AIF-C01 Practice Test 46 — 15 Questions

Question 1

A startup wants to generate high-quality images from text descriptions using Amazon Bedrock. They need to create realistic images of products for an e-commerce catalog. Which model provider should they choose?

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

Accepted Answer

Stability AI. Stability AI is the correct choice because it specializes in image generation models, such as Stable Diffusion, which are designed to produce high-quality, photorealistic images from text prompts. Amazon Bedrock offers Stability AI's models for text-to-image tasks, making it ideal for generating realistic product images for an e-commerce catalog.

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Anthropic

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Mistral AI

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Cohere

Question 2

A developer is building an application that uses Amazon Bedrock to answer questions based on a large internal knowledge base. The knowledge base contains PDFs, Word documents, and web pages. Which TWO AWS services are commonly used together to implement a Retrieval-Augmented Generation (RAG) architecture on AWS? (Select TWO.)

Accepted Answer

Amazon Bedrock Knowledge Bases. Amazon Bedrock Knowledge Bases (A) is correct because it is the managed RAG capability that ingests the PDFs, Word documents, and web pages, chunks and embeds them, and orchestrates retrieval so the foundation model can answer questions grounded in the internal knowledge base. Amazon OpenSearch Serverless (E) is correct because it is the commonly used vector store backing a Bedrock knowledge base, holding the embeddings and performing the vector similarity search that retrieves relevant passages at query time. Together they form the standard AWS RAG pattern: Bedrock Knowledge Bases for ingestion, embedding, and orchestration, and OpenSearch Serverless as the vector index for semantic retrieval. Amazon SageMaker Ground Truth (B) is a data-labeling service for building training datasets, not a retrieval component. AWS Glue (C) is a serverless ETL/catalog service and Amazon Athena (D) is a serverless SQL query engine over S3; neither provides the vector similarity search or managed RAG orchestration this scenario requires.

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Amazon SageMaker Ground Truth

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AWS Glue

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Amazon Athena

Question 3

A startup needs to predict customer churn based on historical data containing labels (churned or not). Which type of machine learning should they use?

Accepted Answer

Supervised learning. The startup has labeled historical data (churned or not), which is the defining characteristic of supervised learning. The goal is to learn a mapping from input features to the known output labels to predict churn for new customers. This is a classic classification problem, making supervised learning the correct choice.

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Reinforcement learning

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Unsupervised learning

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Semi-supervised learning

Question 4

A marketing agency wants to analyze customer feedback from social media posts to gauge sentiment. They have no labeled data and limited ML expertise. The team needs a managed service that provides pre-trained models for sentiment analysis without requiring them to train or manage infrastructure. They also need to process text in multiple languages. Which AWS service should they use?

Accepted Answer

Use Amazon Comprehend with its default sentiment analysis model. Amazon Comprehend is a fully managed natural language processing (NLP) service that provides pre-trained models for sentiment analysis, key phrase extraction, and language detection. It requires no labeled data, no model training, and no infrastructure management, making it ideal for teams with limited ML expertise. Comprehend natively supports multiple languages, including Spanish, French, German, and many others, directly addressing the requirement to process text in multiple languages.

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Use Amazon SageMaker to train a custom sentiment analysis model

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Use AWS Glue to build a custom NLP pipeline

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Use Amazon Rekognition for text analysis

Question 5

A company is using Amazon Rekognition to detect objects in images. They need to detect custom objects that are specific to their domain. What should they do?

Accepted Answer

Use Amazon Rekognition Custom Labels. Amazon Rekognition Custom Labels allows you to train a custom model using your own labeled images to detect domain-specific objects that are not covered by Rekognition's built-in labels. This is the correct service for custom object detection without needing to build a model from scratch.

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Use Amazon Rekognition's built-in labels

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Use Amazon SageMaker Object Detection algorithm

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Use Amazon Comprehend

Question 6

A startup with limited ML expertise wants to quickly prototype a binary classification model using a small customer dataset. They need a managed environment to run Jupyter notebooks and access pre-built algorithms. Which AWS service should they choose?

Accepted Answer

Amazon SageMaker. Amazon SageMaker is the correct choice because it provides a fully managed environment for Jupyter notebooks and includes built-in, pre-built algorithms for binary classification. This allows the startup to quickly prototype without deep ML expertise, as SageMaker handles infrastructure, scaling, and model training.

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AWS Lambda

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Amazon EMR

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AWS Glue

Question 7

A data scientist wants to perform automatic model tuning (hyperparameter optimization) on SageMaker. They need to find the best hyperparameters for a gradient boosting model. Which strategy is BEST for this task?

Accepted Answer

Bayesian optimization. Bayesian optimization is the best strategy for automatic model tuning on SageMaker because it builds a probabilistic model of the objective function and uses it to select the most promising hyperparameters to evaluate next. This approach is far more sample-efficient than random or grid search, making it ideal for expensive-to-evaluate models like gradient boosting, where each training run consumes significant time and compute resources.

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Random search

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Grid search

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Exhaustive search

Question 8

Which TWO are benefits of using Amazon SageMaker JumpStart for foundation models? (Choose 2)

Accepted Answer

Built-in fine-tuning scripts and notebooks. Option A is correct because SageMaker JumpStart provides built-in fine-tuning scripts and example notebooks that let you adapt foundation models to your own data without writing the training pipeline from scratch. Option D is correct because JumpStart includes a catalog of pre-trained foundation models (e.g., from providers like Hugging Face, Meta, and AI21) that you can deploy or customize directly. Option B is not correct because fine-tuning still requires code or configuration, such as selecting hyperparameters and preparing datasets, so it is not fully no-code. Option C is not correct because automatic scaling still requires configuring endpoint settings like instance counts and autoscaling policies. Option E is not correct because model usage in JumpStart is billed through SageMaker resources and is not free or unlimited.

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No coding required to fine-tune models

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Automatic scaling without any configuration

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Free unlimited usage for all models

Question 9

A financial institution uses a machine learning model to approve loan applications. To comply with regulatory requirements, they need to explain individual predictions. Which AWS service and feature combination should they use?

Accepted Answer

Amazon SageMaker Clarify with SHAP values. Amazon SageMaker Clarify provides explainability for machine learning models, including feature attribution using SHAP (SHapley Additive exPlanations) values. SHAP values explain individual predictions by quantifying the contribution of each feature. This is essential for regulatory compliance in financial services, where explanations for loan decisions are required. SageMaker Clarify can generate these explanations and integrate with SageMaker models.

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AWS Glue DataBrew with correlation analysis

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Amazon QuickSight with ML Insights

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Amazon SageMaker Model Monitor with data quality checks

Question 10

Which parameter controls the randomness of generated text in a foundation model?

Accepted Answer

temperature. Temperature is the correct parameter because it directly controls the randomness of token sampling in a foundation model. A lower temperature (e.g., 0.1) makes the model more deterministic by concentrating probability mass on the most likely tokens, while a higher temperature (e.g., 1.5) flattens the probability distribution, increasing the likelihood of less probable tokens and thus generating more diverse or creative outputs.

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top_p

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stop sequences

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max_tokens

Question 11

A company uses a foundation model for real-time translation in a chat application. The latency is high. Which optimization would reduce latency the most?

Accepted Answer

Use model distillation to create a smaller model. Model distillation reduces the size of the foundation model by training a smaller 'student' model to mimic the behavior of a larger 'teacher' model. This directly decreases inference latency because the smaller model requires fewer computational resources (FLOPs) per forward pass, which is critical for real-time translation in a chat application where low latency is paramount.

Answer

Increase batch size

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Use a larger model

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Use a CDN for model weights

Question 12

A company using Amazon Bedrock needs to redact personally identifiable information (PII) from user inputs before sending them to the foundation model. Which Bedrock Guardrails component should be configured?

Accepted Answer

Sensitive information filters. PII redaction in Guardrails automatically detects and redacts PII in user inputs or model responses.

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Content filters

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Topic restrictions

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Word filters

Question 13

A company is using Amazon Bedrock to summarize long documents. They notice that the summary sometimes omits key details. What is the most likely cause?

Accepted Answer

The model's context window is too small. When summarizing long documents with Amazon Bedrock, the model's context window determines the maximum amount of text it can process at once. If the document exceeds this limit, the model truncates or ignores portions, leading to omitted key details. This is the most likely cause because summarization requires the model to attend to the entire input, and a small context window directly prevents full coverage.

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The model is overfitted

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The prompt lacks examples

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The temperature parameter is too high

Question 14

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?

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.

Answer

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 15

Which AWS service can be used to convert a recorded speech file into text for further analysis?

Accepted Answer

Amazon Transcribe. Amazon Transcribe is an automatic speech recognition (ASR) service that converts audio speech into text. It is specifically designed for this task, supporting both real-time streaming and batch processing of recorded speech files, making it the correct choice for converting a recorded speech file into text for further analysis.

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Amazon Polly

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Amazon Translate

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Amazon Rekognition