Exam Prep – Databricks Certified Data Analyst Associate
(1 day)
Course Description
This course prepares learners for the Databricks Certified Data Analyst Associate exam using the updated September 2025 exam guide. The approach is question-driven: each module is centered on realistic exam-style questions aligned to the official domains. Instructors walk through every option — why it is correct or incorrect — and blend in concise teaching to reinforce Databricks SQL, ingestion, governance, visualization, and analytics concepts.
Learning Objectives
By the end of this course, learners will be able to:
- Confidently answer questions across all exam domains in the new 2025 guide.
- Understand the reasoning behind correct and incorrect answers.
- Apply Databricks SQL and data analysis concepts to ingestion, dashboards, governance, and analytics workflows.
- Strengthen readiness for both the exam and real-world data analysis tasks.
Audience
- Candidates preparing for the Databricks Certified Data Analyst Associate exam after September 30, 2025
- Data analysts with ~6 months of Databricks experience
- Learners who prefer practice-driven exam preparation over traditional lecture-heavy study
Prerequisites
- Intermediate SQL knowledge
- Hands-on experience with Databricks SQL, Unity Catalog, and dashboarding
- Familiarity with data ingestion and management workflows
Course Outline
Module 1: Exam Orientation and Strategy
- Certification format, domains, scoring, timing
- Strategies for analyzing multiple-choice questions
- Question types and common distractors
- Pacing strategies and flagging questions for review
Module 2: Data Intelligence Platform and Ingestion
- Unity Catalog for discovering, querying, cleaning, and certifying datasets
- Ingestion methods: UI loading, API ingestion, Auto Loader, Delta Sharing, Marketplace
- Permissions, lineage, and catalog governance
Module 3: Executing and Optimizing Queries
- Creating and managing views
- Aggregate functions, filtering logic, and joins
- Query performance optimization and caching strategies
Module 4: Core SQL and Lakehouse Analytics
- Filtering, aggregation, joins, and subqueries
- MERGE, INSERT, and COPY INTO operations
- Working with nested data, rollups, cubes, and window functions
- Using UDFs for extended analysis
Module 5: Visualizations, Dashboards, and Alerts
- Building tables, counters, pivots, and styled charts
- Adding parameters, filters, and interactivity
- Scheduling dashboard refreshes and creating alerts
- Sharing and collaboration practices
Module 6: Analytics Applications and Data Enhancement
- Applying statistical concepts: descriptive measures, distributions, continuous vs. discrete
- Data blending and last-mile ETL within analytics workflows
- Using dashboards and SQL queries to solve end-to-end business scenarios
