AIF-C01 Question Bank · Latest 50 Questions Free Preview (Page 1/5)
Questions 1–10 of the latest 50 AIF-C01 questions (5 pages total), with answers and explanations, no login required. Practice all 435 questions online.
Question 1 · No. 386 · Single choice
A company wants to customize Amazon Bedrock foundation models (FMs) to improve an application's performance. The company must prepare a training dataset for text-to-text model fine-tuning. Which dataset format should the company use to train the models?
- A. A JSON file with labeled data
- B. A CSV file with unlabeled data
- C. A CSV file with tabular data
- D. A text file with unlabeled data
Answer: A
Explanation: For Amazon Bedrock fine-tuning of text-to-text foundation models, the training dataset must be in JSON Lines (JSONL) format with labeled data consisting of prompt-completion pairs. Amazon Bedrock expects a JSON file containing labeled training examples where each entry has a 'prompt' field and a 'completion' field. Options B, C, and D are incorrect because fine-tuning requires labeled data (not unlabeled) and the required format is JSON, not CSV or plain text files.
Question 2 · No. 387 · Single choice
A company wants to classify images of different objects based on custom features extracted from a dataset. Which solution will meet this requirement with the LEAST development effort?
- A. Use traditional ML algorithms with custom features extracted from the dataset.
- B. Use a pre-trained deep learning model. Fine-tune the model on the dataset.
- C. Use a generative adversarial network (GAN) model to classify the images.
- D. Use a support vector machine (SVM) with manually engineered features for classification.
Answer: B
Explanation: Using a pre-trained deep learning model with fine-tuning requires the least development effort for image classification tasks. Pre-trained models (such as ResNet, VGG, or Inception) have already learned rich feature representations from large datasets like ImageNet. Fine-tuning only requires adjusting the model on the new dataset, which eliminates the need for manual feature engineering, custom feature extraction pipelines, or building models from scratch. Options A and D require significant effort in manual/custom feature engineering, while option C (GANs) is primarily designed for image generation rather than classification and would require substantial custom development.
Question 3 · No. 388 · Single choice
An AI practitioner wants to evaluate ML models. The AI practitioner wants to provide explanations of model predictions to customers and stakeholders. Which AWS service or feature will meet these requirements?
- A. Amazon QuickSight
- B. Amazon Comprehend
- C. AWS Trusted Advisor
- D. Amazon SageMaker Clarify
Answer: D
Explanation: Amazon SageMaker Clarify is specifically designed to help AI practitioners understand and explain ML model predictions. It provides model explainability features that allow practitioners to provide explanations of model predictions to customers and stakeholders. The other options do not meet these requirements: Amazon QuickSight is a BI tool for data visualization, Amazon Comprehend is an NLP service, and AWS Trusted Advisor provides infrastructure recommendations.
Question 4 · No. 389 · Single choice
A news agency publishes articles in English. The agency wants to make articles available in other languages. Which solution meets these requirements?
- A. Add Amazon Transcribe to the company’s website.
- B. Use the Amazon Translate real-time translation feature.
- C. Add Amazon Personalize to the company’s website.
- D. Use the Amazon Textract real-time document processing feature.
Answer: B
Explanation: Amazon Translate is the AWS service specifically designed for translating text content between different languages. It offers a real-time translation feature that can quickly translate articles from English into other languages, which directly meets the news agency's requirement of making articles available in multiple languages. Amazon Transcribe is for speech-to-text conversion, Amazon Personalize is for recommendation systems, and Amazon Textract is for extracting text from documents (OCR), none of which address language translation needs.
Question 5 · No. 390 · Single choice
A company is building a large language model (LLM)-based AI assistant to support service agents by automatically managing customer inquiries. The company wants to reduce the effort that customer service agents require during support calls. The company needs to select a metric to evaluate the AI assistant against one of the company’s business objectives. Which metric will meet these requirements?
- A. Website engagement rate
- B. Average call duration
- C. Agent attrition rate
- D. First contact resolution rate
Answer: B
Explanation: The business objective is to reduce the effort that customer service agents require during support calls. Average call duration directly measures how long agents spend on calls with customers. If the LLM-based AI assistant is effective at automatically managing customer inquiries, it will shorten call durations, thereby directly reducing the time and effort agents need to invest during support calls. Website engagement rate (A) relates to website interaction, not call effort. Agent attrition rate (C) is an indirect, lagging indicator that could result from reduced effort but doesn't directly measure call effort. First contact resolution rate (D) measures resolution effectiveness, not the effort required by agents during the call.
Question 6 · No. 391 · Single choice
A company wants to use its documents as a knowledge base for a large language model (LLM) in a Retrieval Augmented Generation (RAG) solution. Which solution will meet these requirements?
- A. Encrypt each document with encryption keys.
- B. Create embeddings from document chunks.
- C. Label the document data with metadata.
- D. Generate one-hot encoding for each document
Answer: B
Explanation: In a Retrieval Augmented Generation (RAG) solution, documents need to be converted into a format that allows semantic search and retrieval. This is done by splitting documents into smaller chunks and creating vector embeddings (dense numerical representations) from those chunks. These embeddings capture the semantic meaning of the text and are stored in a vector database. When a user query is received, it is also converted into an embedding, and the system retrieves the most semantically similar chunks to provide relevant context to the LLM. Option A (encryption) is for security, not knowledge base creation. Option C (metadata labeling) is supplementary and not the core mechanism. Option D (one-hot encoding) is used for categorical variables in traditional ML, not for representing document semantics in RAG.
Question 7 · No. 392 · Single choice
A company deploys a foundation model (FM). The company notices that the FM is producing answers to user-submitted questions about politics. The company wants to ensure that the model does not send answers to political questions to users. Which AWS solution will meet this requirement?
- A. Amazon Bedrock Guardrails
- B. Amazon Bedrock Agents
- C. Amazon SageMaker Clarify
- D. Amazon SageMaker Model Monitor
Answer: A
Explanation: Amazon Bedrock Guardrails can filter undesirable content and deny responses about specified topics, such as politics.
Question 8 · No. 393 · Single choice
A company plans to build an AI model for the company’s global customer base. The company wants to train the model on a dataset that reflects user diversity. Which action will meet this requirement?
- A. Balance class representation in the dataset.
- B. Use a regional dataset with complete data.
- C. Oversample majority class data.
- D. Drop minority class data records.
Answer: A
Explanation: To ensure an AI model reflects user diversity for a global customer base, the dataset must represent different classes/groups proportionally. Option A (Balancing class representation) helps mitigate bias and ensures the model performs well across diverse user segments. Option B uses only regional data, which contradicts global diversity. Option C (oversampling majority) worsens imbalance, and Option D (dropping minority data) eliminates diversity entirely.
Question 9 · No. 394 · Single choice
A company wants to deploy a secure AI system with controlled access. The system must allow only authorized personnel to access model training data. Which AWS service will meet these requirements?
- A. AWS Key Management System (AWS KMS)
- B. Amazon EMR
- C. AWS Identity and Access Management (IAM)
- D. Amazon Redshift
Answer: C
Explanation: AWS Identity and Access Management (IAM) is the correct service for controlling access to AWS resources. The question specifically asks for controlled access where only authorized personnel can access model training data. IAM allows you to create users, groups, and roles, and attach policies that define permissions, ensuring that only authorized individuals can access specific resources. AWS KMS is for encryption key management, not access control. Amazon EMR is a big data processing service, and Amazon Redshift is a data warehousing service—neither provides access control functionality.
Question 10 · No. 395 · Single choice
Which term is the speed at which a pre-trained foundation model (FM) processes requests and delivers output?
- A. Model size
- B. Inference latency
- C. Context window
- D. Fine-tuning
Answer: B
Explanation: Inference latency refers to the time it takes for a pre-trained foundation model to process an input request and generate/deliver the output. Model size relates to the number of parameters in the model, context window refers to the maximum amount of text the model can handle at once, and fine-tuning is the process of further training a pre-trained model on specific data to adapt it for particular tasks.