AIF-C01 Practice Test 5 — 10 Questions

Question 1

A multinational corporation uses a foundation model via Amazon Bedrock to translate internal communication documents from English to multiple languages. They notice that the translations often miss company-specific jargon and acronyms, leading to confusion. The company has a glossary of approved translations for terms like 'Project Atlas' and 'Operation Synergy.' They want to improve translation accuracy quickly and with minimal effort. What approach should they take?

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

Accepted Answer

Use prompt engineering to include the glossary in each translation request.. Prompt engineering allows the company to inject the glossary directly into the context window of the foundation model with each translation request. This approach requires no model retraining or infrastructure changes, enabling rapid improvement by simply appending the approved translations as instructions or few-shot examples. It is the quickest and least effortful method to enforce company-specific terminology without altering the underlying model.

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Use a larger foundation model that has better language understanding.

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Fine-tune the foundation model on a corpus of bilingual company documents.

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Switch to Amazon Translate with custom terminology.

Question 2

A data scientist is preparing data for a classification model. The dataset contains missing values in several features. Which TWO approaches are appropriate for handling missing data? (Select TWO.)

Accepted Answer

Remove rows with any missing values. Imputation fills missing values with estimated values (e.g., mean, median, mode), allowing the model to use all samples. Removing rows with missing values is a simple deletion approach.

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Set missing values to zero

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Ignore missing values during training

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Replace missing values with -1

Question 3

A data scientist is building a binary classifier to predict customer churn. The dataset is highly imbalanced (95% non-churn, 5% churn). Which metric should be prioritized for model evaluation?

Accepted Answer

F1 score. In imbalanced datasets, accuracy can be misleading because a model that always predicts the majority class achieves 95% accuracy but fails to identify churners. The F1 score balances precision and recall, making it more informative for the minority class.

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Root Mean Squared Error (RMSE)

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Mean Absolute Error (MAE)

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Accuracy

Question 4

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 5

A developer is building an agent using Amazon Bedrock Agents to automate a multi-step workflow that involves querying several databases and APIs. The agent needs to handle intermediate results and decide the next step based on previous outputs. Which capability of Bedrock Agents enables this?

Accepted Answer

Multi-step reasoning. Amazon Bedrock Agents uses multi-step reasoning (often powered by the ReAct pattern—Reasoning and Acting) to decompose a complex user request into a sequence of logical steps, invoke the appropriate action groups or knowledge bases at each step, and use the output of one step as input to decide the next. This capability is essential for automating workflows that require intermediate results and dynamic decision-making based on previous outputs.

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Action groups

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Guardrails

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Knowledge bases

Question 6

A machine learning team needs to share a trained model with multiple teams across different AWS accounts. The model artifacts are stored in an S3 bucket in the central account. What is the most secure way to grant cross-account read access to the model artifacts?

Accepted Answer

Use AWS KMS to encrypt the artifacts and share the KMS key with the target accounts, then use bucket policies and IAM roles in the target accounts.. It implements a defense-in-depth approach: AWS KMS encrypts the model artifacts at rest, and cross-account access is granted by combining an S3 bucket policy that allows the target accounts' IAM roles to read the objects, with those roles assuming the necessary permissions. This ensures that only authenticated and authorized IAM principals in the target accounts can decrypt and access the artifacts, preventing unauthorized access even if the bucket policy is misconfigured.

Answer

Use an S3 bucket policy that grants access to the root user of each target account.

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Make the S3 bucket public.

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Use S3 cross-region replication to copy the artifacts to each target account's bucket.

Question 7

A company uses Amazon Bedrock to generate marketing content. They want to reduce costs while maintaining response quality. Which action is most effective?

Accepted Answer

Select a smaller foundation model that still meets accuracy requirements.. The most effective cost-reduction strategy because smaller foundation models (FMs) have fewer parameters, resulting in lower compute and inference costs per request. If the smaller model still meets the required accuracy benchmarks for the marketing content task, it directly reduces operational expenditure without sacrificing quality. Amazon Bedrock offers a range of FMs (e.g., from large models like Claude 3 Opus to smaller ones like Claude 3 Haiku), allowing you to match model size to task complexity.

Answer

Fine-tune a larger model to improve accuracy and reduce retries.

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Increase the temperature parameter to get shorter responses.

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Cache previous responses to reuse for similar prompts.

Question 8

A company needs to generate high-quality images from text descriptions for a marketing campaign. They need to ensure the images are photorealistic and that the model can generate variations of a given image. Which type of model should they use?

Question 9

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.

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

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Fine-tune a base LLM on the policy documents monthly

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

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

Answer

Fine-tune a base LLM on the policy documents monthly

Answer

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