Model Evaluation

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Bedrock Knowledge Bases

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Bedrock Agents

Question 8

A team is fine-tuning an Amazon Titan Text model on a small dataset of legal documents. After fine-tuning, the model produces outputs that are factually incorrect and sometimes contradicts the training data. What is the most likely cause?

Accepted Answer

The model was fine-tuned for too many epochs on a small dataset, leading to overfitting. Fine-tuning a large language model like Amazon Titan Text on a very small dataset for too many epochs causes overfitting. The model memorizes the limited training examples instead of learning generalizable patterns, leading to outputs that are factually incorrect or contradictory when faced with inputs that differ slightly from the training data.

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The batch size was too large, reducing gradient noise and causing mode collapse

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The learning rate was set too high, causing the model to diverge

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The base model was not designed for legal domain tasks

Question 9

A company is deploying a machine learning model for real-time fraud detection. The model must have latency under 100ms. Which infrastructure choice is most appropriate?

Accepted Answer

Amazon SageMaker real-time endpoints. Amazon SageMaker real-time endpoints are designed for low-latency inference, typically in the tens of milliseconds, making them suitable for real-time fraud detection where latency must be under 100ms. They deploy a model behind a persistent HTTPS endpoint that auto-scales to handle incoming requests with minimal delay.

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Amazon EC2 with Deep Learning AMI

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Amazon SageMaker batch transform

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Amazon SageMaker notebook instance

Question 10

A developer deployed this guardrail to block sensitive topics and sexual content. However, the model still generates responses about a specific sensitive topic that is not in the TopicPolicy. What should the developer do to prevent this?

Accepted Answer

Add the specific topic to the TopicPolicy list. The guardrail's TopicPolicy is designed to block specific topics by listing them. Since the model is generating responses about a sensitive topic not currently in the policy, adding that topic to the list directly extends the policy's coverage to prevent those responses. This is the precise mechanism for blocking new unwanted topics without altering other guardrail components.

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Add a SensitiveInformationPolicy to filter PII

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Increase the InputStrength of the content filter to MAX

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Change the TopicPolicy Type from DENY to ALLOW

Question 11

A company wants to use Amazon Bedrock to build a multilingual customer support chatbot. The chatbot must answer questions in English, Spanish, and French. Which TWO actions should the company take to achieve this? (Select TWO.)

Accepted Answer

Use system prompts to specify the desired output language based on user input. Option A is correct because system prompts in Amazon Bedrock let you instruct the model to detect the user's language and respond in that same language (English, Spanish, or French), which is the simplest way to control output language without extra infrastructure. Option E is correct because the foundation model itself must support multilingual input and output; models such as Anthropic Claude on Bedrock are trained on multilingual data and can natively understand and generate English, Spanish, and French, making them suitable for this chatbot. Option B is not needed because fine-tuning a separate model per language is costly and unnecessary when a multilingual foundation model plus prompt engineering already handles all three languages. Option C is not appropriate because translating every query to English adds latency, cost, and translation errors, and it does not by itself ensure responses come back in the user's original language. Option D is incorrect because embeddings are used for search and retrieval, not for generating multilingual conversational responses, and using a different embedding model per language does not address the chatbot's language requirements.

Answer

Fine-tune a separate model for each language

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Translate all user queries to English before sending to the model

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Use a different embedding model for each language

Question 12

A financial services firm wants to deploy a generative AI application that answers customer questions about account balances and recent transactions. The firm has strict latency requirements (responses under 2 seconds) and wants to minimize costs. Which strategy for model selection and deployment is MOST appropriate?

Accepted Answer

Select a smaller, faster foundation model (e.g., Amazon Titan Text Lite) and use on-demand inference. For latency-sensitive and cost-conscious applications, selecting a smaller, faster model is preferable over a large model or custom deployment. Provisioned Throughput for dedicated capacity would increase cost and may not be needed if the base model performs adequately.

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Use the largest available foundation model via on-demand inference for highest accuracy

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Fine-tune a large model specifically on account data and deploy on a dedicated endpoint

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Deploy a large model using Provisioned Throughput to guarantee low latency

Question 13

A research team is using Amazon Bedrock to analyze scientific papers. They want the model to generate answers based only on papers published after 2023. Which approach should they use?

Accepted Answer

Use Amazon Bedrock Knowledge Bases with a metadata filter to retrieve only papers published after 2023, and generate responses based on retrieved content.. Amazon Bedrock Knowledge Bases with a metadata filter allows you to restrict retrieval to only documents that match specific metadata criteria, such as publication year. By filtering the vector search to only include papers published after 2023, the model generates responses based solely on that retrieved content, ensuring it does not rely on pre-2023 data. This approach is the only one that guarantees the model's answers are grounded exclusively in the specified time range.

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Fine-tune the model on a dataset of post-2023 papers and deploy it.

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Set the maxTokens to a low value to force the model to rely on recent context.

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Include a system prompt instructing the model to ignore data before 2023.

Question 14

A developer is building an application that translates customer support tickets from English to Spanish using Amazon Bedrock. They need to evaluate the quality of translations. Which automated metric is most appropriate for comparing the model's translations to professional human translations?

Accepted Answer

BLEU. BLEU (Bilingual Evaluation Understudy) is specifically designed to evaluate machine translation quality by comparing n-gram overlap between the model's output and one or more professional human reference translations. It is the standard automated metric for translation tasks on Amazon Bedrock and in NLP generally.

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Accuracy

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BERTScore

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ROUGE

Question 15

A company is building a RAG application that indexes thousands of PDF documents. They notice that some documents are very long (hundreds of pages) and the vector search often returns irrelevant chunks. Which configuration change would MOST improve retrieval relevance?

Accepted Answer

Adjust the chunk size and overlap to better capture context from the documents. Chunk size and overlap directly control how much semantic context each embedded vector carries. When documents are hundreds of pages long, a fixed chunk size (e.g., 512 tokens) can split related sentences across chunks, producing vectors that represent fragments rather than complete ideas. Tuning chunk size and overlap ensures each chunk is semantically self-contained, which is the single most impactful lever for retrieval precision in a RAG pipeline.

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Switch from Amazon OpenSearch Serverless to Pinecone

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Increase the embedding dimension from 1024 to 4096

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Use a larger, more capable foundation model for response generation