Data engineering, warehousing, business intelligence and analytics platform certifications.
Data certifications track the shift from batch warehousing to streaming lakehouse architectures. Expect questions on ingestion patterns, partitioning, incremental processing, governance and the cost characteristics of storage and compute separation.
Analyst-oriented exams cover modelling, semantic layers and visualisation, while engineer-oriented exams focus on pipelines, orchestration and reliability.
Core data concepts on Azure: relational and non-relational stores, the difference between transactional and analytical workloads, and the shape of a modern analytics pipeline.
Open the exam guideFilter by practice availability, provider, level or exam code to narrow the list.
21 exams
20 with practice · 1 guide only
The Power BI analyst exam: connecting and cleaning data, building a performant model, writing DAX, designing reports, and managing workspaces and row-level security.
Designing and operating data pipelines on AWS: ingestion and transformation, storage selection, orchestration, data quality, and governance across the analytics stack.
The MLOps-oriented associate exam: preparing data for training, developing and tuning models, deploying inference workloads, and monitoring them once they are live.
Designing and operating data systems on Google Cloud: processing system design, ingestion and transformation, storage selection, analysis enablement and workload automation.
Core data concepts on Azure: relational and non-relational stores, the difference between transactional and analytical workloads, and the shape of a modern analytics pipeline.
Data engineering on Microsoft Fabric: workspace and lifecycle management, ingesting and transforming batch and streaming data, and monitoring and optimising the result.
Production machine learning on Google Cloud: framing problems, building and scaling models on Vertex AI, serving them reliably, and automating and monitoring the pipeline.
The Fabric analytics engineer exam: maintaining an analytics solution, preparing data across lakehouse and warehouse workloads, and building semantic models that perform.
The foundation of the SnowPro programme: Snowflake architecture, account access and security, performance and cost management, data loading and unloading, and data transformation.
The long-running specialty ML exam, weighted heavily toward modelling: algorithm selection, feature engineering, evaluation metrics and production ML operations on AWS.
The associate data engineering exam: the lakehouse platform, ELT with Spark SQL and Python, incremental processing, production pipelines, and data governance with Unity Catalog.
Apache Spark development fundamentals: the DataFrame API, transformations and actions, Spark architecture and execution, and troubleshooting performance in distributed jobs.
An associate-level data exam covering ingestion and preparation, analysis and presentation, pipeline orchestration, and the governance side of managing data on Google Cloud.
Implementing Salesforce Data Cloud: ingesting and modelling customer data, identity resolution, segmentation and activation, and the governance that keeps it trustworthy.
Professional data engineering on Databricks: platform internals, advanced data processing and modelling, security and governance, monitoring and logging, and testing and deployment.
Advanced Snowflake data engineering: ingestion and transformation at scale, Snowpark development, pipeline orchestration with streams and tasks, and performance and security in pipelines.
Machine learning on Databricks at associate level: the ML platform and MLflow, data preparation and feature engineering, model development, and deployment and lifecycle management.
Architecting on Snowflake: account and data architecture design, security and governance at scale, performance optimisation, and designing data sharing and application patterns.
Analytics on the lakehouse: Databricks SQL, data management and ingestion for analysts, SQL for analysis, dashboards and visualisation, and analytics applications in practice.
An analyst-focused, vendor-neutral data certification: data concepts and environments, mining, analysis, visualisation, and the governance and quality controls around them.
Advanced analytics on Snowflake: domain and data modelling for analysis, complex SQL and window functions, visualisation and reporting integration, and analytics performance tuning.
How many exams each provider contributes here.
Where each exam sits, so you can plan a progression rather than a single jump.
22 exams from 7 vendors are tagged to Data & Analytics. Exams appear in this category when it is either their primary focus or a substantial secondary one.
Microsoft Power BI Data Analyst (PL-300) is the most common entry point. The Power BI analyst exam: connecting and cleaning data, building a performant model, writing DAX, designing reports, and managing workspaces and row-level security.
Microsoft Power BI Data Analyst (PL-300), AWS Certified Data Engineer – Associate (DEA-C01) and AWS Certified Machine Learning Engineer – Associate (MLA-C01) are the most frequently pursued in this category.
AI & Machine Learning
Exams covering generative AI, model deployment, MLOps and responsible AI practice.
Databases
Relational and cloud database administration, SQL, performance and high availability exams.
Cloud Computing
Certifications covering public cloud platforms, architecture, migration and cost management.