AIF-C01 Exam Questions 2026 – Practice Test with Verified Answers

Explanation:
Step 1: Store meeting audio recordings in an Amazon S3 bucket.
Step 2: Convert meeting audio recordings to meeting text files by using Amazon Transcribe.
Step 3: Summarize meeting text files by using Amazon Bedrock.
The company wants to create an application to summarize meeting audio recordings, which requires a sequence of steps involving storage, speech-to-text conversion, and text summarization. Amazon S3 is the recommended storage service for audio files, Amazon Transcribe converts audio to text, and Amazon Bedrock provides generative AI capabilities for summarization. These three steps, in this order, create an efficient workflow for the application.
Exact Extract from AWS AI Documents:
From the Amazon Transcribe Developer Guide:
"Amazon Transcribe uses deep learning to convert audio files into text, supporting applications such as meeting transcription. Audio files can be stored in Amazon S3, and Transcribe can process them directly from an S3 bucket."
From the AWS Bedrock User Guide:
"Amazon Bedrock provides foundation models that can perform text summarization, enabling developers to build applications that generate concise summaries from text data, such as meeting transcripts."
(Source: Amazon Transcribe Developer Guide, Introduction to Amazon Transcribe; AWS Bedrock User Guide, Text Generation and Summarization)
Detailed
Step 1: Store meeting audio recordings in an Amazon S3 bucket. Amazon S3 is the standard storage service for audio files in AWS workflows, especially for integration with services like Amazon Transcribe. Storing the recordings in S3 allows Transcribe to access and process them efficiently. This is the first logical step.
Step 2: Convert meeting audio recordings to meeting text files by using Amazon Transcribe. Amazon Transcribe is designed for automatic speech recognition (ASR), converting audio files (stored in S3) into text. This step is necessary to transform the meeting recordings into a format that can be summarized.
Step 3: Summarize meeting text files by using Amazon Bedrock. Amazon Bedrock provides foundation models capable of generative AI tasks like text summarization. Once the audio is converted to text, Bedrock can summarize the meeting transcripts, completing the application’s requirements.
Unused Options Analysis:
Convert meeting audio recordings to meeting text files by using Amazon Polly. Amazon Polly is a text-to-speech service, not for converting audio to text. This option is incorrect and not used.
Store meeting audio recordings in an Amazon Elastic Block Store (Amazon EBS) volume. Amazon EBS is for block storage, typically used for compute instances, not for storing files for processing by services like Transcribe. S3 is the better choice, so this option is not used.
Summarize meeting text files by using Amazon Lex. Amazon Lex is for building conversational interfaces (chatbots), not for text summarization. Bedrock is the appropriate service for summarization, so this option is not used.
Hotspot Selection Analysis:
The task requires selecting and ordering three steps from the list, with each step used exactly once or not at all. The selected steps―storing in S3, converting with Transcribe, and summarizing with Bedrock―form a complete and logical workflow for the application.
Reference: Amazon Transcribe Developer Guide: Introduction to Amazon Transcribe (https://docs.aws.amazon.com/transcribe/latest/dg/what-is.html)
AWS Bedrock User Guide: Text Generation and Summarization (https://docs.aws.amazon.com/bedrock/latest/userguide/what-is-bedrock.html)
AWS AI Practitioner Learning Path: Module on Speech-to-Text and Generative AI
Amazon S3 User Guide: Storing Data for Processing (https://docs.aws.amazon.com/AmazonS3/latest/userguide/Welcome.html)

Explanation:
The verified answer is D. Bilingual Evaluation Understudy (BLEU) score. The company wants to evaluate whether the chatbot’s generated language matches a target communication style that includes creative spelling and shortened words. AWS AI Practitioner guidance lists BLEU, ROUGE, and BERT Score as relevant metrics for assessing foundation model performance. AWS Prescriptive Guidance describes BLEU as a metric-based evaluation method that measures n-gram overlap and focuses on precision. Because the requirement is about matching a particular style of generated language against reference examples, BLEU is the best fit among the given options.
F1 score is incorrect because it is primarily used for classification-style problems where precision and recall are combined into one score. It is not the correct metric for evaluating generated text style.
BERT Score is incorrect in this specific question because BERT Score focuses on semantic similarity using contextual embeddings. That can be useful when paraphrases should be accepted, but the question emphasizes surface language style: creative spelling, shortened words, and target-audience wording. Those are closer to token and phrase overlap against reference-style responses.
ROUGE is incorrect because ROUGE is commonly used for summarization and measures overlap with reference text, especially recall-oriented overlap. The scenario is not primarily asking whether the chatbot preserved all important content from a source document. It is asking whether the generated response matches a language style.
BLEU is therefore the best answer because it can compare the generated chatbot output to reference examples written in the desired teenage language style and measure how closely the model’s wording overlaps with those references.

Explanation:
Decision trees are an interpretable machine learning algorithm that clearly documents the decision-making process by showing how each input feature affects the output. This transparency is particularly useful when explaining how the model arrives at a certain decision, making it suitable for classifying genes into categories.
Option A (Correct): "Decision trees": This is the correct answer because decision trees provide a clear and interpretable representation of how input features influence the model's output, making it ideal for understanding the inner mechanisms affecting predictions.
Option B: "Linear regression" is incorrect because it is used for regression tasks, not classification.
Option C: "Logistic regression" is incorrect as it does not provide the same level of interpretability in documenting decision-making processes.
Option D: "Neural networks" is incorrect because they are often considered "black boxes" and do not easily explain how they arrive at their outputs.
AWS AI Practitioner
Reference: Interpretable Machine Learning Models on AWS: AWS supports using interpretable models, such as decision trees, for tasks that require clear documentation of how input data affects output decisions.

Explanation:
The best solution is Amazon Bedrock Knowledge Bases, which allows for the seamless integration of structured and unstructured internal documents―such as PDFs, CSVs, and extracted image text― into a retrieval-augmented generation (RAG) pipeline. According to AWS documentation, Bedrock Knowledge Bases offer a no-code or low-code setup to link your enterprise data with foundation models for context-aware responses, without needing to fine-tune or retrain models. The system indexes documents in an Amazon S3 bucket, creates embeddings, and stores them in a vector store. At inference time, the model retrieves relevant context and incorporates it into its response. This approach provides dynamic and up-to-date responses while maintaining data privacy, with minimal operational overhead. Unlike fine-tuning or building a model from scratch in SageMaker, which requires considerable compute resources and model management, Bedrock Knowledge Bases are serverless and easy to configure. It is designed exactly for internal knowledge AI assistants.
Referenced AWS AI/ML Documents and Study Guides:
Amazon Bedrock Developer Guide C Knowledge Bases
AWS Generative AI Best Practices C RAG Patterns for Enterprise Search

Explanation:
Amazon SageMaker Model Cards are specifically designed to support transparency, responsible AI, and governance throughout the machine learning lifecycle. According to AWS AI documentation, Model Cards provide a structured way to document critical information about machine learning models, including intended use cases, business context, training data assumptions, evaluation metrics, ethical considerations, and known limitations or risks. This makes Model Cards the correct feature for the requirements described.
AWS emphasizes that Model Cards help organizations meet internal governance standards and external regulatory or compliance expectations by improving model interpretability and accountability. They allow AI practitioners to clearly explain why a model was built, how it was trained, what data it relies on, and what risks or biases may exist. This aligns directly with the need to document business problems, training considerations, and usage risks.
Other options do not fully address these needs. Amazon SageMaker Model Registry focuses on versioning, approval workflows, and deployment tracking of models, but it does not provide comprehensive documentation for transparency or ethical considerations. Amazon SageMaker Model Monitor is designed to detect data drift, bias drift, and performance degradation in production, not to document assumptions or governance details. Model Dashboard offers a centralized view of deployed models and endpoints but does not provide structured, narrative documentation of model intent or risks.
AWS positions Model Cards as a core component of Responsible AI, enabling organizations to promote fairness, explainability, and trust in machine learning systems. By capturing standardized, auditable documentation, Model Cards ensure that stakeholders―including developers, auditors, and business leaders―can understand how a recommendation system should be used and where caution is required.