Microsoft Certified: Azure Data Fundamentals Advanced Practice Exam: Hard Questions 2025

You are designing a data platform for an e-commerce company. The business asks for a single metric, Daily Active Users (DAU), that is computed consistently across marketing dashboards, finance reports, and an ML feature store. Data originates from multiple systems and arrives late (up to 48 hours). The company has repeatedly had issues with the same metric being calculated differently by different teams. Which concept best addresses this problem end-to-end?

A healthcare app stores patient profiles and visit records. Regulatory audits require proving where each reported value originated, including transformations. Multiple teams transform data in different pipelines before reporting. You need to reduce audit time and ensure trust in reported datasets. Which core data concept is MOST directly required?

A retail company is migrating an on-premises OLTP database to Azure. The workload requires: strict ACID transactions, complex joins, referential integrity, and the ability to enforce business rules using constraints. It also must support read scaling for reporting without affecting write performance. Which Azure data service is the BEST fit?

A team reports frequent deadlocks and inconsistent query performance after migrating to a relational PaaS on Azure. Investigation shows a dashboard tool runs long-running read queries directly against the OLTP database during business hours. You need to recommend a design change that minimizes contention while keeping data close to real-time. What should you recommend?

You are designing a relational data model for a multi-tenant SaaS application. Tenants can be added/removed frequently. The app needs to enforce tenant-level data isolation, support transactional updates, and allow efficient queries by tenant and time. Which approach is MOST appropriate as a foundational relational design consideration on Azure?

An IoT solution ingests millions of device telemetry events per minute. Each event is semi-structured and new fields appear frequently. The application must support low-latency reads of the latest device state and allow querying by deviceId and timestamp. The team wants minimal schema management overhead and global distribution. Which Azure data store is the BEST fit?

A data engineering team stores raw clickstream logs as files in Azure. Data must be retained cheaply, support hierarchical folders, POSIX-like ACLs, and be optimized for big data analytics engines. The team will later curate data into structured formats but wants to keep immutable raw files. Which storage option should you recommend?

Your company needs to model complex relationships among entities (customers, devices, locations, and connections) and run queries like “find all devices within 3 hops of a compromised device” with low latency. The current approach uses multiple self-joins in a relational database and performs poorly. Which non-relational data model is MOST appropriate?

A finance organization has a curated star schema used for enterprise reporting. They require massively parallel query execution over large fact tables and want to separate compute from storage to scale resources for heavy month-end reporting. Which Azure analytics component best matches this requirement?

A manufacturing company needs near-real-time dashboards showing production anomalies. Data arrives continuously from sensors. The solution must detect anomalies within seconds and also store raw events for later batch analytics and model training. Which approach best fits an Azure analytics workload pattern?