Implementing AI Solutions

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?

A data science team is preparing a dataset for a binary classification model. The dataset has 95% negative class and 5% positive class. Which technique should they apply to avoid biased model predictions?

In the AI project lifecycle, which phase involves partitioning the dataset into training, validation, and test sets?

A developer is implementing a RAG system and needs to chunk large legal documents. The documents contain nested clauses and cross-references that should not be split across chunks. Which chunking strategy is MOST suitable?

A team is evaluating a fine-tuned LLM for a code generation task. They notice the model rarely generates correct syntax but often produces plausible-looking code. Which evaluation metric is MOST appropriate to quantify this issue?

An AI system uses a pre-trained image classification model to detect defects in manufacturing. The team wants to deploy the model in an edge device with limited GPU memory. Which technique should they consider first?

A team is deploying a multi-modal AI model that processes both text and images. They need to ensure that inference requests are handled quickly even during traffic spikes. Which integration pattern is BEST suited for this use case?

In prompt engineering, which technique involves providing a few correct input-output examples in the prompt to guide the model's response?

A recommendation system for an e-commerce site is producing stale suggestions that do not reflect recent user behavior. The system is updated offline every 24 hours. Which change would MOST directly address this issue?

During testing a chatbot, the QA team observes that the bot sometimes responds with harmful content when given adversarial prompts. Which type of testing should be prioritised to catch these edge cases?

A company is fine-tuning an LLM for a domain-specific task using LoRA. They have limited GPU memory and need to reduce memory footprint without sacrificing fine-tuning quality. Which approach should they consider?

Which similarity measure is commonly used in vector search to find the angle between vectors, making it well-suited for high-dimensional embeddings?

A team is designing an AI agent that needs to interact with external APIs, search the web, and perform multi-step reasoning. Which TWO architectural components are essential for this agentic workflow? (Choose TWO.)

An organisation is developing a document intelligence system that extracts information from scanned invoices. Which THREE data preparation steps are critical to ensure high extraction accuracy? (Choose THREE.)

A machine learning engineer is deploying a production model that requires strict monitoring. Which TWO monitoring strategies should be implemented to detect data drift and model degradation? (Choose TWO.)

A company is building an AI-powered document intelligence system to extract key fields from scanned invoices. The data contains 95% of invoices from one vendor and 5% from others. During model training, the F1 score is 0.95 on the overall test set, but the performance on the minority vendor invoices is very poor. What is the MOST likely cause?

A data scientist is preparing a dataset for a classification model. The dataset has missing values in several features and features with very different scales. Which two data preparation steps should be applied?

A team is implementing a RAG system for legal document retrieval. The documents are long (50-100 pages) with clear section headings. They want to ensure that retrieved chunks are semantically coherent and respect document structure. Which chunking strategy is MOST appropriate?

A developer is building an AI agent that needs to call external APIs to complete user requests. The agent must decide which API to call based on the user's natural language input. Which technique should the developer use to enable the agent to invoke APIs?

A machine learning engineer is deploying a real-time anomaly detection system for manufacturing sensor data. The system must process thousands of readings per second with minimal latency. Which deployment architecture is BEST suited?

A data scientist is building a binary classification model to predict customer churn. The dataset has 90% non-churn and 10% churn. After training, the model achieves 90% accuracy, but the recall for the churn class is only 20%. Which metric should the team primarily focus on to evaluate the model's effectiveness?

A developer is fine-tuning a large language model for a code generation task. The available GPU has only 8GB of VRAM, and the base model is 7B parameters. Which fine-tuning technique is MOST feasible?

A company has an existing AI chatbot that uses a fine-tuned LLM to answer customer queries. They want to add the ability to retrieve real-time order status from their database. Which integration pattern should they use?

A data science team is preparing a dataset for a supervised learning task. They split the data into training and test sets. The team then normalizes the features using the mean and standard deviation calculated from the entire dataset before splitting. What issue does this introduce?

A developer is building a RAG system and needs to choose a similarity metric for retrieving document chunks. The embedding model they use produces normalized vectors (unit vectors). Which similarity metric is equivalent to cosine similarity in this case?

An organization wants to implement an AI system to automatically categorize support tickets into predefined categories. They have a labeled dataset of 10,000 tickets. Which approach is MOST appropriate?

A team is developing an AI agent to assist users with multi-step tasks such as booking a flight, reserving a hotel, and scheduling a car rental. The agent needs to reason about the order of steps and handle dependencies. Which pattern is BEST suited?

A data scientist is preparing a dataset for a regression model. The dataset contains 100 features, some of which are highly correlated. To improve model performance and reduce overfitting, which TWO techniques should the data scientist apply? (Select TWO)

A company is deploying a generative AI application that produces structured JSON output for downstream processing. They want to ensure the output is consistently valid JSON and matches a specific schema. Which THREE techniques should they use? (Select THREE)

A team is building an AI-powered recommendation system for an e-commerce platform. They want to test the system before deployment. Which TWO types of testing are MOST relevant for this AI system? (Select TWO)

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?

A data scientist is preparing a dataset for a binary classification model to detect fraudulent transactions. The dataset contains 1% fraudulent and 99% legitimate transactions. The goal is to maximize recall for the fraud class while maintaining a precision above 0.5. Which data preparation strategy is MOST effective?

A team is implementing a document intelligence solution to extract key-value pairs from invoices. They plan to use a pre-trained vision-language model with a RAG pipeline that indexes invoice images. Which chunking strategy is BEST suited for invoice documents that have a consistent layout but vary in length?

Which similarity metric is MOST appropriate for comparing dense vector embeddings in a vector store used for document retrieval, when the embeddings are normalized to unit length?

A developer is integrating an AI microservice that accepts image uploads and returns classification labels. The service must handle spikes of up to 1,000 requests per minute but average 100 requests per minute. Which deployment architecture BEST meets these requirements with cost efficiency?

A team fine-tunes a 7B parameter LLM using LoRA on a custom instruction dataset. After training, they observe that the model's outputs are only marginally different from the base model. Which is the MOST likely cause?

A chatbot application uses a system prompt to set the assistant's behavior. The developer wants the LLM to output structured JSON for downstream processing. Which technique BEST ensures the output is valid JSON?

In the AI project lifecycle, which phase involves splitting the dataset into training, validation, and test sets while ensuring no data leakage?

An AI system for detecting anomalies in manufacturing sensor data uses a model trained on normal operation data only. During monitoring, the model flags many false positives. Which adjustment is MOST likely to reduce false positives?

A company is deploying a code generation AI assistant for internal developers. They want to ensure the assistant does not generate code with security vulnerabilities. Which testing approach is MOST critical?

Which component in a RAG system is responsible for converting document chunks into numerical representations that enable similarity search?

A team is building a recommendation system for an e-commerce platform. They want to use collaborative filtering but have a cold-start problem for new users. Which hybrid approach BEST addresses cold start while leveraging collaborative signals?

A data scientist is fine-tuning a large language model for a domain-specific task using QLoRA. Which TWO statements correctly describe QLoRA's advantages?

A team is developing an AI agent that can answer questions by querying a SQL database and a REST API. The agent should decide which tool to call, parse the response, and reason about the next step. Which THREE concepts should be implemented to build this agent?

An organization is deploying an image classification model to detect defects on a production line. Which TWO steps are essential during the model monitoring phase of the AI project lifecycle?

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?

A data scientist is preparing a dataset for a binary classification model to detect fraudulent transactions. The dataset has 1% fraud cases (minority class) and 99% non-fraud cases. Which data preparation technique is MOST appropriate to address the class imbalance before training?

An AI team is deploying a fine-tuned LLM for a code generation assistant. They need to ensure the model outputs only syntactically valid JSON for integration with downstream systems. Which prompt engineering technique is MOST effective for enforcing structured output?

A company is building a document intelligence system that extracts key fields from scanned invoices. They have a labeled dataset of 10,000 invoices but need to decide between a traditional OCR+rule-based pipeline and an AI-based model. Which use case characteristic STRONGLY favors the AI-based approach?

During the evaluation phase of an AI project, the team measures the model's F1 score on a held-out test set. They find the F1 score is 0.92, but when deployed in production, the model performs poorly on new data. What is the MOST likely cause of this discrepancy?

A team is implementing a RAG system for a large legal document repository. They need to chunk the documents for efficient retrieval. The documents contain long sections with subsections, and the team wants to preserve the hierarchical structure. Which chunking strategy is MOST appropriate?

A machine learning engineer is building a recommendation system for an e-commerce platform. The system should suggest products based on user purchase history and browsing behavior. Which model selection is BEST suited for this task?

A startup wants to add an AI-powered virtual assistant to their mobile app. They have limited in-house AI expertise and need a solution that can be integrated quickly with minimal infrastructure management. Which deployment pattern is MOST suitable?

During testing of an AI system that classifies support tickets into categories, the team notices the model frequently misclassifies tickets about a new product feature that was introduced after the model was trained. Which type of testing should the team prioritize to catch this issue?

A data science team is fine-tuning a large language model for a domain-specific task using LoRA. They have a limited GPU budget and want to minimize memory usage during training. Which technique should they use?

An AI developer is building an agent that can book flights and hotels by calling external APIs. The agent needs to decide which API to call and in what order based on user requests. Which pattern is BEST suited for this multi-step reasoning and tool use?

A team is deploying an anomaly detection system for real-time monitoring of server metrics. The system should alert when metrics deviate significantly from normal patterns. Which type of AI model is MOST suitable?

A company is building a RAG-based Q&A system for a large collection of technical manuals. They need to choose an embedding model and a similarity search method. Which TWO choices are most appropriate for this scenario? (Select TWO)

A team is deploying a fine-tuned LLM for generating code snippets. They want to test the system thoroughly before production. Which THREE testing types should they include in their test plan? (Select THREE)

A data scientist is preparing a dataset for training a customer churn prediction model. To prevent train/test leakage, which TWO practices should be followed? (Select TWO)

A data scientist is preparing a dataset for a binary classification model. The dataset has 95% majority class and 5% minority class. Which data preparation technique is BEST to address the class imbalance?

A team is building a recommendation system for an e-commerce platform. They need to update recommendations in real-time as users browse. Which integration pattern is MOST suitable?

During testing of a customer service chatbot, the team notices that the model sometimes generates plausible-sounding but factually incorrect answers about company policies. Which evaluation approach is BEST to systematically detect and quantify this issue?

A team is implementing a RAG system for legal document retrieval. The documents are long and cover multiple topics. Which chunking strategy is MOST appropriate to ensure each chunk contains coherent information?

An AI practitioner is fine-tuning a large language model for a domain-specific task using a small labeled dataset (500 examples). They have limited GPU memory. Which technique is MOST suitable?

A developer is building an AI agent that needs to call external APIs (e.g., get weather, send email) based on user requests. Which pattern is BEST for enabling the agent to autonomously decide when to call these APIs?

A team is training a image classification model. They split the dataset into training, validation, and test sets. After training, the model achieves 98% accuracy on the training set but only 72% on the test set. Which step in the AI project lifecycle should the team focus on?

Which similarity search metric is BEST for comparing dense vector embeddings when the magnitude of the vectors is not important, only the direction?

A company wants to build a code generation tool that helps developers write Python functions. The tool must generate syntactically correct code. Which prompt engineering technique is MOST effective?

An AI system performs anomaly detection on sensor data in a manufacturing plant. The model is deployed and running well. After two months, the plant installs new sensors that produce data with a different distribution. The anomaly detection starts failing with many false positives. Which action should the team take?

A team is building a document intelligence application that extracts key fields from invoices. They have 10,000 labeled invoices. What is the first step in the AI project lifecycle?

A team is considering whether to fine-tune a base LLM or use RAG for a question-answering system over a large, static corpus of scientific papers. The answer must be highly accurate and grounded in the papers. Which approach is BEST and why?

A team is implementing a RAG system. They are designing the document loading and chunking strategy. Which TWO techniques are commonly used for chunking documents? (Select two.)

A company is deploying an LLM-based chatbot that must output responses in a structured JSON format for downstream processing. Which THREE prompt engineering techniques should the team use to ensure the output is valid and correctly structured? (Select three.)

An AI team is evaluating whether to use AI for a customer segmentation task. They have a dataset of customer demographics and purchase history. Which TWO conditions would make AI a better choice than a traditional rule-based approach? (Select two.)

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?

During data preparation for a classification model, the data scientist notices that one class has 95% of the samples and the other has only 5%. Which technique is MOST appropriate to address this imbalance?

A team is deploying a fine-tuned LLM for code generation. They need to ensure the model output is always valid JSON. Which prompt engineering technique should they use?

A data science team is building a model to detect fraudulent transactions. They have a dataset of 1 million normal transactions and 1,000 fraudulent ones. What is the MOST effective data preparation step to handle this imbalance?

Which stage of the AI project lifecycle involves splitting data into training, validation, and test sets?

A developer is implementing a RAG system and needs to choose a similarity metric for retrieving document chunks. The embedding model produces normalized vectors. Which metric is computationally efficient and equivalent to cosine similarity for normalized vectors?

A team is fine-tuning a large language model using LoRA. They have limited GPU memory. Which technique can further reduce memory consumption while maintaining similar fine-tuning quality?

An AI agent is designed to book flights by calling an external API. The agent must decide which tool to call based on user input, then generate the correct API parameters. Which pattern is MOST appropriate for this workflow?

Which embedding type is MOST suitable for capturing semantic meaning of text in a RAG pipeline?

A company is building a recommendation system for an e-commerce site. They have historical user-item interaction data. Which approach is most appropriate?

A data scientist is training a binary classifier and observes that the training accuracy is 99% but the test accuracy is only 70%. Which of the following is the MOST likely cause?

Which chunking strategy for RAG is MOST appropriate when documents have a natural hierarchical structure (e.g., sections, subsections)?

A team is deploying an AI microservice for real-time object detection in streaming video. Which TWO integration patterns are most appropriate? (Choose two.)

An organization wants to fine-tune a 7B parameter LLM for a specialized legal document summarization task. They have a small labeled dataset (500 examples) and limited GPU budget. Which THREE techniques should they consider? (Choose three.)

A team is designing a RAG system for a large collection of PDFs. They need to choose document chunking strategies. Which TWO strategies are considered best practices? (Choose two.)

A company wants to build a conversational agent that can handle complex multi-step tasks such as booking a flight, reserving a hotel, and scheduling a car rental in a single session. The agent must be able to break down the user's request into sub-tasks, call external APIs, and reason about the results. Which design pattern is BEST suited for this requirement?

During the data preparation phase of an AI project, a data scientist discovers that the target variable in a binary classification dataset is heavily imbalanced: 95% negative class and 5% positive class. Which technique should be applied to improve model performance on the minority class?

A developer is implementing a RAG system for legal document review. The documents are long (50-100 pages) with dense sections. They need to chunk the documents in a way that preserves semantic coherence while keeping chunks small enough for effective retrieval. Which chunking strategy is MOST appropriate?

An AI team is deploying a large language model for a customer-facing application. They need to ensure that the model's output is always in valid JSON format for downstream processing. Which prompt engineering technique should they use?

A data science team is training an image classification model for a medical imaging application. To prevent data leakage, they must partition the dataset correctly. Which approach ensures that no patient images appear in both training and test sets?

A company is fine-tuning a large language model using PEFT (Parameter-Efficient Fine-Tuning) to reduce GPU memory usage. They have limited hardware and need to fine-tune a 70B parameter model on a single GPU with 24 GB VRAM. Which technique is MOST suitable?

In the AI project lifecycle, after a model is trained and evaluated, it is deployed to a production environment. What is the NEXT critical step to ensure the model continues to perform well over time?

A developer is building an AI microservice that processes document intelligence requests asynchronously. Users upload PDFs, and the service extracts text and analyzes it with an LLM. The processing time per document can be up to 5 minutes. Which integration pattern is MOST appropriate?

A team is evaluating an LLM-based code generation assistant. They want to measure the quality of generated code for correctness, security, and efficiency. Which evaluation framework is BEST suited for this task?

When implementing a vector store for a RAG system, which similarity search metric is MOST commonly used to find the most relevant document chunks for a given query embedding?

A company is deciding between fine-tuning and RAG for a domain-specific legal assistant that must provide accurate answers based on a changing set of statutes and regulations. The statutes are updated quarterly. Which approach is PREFERRED and why?

A recommendation system for an e-commerce platform is experiencing a high false positive rate in its anomaly detection module, causing legitimate transactions to be flagged as fraudulent. The team wants to reduce false positives without significantly increasing false negatives. Which action is MOST effective?

A team is setting up a test suite for an AI system that includes a data pipeline, an LLM API call, and an output evaluation step. Which TWO types of tests should they prioritize to ensure the system's reliability?

A data scientist is preparing a dataset for a text classification model. To prevent train/test leakage, which THREE practices should they follow?

A developer is building an AI agent that needs to call external tools (e.g., weather API, database) and reason about the results to answer user queries. Which THREE components are essential for implementing this agentic workflow?

A data science team is building a binary classifier to detect fraudulent transactions. The dataset has only 2% fraud cases. Which data preparation technique is MOST critical to address this imbalance?

An AI system must extract text from scanned invoices and output structured fields (invoice number, date, total amount). Which type of AI application is this?

A team is deploying a generative AI model for a real-time customer-facing application. They need to balance cost and latency. Which deployment strategy is MOST suitable?

A team is evaluating an LLM-based chatbot that frequently hallucinates when answering questions about internal policies. Which testing approach would MOST effectively quantify this issue?

An AI engineer needs to select a similarity measure for comparing dense embedding vectors in a vector store for document retrieval. Which two measures are commonly used?

A company wants to fine-tune a 70B-parameter LLM for a specialized domain but has limited GPU memory (e.g., 24 GB VRAM). Which technique allows fine-tuning with minimal memory footprint?

A team is building a RAG system with a large repository of technical manuals. They want to ensure that each retrieved chunk is semantically coherent and that related concepts are grouped together. Which chunking strategy is BEST?

An AI application needs to generate structured JSON output from an LLM. The development team wants to ensure the output always conforms to a specific schema. Which prompt engineering technique is MOST suitable?

A data scientist notices that a model's performance on the training set is excellent, but validation accuracy is poor. The team used the same dataset for feature engineering and model selection. What is the MOST likely cause?

A company wants to build a system that automatically tags uploaded images with objects they contain (e.g., 'car', 'tree', 'person'). Which AI application type is this?

A data science team is developing a churn prediction model. Which TWO data preparation best practices are MOST important to prevent overfitting and ensure generalization?

An organization is deploying an AI agent that uses the ReAct pattern to answer customer queries by calling external APIs. Which THREE components are essential in this agentic workflow?

A company is choosing between fine-tuning and RAG for a legal document assistant. Which TWO factors would MOST strongly favor RAG over fine-tuning?

A team is designing an AI microservice for image classification. Which THREE practices should they implement for effective integration and testing?

An AI engineer is selecting a PEFT technique to fine-tune a large language model. Which TWO are examples of PEFT (Parameter-Efficient Fine-Tuning)?

A data science team is preparing a dataset for a binary classification model to detect fraudulent transactions. The dataset has 99% legitimate and 1% fraudulent examples. Which TWO techniques should the team apply to improve model performance on the minority class?

A company is building a code generation assistant for internal developers. They want the assistant to generate code snippets consistent with the company's coding style and use private libraries. They have a few thousand examples of internal code. Which THREE considerations are critical when deciding between fine-tuning a base LLM and using RAG?

An AI team is deploying a real-time document intelligence service that extracts key-value pairs from invoices. The pipeline includes an LLM that calls a function to parse structured output. Which TWO testing strategies are essential before production deployment?

A team is implementing a RAG system for a legal document Q&A. They need to chunk documents effectively. Which THREE chunking strategies should they consider to improve retrieval accuracy for legal texts that contain hierarchical sections (clauses, sub-clauses, definitions)?

A company wants to use AI to automatically detect anomalies in server log data. The data is time-series and labeled with 'normal' and 'anomaly' for the past year. Which TWO techniques are appropriate for this use case?