Azure DP-900 study plan
Azure DP-900 · Give each week a clear focus.
8 weeks · About 6 hours per week
Describe core data concepts
Define structured, semi-structured, and unstructured data and give two concrete examples of each from your own experience
Distinguish OLTP from OLAP workloads by listing three contrasting characteristics of each
Classify at least six common file formats (CSV, JSON, XML, Parquet, Avro, ORC, plain text) as structured or semi-structured and note which are row-based vs column-based
Describe the day-to-day responsibilities of a data engineer, database administrator, data analyst, and data scientist, and state which role owns each of ten sample tasks
Your checkpoint
A one-page handwritten or typed summary sheet of Domain 1 terminology, plus a closed-book self-quiz of 25 questions scored at 80% or higher
Describe core data concepts & Describe an analytics workload on Azure
Explain batch processing versus stream processing and identify which one fits each of five described business scenarios
Define core analytics concepts: data ingestion, data storage, data processing, data visualization, and the difference between a data lake and a data warehouse
Describe the purpose of Azure Data Factory, Azure Synapse Analytics, Azure Databricks, Azure Stream Analytics, and Power BI in one sentence each
Trace a described end-to-end analytics pipeline and label where each Azure service fits
Your checkpoint
A hand-drawn or text-based architecture diagram of one batch pipeline and one streaming pipeline, with every service named and its role annotated
Describe an analytics workload on Azure
Compare Azure Synapse Analytics, Azure Databricks, and Azure Data Factory by listing the primary job each performs and one case where each is the better choice
Differentiate the Synapse dedicated SQL pool, serverless SQL pool, and Spark pool in terms of when you would use each
Describe how Azure Data Lake Storage Gen2 organizes data using containers, directories, and files, and explain the hierarchical namespace benefit
Explain the difference between Azure Event Hubs and Azure Stream Analytics in a streaming workload
Your checkpoint
A four-row comparison table covering ingestion, storage, processing, and visualization services, with at least two selection rules written beneath it
Describe an analytics workload on Azure & Describe core data concepts
Describe how Power BI connects to a data source, models data, and produces a report, including the difference between a dataset and a report
Explain data visualization choices by matching chart types to described scenarios (trend over time, part-to-whole, comparison across categories)
Re-state the five analytics stages from memory and map each to at least one Azure service
Identify the correct Azure analytics service for ten described scenarios under timed conditions
Your checkpoint
A timed 30-question practice set covering Domains 1 and 4, scored at 80% or higher, with every missed question rewritten as a corrected statement
Identify considerations for relational data on Azure
Define the relational model: table, row, column, primary key, foreign key, and candidate key, using a small example schema you design yourself
Write and correctly run SELECT, WHERE, ORDER BY, INSERT, UPDATE, and DELETE statements against a sample relational database
Normalize a described unnormalized table to third normal form and explain each step
Compare Azure SQL Database, Azure SQL Managed Instance, and SQL Server on Azure virtual machines by deployment model, control level, and typical use case
Explain how Azure Database for MySQL and Azure Database for PostgreSQL differ from Azure SQL Database in supported engines and migration fit
Your checkpoint
A working T-SQL script file containing at least eight statements (including a join and an aggregate) that executes without errors, plus a results note
Identify considerations for relational data on Azure & Describe considerations for working with non-relational data on Azure
Explain the role of indexes, views, stored procedures, and transactions in a relational database and state one benefit of each
Describe Azure SQL security basics: authentication versus authorization, firewall rules, and role-based access
Define the main non-relational data models: key-value, document, graph, and column-family, with one realistic use case each
Compare Azure Blob Storage, Azure Table Storage, Azure Cosmos DB, and Azure Files by data model and primary scenario
Your checkpoint
A two-column contrast sheet (relational vs non-relational) listing at least ten decision criteria, plus a 15-question self-quiz scored at 80% or higher
Describe considerations for working with non-relational data on Azure & Identify considerations for relational data on Azure
Describe Azure Cosmos DB capabilities: multiple APIs, global distribution, and tunable consistency levels, and match each consistency level to a described scenario
Distinguish Blob Storage access tiers (hot, cool, cold, archive) and select the right tier for four described workloads
Classify Blob Storage content types as block blobs, append blobs, or page blobs for described scenarios
Explain provisioned throughput versus serverless in Cosmos DB and state when each is appropriate
Re-verify the ten weak points you recorded in Weeks 5 and 6 by answering them cold without notes
Your checkpoint
A 20-question mixed practice set covering Domains 2 and 3 scored at 85% or higher, plus a fixed list of remaining weak areas
Describe core data concepts & Identify considerations for relational data on Azure & Describe considerations for working with non-relational data on Azure & Describe an analytics workload on Azure
Complete two full-length timed practice exams under exam conditions and record the score per domain
Rewrite every missed item as a corrected factual statement and re-quiz yourself on those statements
Recite the four domains and their weightings from memory and confirm your study time matched the weightings
Review the two lowest-scoring domains for 60 minutes each and re-test only those domains
Your checkpoint
A score log showing two full practice exam results with per-domain breakdown, both at or above the passing threshold, and a final one-page cue sheet of facts you most often missed
Milestones
Week 2: You can explain, without notes, the difference between batch and streaming processing and name the Azure service used for each; Domain 1 self-quiz at 80% or higher
Week 4: You can name and correctly place all five analytics stages in a pipeline and select the right Azure service for ten described scenarios; combined Domain 1 and 4 practice score at 80% or higher
Week 6: You have written and successfully executed a multi-statement T-SQL script and can contrast relational versus non-relational storage across ten criteria
Week 8: Two full timed practice exams completed with per-domain scores at or above the passing threshold and no domain below 70%