Implementing AI Solutions

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 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 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?

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?

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 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 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 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?

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?

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 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?

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?

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 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 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.)

A logistics company is deploying a computer vision model on Azure to detect damaged packages on a conveyor belt. The model runs on Azure IoT Edge devices at each warehouse and must operate during network outages. The team needs to ensure the deployment behaves correctly under intermittent connectivity. (Choose two.)

A logistics company is deploying an AI model that predicts delivery delays. The model is served through an API used by dispatch software. The operations team wants to detect when the model's input data distribution shifts so they can trigger retraining. Which TWO implementation practices best support ongoing detection of data drift in production? (Choose two.)

A media company runs an AI content moderation pipeline that classifies user uploads into allowed, review, and blocked categories. The team notices that the model's blocked decisions have drifted: content that was previously labeled review is now being blocked, and appeals are rising. Which action should the team take FIRST to diagnose the drift?

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

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

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

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

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?

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?

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

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

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 city transit agency wants an AI system to predict bus arrival times. The agency has three years of historical GPS traces, schedule data, and weather records, but no team experienced in building machine learning models. Leadership asks which engagement model will get a working predictor into operations fastest without permanently expanding headcount. Which approach BEST fits?

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 media company is deploying a generative AI assistant to summarize customer support calls. The assistant must produce concise summaries in English, but the call transcripts are in Spanish. The team wants to use a single model that can handle both translation and summarization. Which approach is MOST appropriate?

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?

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?

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?

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 retail bank is rolling out an AI assistant built on Azure AI Foundry to answer customer questions about account policies. The compliance team requires that any response containing financial advice be routed to a human agent and that all interactions be logged for audit. Which combination of capabilities should the developer implement to meet these requirements?

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

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 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?

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

A media company is deploying a generative AI assistant that drafts marketing copy for regional campaigns. Legal requires that no customer personal data, unreleased product names, or internal pricing appear in generated output, and that every draft be attributable to a source. The team plans to use retrieval-augmented generation over an approved content repository. Which TWO controls should be implemented to satisfy these requirements? (Choose 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 hospital is deploying an AI triage assistant that summarizes patient intake notes for emergency department nurses. Before go-live, the clinical informatics team must define a human oversight process that satisfies both safety and regulatory expectations. Which approach is MOST appropriate?

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?

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 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?

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 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 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 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 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 financial services firm is deploying a credit-scoring model built with Amazon SageMaker. Compliance requires that every prediction be explainable to a loan officer and to regulators. The model uses gradient boosting on 120 features. Which approach BEST satisfies the explainability requirement while keeping the production model unchanged?

A retail bank deploys a machine learning model that scores loan applications. Compliance requires that the bank be able to explain to regulators why any individual applicant was denied, in terms of the applicant's own feature values. The model is a gradient-boosted tree ensemble trained on 200 features. Which approach BEST satisfies this requirement?

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 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 small marketing team has built an internal AI assistant that answers questions about their product catalog. They want to deploy it quickly with minimal infrastructure management and pay only for what they use. The team has no Kubernetes expertise and wants the provider to handle scaling, patching, and availability. Which deployment option best matches these constraints?

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?

A retail company wants to forecast weekly demand for thousands of SKUs across stores. The data includes strong seasonal patterns, promotional calendars, and intermittent demand for slow-moving items. The team has limited time and wants a baseline before investing in custom deep learning. Which approach is BEST as the initial production model?

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 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 hospital's radiology AI triage model was validated at 94% sensitivity on a curated research dataset. After six months in production, clinicians report that it misses many positive cases on images from a newly installed scanner. The data science team confirms the model has not been retrained. Which action should the team take FIRST to diagnose and correct the problem?

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 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?

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

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)?

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?

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

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 retail bank is deploying a customer-facing AI assistant that must never disclose internal policy text. The team has a system prompt with instructions, but red-team testing shows users can extract the policy by asking the model to 'repeat everything above this line.' Which implementation change most directly mitigates this prompt-injection extraction risk in production?

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?

A financial services firm is deploying an LLM-based assistant that summarizes internal earnings reports. Compliance requires that the assistant's outputs be auditable and that sensitive financial figures not be sent to an external model provider. Which TWO implementation measures should the team adopt? (Choose two.)

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.)

A logistics company is deploying an AI model that predicts delivery delays. The model will run on edge devices in trucks with intermittent connectivity. The team must ensure the deployment meets latency and reliability requirements. Which TWO implementation practices are MOST appropriate for this edge AI deployment? (Choose two.)

A hospital is deploying an AI triage assistant that suggests priority levels for emergency room patients. Clinicians will review every suggestion before acting. The compliance team requires that the system log who reviewed each suggestion, what the clinician decided, and whether they overrode the AI. Which implementation practice best satisfies this requirement?

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 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 hospital is deploying a vision model that flags possible pneumonia on chest radiographs. Radiologists report that the model performs well overall but frequently flags images from a newly installed portable X-ray unit. The images are technically adequate. The team must diagnose the cause before changing the model. Which action should the team take FIRST?

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?

A hospital's AI triage assistant was validated on data from its own emergency department. Before rolling it out to three affiliated hospitals with different patient demographics, imaging equipment, and documentation habits, the governance committee requires evidence that the model will not silently underperform at the new sites. Which activity BEST provides that evidence?

An organization runs a customer-support LLM that calls internal tools to look up order status and issue refunds. Security testing reveals that a user can paste text into the chat that causes the model to invoke the refund tool with an attacker-controlled amount. The team wants to reduce this prompt-injection risk without removing tool functionality. Which control is MOST effective?

A retail company wants to use AI to personalize marketing emails. They have a large dataset of customer purchase history and demographics. The data science team plans to use a collaborative filtering approach. Which data is MOST critical for this approach?

A financial services firm is implementing an AI solution that scores loan applications. The model must be auditable, and regulators require the firm to explain why any individual application received a particular decision. The data science team trained a gradient-boosted tree model with high accuracy. Which approach best meets the explainability requirement for individual decisions?

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?

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 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?

A media company fine-tunes a large language model on Azure Machine Learning to generate sports recaps. After deployment, the model occasionally emits statistics that were never in the source game data. The team wants a systematic way to reduce these unsupported claims without retraining the base model. Which approach BEST addresses this?

A media company is deploying a generative AI assistant that drafts marketing copy. Legal requires that every generated draft be attributable to source material and that the system must not reproduce copyrighted passages verbatim. The team wants to enforce this at generation time rather than only reviewing outputs afterward. Which implementation approach BEST meets these requirements?

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?

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?

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?

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 healthcare provider is deploying an AI model to predict patient readmission risk. The model was trained on historical data that includes a feature indicating whether the patient has diabetes. The provider wants to ensure the model does not discriminate based on this feature. Which technique should be used to detect and mitigate bias related to the diabetes feature?

A retail company wants its customer support chatbot to answer questions about current promotions that change weekly. The team has an LLM API but does not want to retrain the model each week. Which implementation approach is MOST appropriate?

A media company is deploying an AI service that transcribes customer support calls and then summarizes them for agents. The transcription model runs on-premises and produces text, but the summarization LLM is hosted in a public cloud. Compliance requires that no raw call audio or verbatim transcript ever leaves the company network. Which deployment pattern best satisfies this requirement while still using the cloud LLM?

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

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 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?

A hospital is implementing an AI triage assistant that suggests urgency levels for emergency department patients. The clinical leadership wants to ensure the system does not systematically undertriage patients from a particular demographic group. Which practice best addresses this requirement during implementation?

A company is deploying a large language model (LLM) for internal knowledge management. The model will answer employee questions based on a corpus of confidential documents. The security team requires that the model not leak sensitive information and that responses be accurate. Which TWO techniques should be implemented to meet these requirements? (Choose two.)

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 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?

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 marketing team wants to deploy a generative AI assistant that writes product descriptions. Before launch, they must ensure the assistant does not produce copyrighted text or brand-inappropriate claims. Which implementation step best addresses this requirement at generation time?

A financial services company has deployed a credit-risk model that was trained on historical loan data. Regulatory auditors require that the model's decisions be explainable to applicants who are denied credit. The data science team must integrate an explanation capability into the existing production inference pipeline with minimal latency impact. Which approach BEST satisfies the requirement?

A retail bank has deployed a credit-risk scoring model as a REST endpoint behind an API gateway. The model was trained on data from 2019–2023. Compliance now requires the bank to detect when input feature distributions drift away from the training baseline and to trigger retraining before approval rates degrade. Which approach should the bank implement FIRST?

A media company is deploying an AI system that generates short news summaries from full articles. Before launch, the responsible AI review board asks the team to define monitoring that will detect harmful or degraded behavior in production. Which TWO monitoring practices should the team implement? (Choose two.)

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?

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?

A logistics company has an AI model that predicts delivery delays. The model performs well in offline evaluation, but after deployment the operations team notices that predictions for a specific region are consistently biased low. The region recently changed its address format in the source system. Which action should the team take to resolve the issue?

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?

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

A logistics company is deploying a computer vision model that reads container identification numbers from photos taken at warehouse gates. The model performs well in testing but struggles in production because lighting, camera angles, and container wear vary widely. The team wants to improve robustness before full rollout. Which TWO actions should they take? (Choose two.)

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 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 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?

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

A financial services company is deploying a credit-scoring model built with the AI+ toolkit. The model must produce an explanation for each decision that regulators can review, showing which input features most influenced the score. The data science team has already trained a gradient-boosted tree ensemble. Which approach should the team use to satisfy the regulatory requirement?

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?

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?

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 financial services firm runs an AI model that scores loan applications. Regulators require the firm to explain any adverse decision to an applicant. The model is a gradient-boosted tree with hundreds of features. Which implementation approach best satisfies the explainability requirement without replacing the model?

A hospital's AI triage assistant summarizes patient notes for clinicians. During post-deployment monitoring, the team notices the model's outputs drift in tone and length after the vendor silently updated the underlying foundation model. The application code did not change. Which action best restores reproducibility and protects against future silent model changes?

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 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?

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 logistics company runs a vision model on edge devices in warehouses to detect damaged packages on conveyor belts. The model must classify each package within 40 milliseconds, and network connectivity to the cloud is unreliable. During a pilot, engineers notice that accuracy on the edge devices is several points lower than the accuracy measured during cloud-based evaluation on the same test images. Which cause is MOST likely?

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 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?