Examworthyexamworthy.comMicrosoft Azure Data Fundamentals (DP-900) cheat sheet
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At a glance
Format: Multiple choice and multiple response, at a Pearson VUE testing center or online proctored
Domain weight map
Heaviest first - spend your time hereHow this exam thinks
DP-900 is a vocabulary-and-service-map exam: nearly every question is a definition or a short scenario that resolves to naming the correct data concept or the Azure service Microsoft positions for that workload.
Spot the trap
Tempting wrong answers, and why they failTempting but wrong
Customer profiles stored as JSON documents whose fields vary between records are unstructured data.
Why it fails
JSON with varying fields is the classic example of semi-structured data, which has some structure while allowing variation between instances. Unstructured data has no structure at all, so JSON documents with recognisable fields do not qualify.
Describe Core Data Concepts
Tempting but wrong
In a star schema, the central fact table holds the descriptive entities such as customer, product, and store used to group and slice the data.
Why it fails
Those descriptive entities are held by the surrounding dimension tables. The central fact table holds the numeric values that can be aggregated by the related dimensions.
Describe an Analytics Workload on Azure
Tempting but wrong
In a relational database, a single customer is represented as one column in the customer table.
Why it fails
A column stores one attribute (such as Email) across many entity instances, not a single instance. A single customer is one row in the table that models that entity.
Identify Considerations for Relational Data on Azure
Tempting but wrong
Azure Files is the right service for storing large numbers of images, video, and backup archives that applications read and write through a cloud storage API.
Why it fails
Azure Files provides cloud network file shares mounted over SMB or NFS, not an object store accessed through a Blob API. Azure Blob storage is the object store designed for unstructured data like images, video, and archives, read and written through the Blob storage API.
Describe Considerations for Working with Non-Relational Data on Azure
Tempting but wrong
A graph database stores structured data in normalised tables that reference each other through primary keys.
Why it fails
A graph database stores entities as nodes with links that define their relationships, not as normalised tables. It is the relational database that uses tables and primary-key references to store each value only once.
Describe Core Data Concepts
Tempting but wrong
The fact table at the centre of a star schema stores the raw files exactly as received before any schema is applied.
Why it fails
Storing raw files with no schema describes a data lake, not a fact table. A star schema fact table holds the numeric values that are aggregated by its related dimension tables.
Describe an Analytics Workload on Azure
Tempting but wrong
Each individual customer gets its own separate table created just for that one instance.
Why it fails
A table models a whole collection of entities; one instance is a row inside it, not its own table. One customer occupies one row in the shared customer table.
Identify Considerations for Relational Data on Azure
Tempting but wrong
Azure Table storage is a good fit for holding arbitrary binary objects such as images and video files.
Why it fails
Table storage holds semi-structured key/value rows, not arbitrary binary objects. Images and video belong in Azure Blob storage, which stores them as binary large objects accessed through the Blob storage API.
Describe Considerations for Working with Non-Relational Data on Azure
Key terms
Exam-day rules
- Read each scenario for the distinctive feature. The one detail that stands out, such as unstructured binary data, global distribution, or automated patching, is what selects the service, so find it before judging the options.
- Tell the storage services apart by data shape. Unstructured objects mean Blob, a mountable share means Files, key-value rows mean Table, and asynchronous messages mean Queue; do not default to the one you have heard of most.
- Use the management model to pick the Azure SQL option. If you patch the OS and engine yourself it is SQL on a VM; if Microsoft does it for you it is Azure SQL Database or Managed Instance.
- Keep the star schema pair straight. Numbers and measures live in the fact table, descriptive entities live in the dimension tables, and a fact joined directly to its dimensions is a star schema.
- Separate the Power BI artefacts by purpose. A report is the interactive analysis, a dashboard is the pinned single-page summary, and a paginated report is the pixel-perfect printable layout.
Revision schedule
- Day 1Map the four domains and book a date
- Week 1Lock the core vocabulary
- Week 2Drill the relational concepts and Azure SQL ladder
- Week 3Build the non-relational storage decision tree
- Week 4Cover the analytics stack and Power BI