AIF-C01 Practice Test 41 — 15 Questions

Question 1

An application needs to store and search vector embeddings of 10 million documents for a RAG system. Which Amazon vector store is a fully managed, serverless option that integrates natively with Amazon Bedrock Knowledge Bases?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Amazon OpenSearch Serverless. Amazon OpenSearch Serverless is a fully managed, serverless vector store that integrates natively with Amazon Bedrock Knowledge Bases. It supports vector search for embeddings and automatically scales compute and storage capacity, making it ideal for RAG workloads with 10 million documents without requiring infrastructure management.

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Amazon Aurora PostgreSQL with pgvector

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MongoDB Atlas

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Pinecone

Question 2

A healthcare startup is using an LLM to summarize patient medical records. They are concerned about hallucinations where the model may invent symptoms or treatments. Which combination of techniques should they implement to reduce hallucinations while maintaining accuracy?

Accepted Answer

Implement RAG with a trusted medical knowledge base and use Bedrock Guardrails with contextual grounding check. Grounding the model with Retrieval-Augmented Generation (RAG) using verified medical databases and applying Bedrock Guardrails with contextual grounding checks directly reduces hallucinations by ensuring responses are based on retrieved facts and filtered for accuracy.

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Apply prompt engineering with few-shot examples and set temperature to 0

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Use a larger LLM and increase the temperature parameter to improve creativity

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Fine-tune the LLM on a large corpus of medical literature and use a higher temperature for diversity

Question 3

Which AWS service provides a serverless API for accessing foundation models with per-token pricing?

Accepted Answer

Amazon Bedrock. Amazon Bedrock is a fully managed service that provides a serverless API for accessing foundation models (FMs) from providers like AI21 Labs, Anthropic, Cohere, Meta, and Stability AI. It offers per-token pricing, meaning you pay only for the number of tokens processed in both input and output, with no upfront commitments or infrastructure management required.

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Amazon API Gateway

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AWS Lambda

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Amazon SageMaker

Question 4

A company wants to build a multi-language customer support chatbot using Amazon Bedrock. The chatbot should support English, Spanish, and French. The team needs to translate user queries into English before processing and then translate responses back. Which TWO approaches could achieve this? (Choose TWO)

Accepted Answer

Use a Bedrock Agent with an action group that calls a translation Lambda function. Option B is correct because a Bedrock Agent can invoke an action group backed by a Lambda function that calls a translation service (e.g., Amazon Translate) to convert the user query into English and translate the response back, giving a deterministic, programmatic translation step within the agent workflow. Option C is correct because a foundation model on Bedrock can be instructed through prompt engineering to perform the translation as part of the same inference call, e.g., asking it to translate the Spanish or French input to English, answer, then translate the answer back, which is a valid no-extra-service approach. Option A is not appropriate because fine-tuning a separate model per language pair is costly and unnecessary when Bedrock models or a translation service can handle translation directly. Option D is wrong because Bedrock Guardrails are for content filtering, PII redaction, and topic denial, not translation. Option E is wrong because a Knowledge Base is for retrieval-augmented generation over stored documents, not for translating queries and responses.

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Use a separate model fine-tuned for translation for each language pair

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Configure Bedrock Guardrails to translate automatically

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Store translated documents in a Bedrock Knowledge Base

Question 5

A developer wants to store and search vector embeddings for a RAG application. Which AWS-managed vector store option is serverless and can be used with Amazon Bedrock?

Accepted Answer

Amazon OpenSearch Serverless. Amazon OpenSearch Serverless is a fully managed, serverless vector store that supports k-NN vector search and is a natively supported backend for Amazon Bedrock Knowledge Bases. It requires no cluster provisioning or capacity planning, making it the correct serverless option for storing and searching embeddings in a RAG application.

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Amazon RDS for MySQL

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Amazon Redshift

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Amazon DynamoDB

Question 6

A company is using Amazon Bedrock to generate code snippets. They notice the model occasionally generates code that fails to compile. What is the most effective way to improve code quality without retraining?

Accepted Answer

Use few-shot prompt engineering with correct code examples and formatting instructions.. Few-shot prompt engineering provides the model with explicit examples of correct code and formatting instructions, guiding it to generate syntactically valid code without modifying the underlying model. This approach leverages in-context learning to improve output quality by conditioning the model on desired patterns, which is more effective than parameter adjustments alone for addressing compilation errors.

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Reduce the temperature parameter to 0 for deterministic output.

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Increase the max token limit to allow the model to complete the code fully.

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Fine-tune the model on a dataset of correct code snippets.

Question 7

A company has built a RAG application using Amazon Bedrock Knowledge Bases. Users report that answers are sometimes based on irrelevant or incorrect document chunks. The team has verified that the embedding model is appropriate and the documents are correctly indexed. What is the MOST likely cause of the poor retrieval quality?

Accepted Answer

The chunking strategy is suboptimal. Chunking strategy directly affects retrieval relevance. If chunks are too large, they may contain irrelevant information; if too small, they may miss context. Overlap size also matters. Optimizing chunking often fixes relevance issues when embeddings are correct.

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The foundation model is too small

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The prompt template is missing instructions

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The vector store is too slow

Question 8

A financial services company uses Amazon Bedrock Knowledge Bases to power a Q&A bot for analysts. They notice that the bot sometimes gives outdated information because documents are updated weekly. They cannot retrain or rebuild the knowledge base weekly. What is the MOST efficient solution?

Accepted Answer

Use incremental data ingestion to sync only the updated documents weekly. Amazon Bedrock Knowledge Bases supports incremental data ingestion, which allows you to sync only the documents that have changed since the last sync. This avoids the need to rebuild the entire knowledge base from scratch, saving time and compute resources while keeping the Q&A bot up to date with weekly document updates.

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Rebuild the entire knowledge base from scratch every week using a scheduled job

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Set the chunk size to zero so the entire document is always retrieved

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Use a Lambda function to manually delete and re‑upload all documents each week

Question 9

A company wants to forecast product demand across thousands of SKUs with different demand patterns. They have 3 years of historical sales data, plus external factors like holidays and promotions. Which combination of AWS services and approach would deliver the most accurate forecasts with minimal manual effort?

Accepted Answer

Use Amazon Forecast with the DeepAR+ algorithm and provide item metadata, holiday calendars, and promotion data. Amazon Forecast is purpose-built for time-series forecasting and automatically handles multiple SKUs, holidays, and promotions. SageMaker would require building custom models from scratch. Comprehend is NLP, and QuickSight is for visualization.

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Use Amazon Comprehend to analyze customer reviews and correlate with sales

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Upload data to Amazon QuickSight and use its built-in forecasting widget

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Train individual ARIMA models for each SKU using Amazon SageMaker built-in algorithms

Question 10

A bank uses an AI system to detect fraudulent transactions. The model has high precision but low recall for small transactions, potentially missing fraud. Which approach aligns with responsible AI?

Accepted Answer

Tune the model to achieve an acceptable balance between recall and precision. Responsible AI requires balancing competing objectives like precision and recall to align with ethical principles and business needs. In fraud detection, high precision with low recall means many fraudulent transactions are missed, which can lead to significant financial losses and erode customer trust. Tuning the model to achieve an acceptable trade-off ensures that the system is both effective and fair, minimizing harm while maintaining operational viability.

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Send all flagged transactions to customers for confirmation

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Focus only on precision to minimize false positives

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Increase the detection threshold to reduce false positives

Question 11

A company wants to use a foundation model to automatically summarize lengthy documents. Which capability of foundation models is being utilized?

Accepted Answer

Text generation. Summarization is a text generation task where the model produces a concise version of the original content. Foundation models (e.g., GPT, Claude) are pre-trained on vast corpora and can generate coherent summaries by predicting the next tokens conditioned on the input document. This directly utilizes the text generation capability, not classification or translation.

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Sentiment analysis

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Text classification

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Machine translation

Question 12

A financial institution is deploying a fraud detection model using Amazon SageMaker. The model must be able to handle sudden spikes in inference requests during promotional events while keeping costs low. The team wants to use a serverless architecture to avoid provisioning idle capacity and to scale automatically from zero. However, the inference latency requirement is under 5 seconds for each request. Which SageMaker inference option should they choose?

Accepted Answer

Use Amazon SageMaker Serverless Inference. Amazon SageMaker Serverless Inference is the correct choice because it automatically scales from zero to handle sudden spikes in inference requests, aligning with the requirement to avoid provisioning idle capacity. It also meets the sub-5-second latency requirement for fraud detection, as it is designed for low-latency, on-demand inference without managing underlying infrastructure.

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Use Amazon SageMaker Multi-Model Endpoints

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Use Amazon SageMaker real-time endpoints with auto-scaling

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Use Amazon SageMaker Asynchronous Inference

Question 13

Which metric is most appropriate for evaluating a classification model when false positives are costly?

Accepted Answer

Precision. Precision is the most appropriate metric when false positives are costly because it measures the proportion of true positive predictions among all positive predictions (TP / (TP + FP)). A high precision indicates that when the model predicts a positive class, it is very likely correct, minimizing the number of false positives. This directly aligns with the business requirement to avoid costly false alarms.

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F1 score

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Recall

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Accuracy

Question 14

A company uses Bedrock Guardrails to filter harmful content in a generative AI application. They need to prevent the model from discussing proprietary internal projects. Which Guardrail component should be configured?

Accepted Answer

Topic restrictions. Topic restrictions allow administrators to define denied topics; the model will not generate responses related to those topics.

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Content filters

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Grounding check

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Word filters

Question 15

A data science team is building a resume screening model and wants to ensure it does not exhibit gender bias. Which TWO actions are most effective for mitigating bias? (Choose TWO.)

Accepted Answer

Apply adversarial debiasing techniques during training.. Option A is correct because adversarial debiasing trains a predictor alongside an adversary that tries to detect the protected attribute (gender) from the predictor's outputs, forcing the model to learn representations that cannot discriminate by gender, which directly mitigates bias during training. Option D is correct because regularly auditing model predictions for disparate impact across genders (e.g., using metrics like demographic parity, equal opportunity, or the 80% rule) detects bias that may persist or emerge after deployment and enables corrective action. Option B is not correct because increasing model complexity with deep learning does not inherently reduce bias and can even amplify it by fitting spurious correlations in the data. Option C is not correct because simply removing the gender attribute and correlated features does not eliminate bias, since proxy variables and historical patterns can still encode gender information. Option E is not correct because equal representation of male and female candidates in the training set does not guarantee fairness, as bias can arise from label imbalance, feature correlations, or unequal outcomes despite balanced group sizes.

Answer

Use a more complex deep learning model.

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Remove the gender attribute and all correlated features from the dataset.

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Ensure the training dataset has equal numbers of male and female candidates.