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Databricks Certified Data Analyst Associate Syllabus

Understanding of Databricks Data Intelligence Platform

Core platform components, Unity Catalog objects and Marketplace.

11%
Weight
5
Questions
11
Marks

Platform Components

  • Databricks SQL and SQL warehouses
  • Delta Lake and the Data Intelligence Engine
  • Lakeflow Declarative Pipelines and Lakeflow Jobs
  • Mosaic AI and Lakebase at a high level

Unity Catalog Objects and Marketplace

  • Catalogs, schemas, managed and external tables
  • Views, certified tables and metric views
  • Lineage in Catalog Explorer
  • Databricks Marketplace roles and features

Managing Data

Discovering, tagging and cleaning governed data in Unity Catalog.

8%
Weight
4
Questions
8
Marks

Discovery and Tagging

  • Finding and querying certified datasets
  • Governed tags on data assets
  • Viewing lineage including external lineage
  • Catalog Explorer navigation

Data Cleaning with SQL

  • Removing invalid records
  • Handling missing values
  • Type casting and standardization
  • Writing cleaned results to Unity Catalog tables

Importing Data

Ingestion approaches available to analysts.

5%
Weight
2
Questions
5
Marks

Ingestion Approaches

  • Cloud object storage (S3) ingestion
  • Auto Loader
  • Lakeflow Connect managed connectors
  • API-driven ingestion

Shared and Uploaded Data

  • Delta Sharing
  • Databricks Marketplace datasets
  • Uploading files through the workspace UI
  • Creating a table from an uploaded file

Executing Queries Using Databricks SQL and SQL Warehouses

Writing and running SQL on SQL warehouses, including views, joins and time travel.

20%
Weight
9
Questions
20
Marks

SQL Warehouses and Genie Code

  • Role of a SQL warehouse in query execution
  • Using Genie Code (formerly Databricks Assistant) to write and debug SQL
  • Lakehouse Federation joins with external sources
  • Creating managed and external tables from CSV, Parquet and Delta

Query Techniques

  • Aggregations including approx_count_distinct and summary statistics
  • Inner, left, right and multi-key joins
  • UNION vs. UNION ALL set operations
  • Sorting and filtering
  • Delta Lake time travel queries

Views and Tables

  • Materialized views vs. streaming tables
  • Dynamic views vs. materialized views
  • Creating materialized views in Databricks SQL
  • Choosing a view type for freshness and cost

Analyzing Queries

Diagnosing and improving query performance and validating results.

15%
Weight
7
Questions
15
Marks

Performance Tools

  • Photon features and supported workloads
  • Query history and Query Insights
  • Query Profile to find bottlenecks
  • Result and disk caching

Table Optimization and Validation

  • Predictive Optimization use cases
  • Automatic Liquid Clustering
  • Delta table history for auditing and validation
  • Fixing queries to return the intended results

Working with Dashboards and Visualizations in Databricks

Building, sharing and scheduling AI/BI dashboards and alerts.

16%
Weight
7
Questions
16
Marks

Building Dashboards

  • Multi-page AI/BI dashboards with multiple datasets
  • Visualization, text and image widgets
  • Visualizations in notebooks and the SQL editor
  • Choosing effective visualization types

Parameters, Sharing and Alerts

  • Query and dashboard parameters
  • Sharing with users, groups and external viewers
  • Embedding dashboards in external apps
  • Scheduled refreshes
  • Alerts with thresholds and destinations

Developing, Sharing, and Maintaining AI/BI Genie Agents (Genie Spaces)

Creating, distributing and improving Genie spaces for conversational analytics.

12%
Weight
5
Questions
12
Marks

Creating Genie Spaces

  • Purpose and components of a Genie space
  • Sample questions and domain instructions
  • Curated Unity Catalog datasets and trusted assets
  • Choosing the SQL warehouse

Sharing and Optimizing

  • Permissions and distribution via links and apps
  • Monitoring questions, accuracy and feedback
  • Benchmarks to validate answers
  • Refreshing Unity Catalog metadata

Data Modeling with Databricks SQL

Analytical data models and their fit with the medallion architecture.

5%
Weight
2
Questions
5
Marks

Dimensional Models

  • Star schemas
  • Snowflake schemas
  • Fact and dimension tables
  • Choosing a model for an analytical workload

Data Vault and Medallion

  • Data vault hubs, links and satellites
  • Bronze, silver and gold layers
  • Mapping models to medallion layers
  • Gold-layer tables for BI

Securing Data

Protecting data and workspace objects with Unity Catalog.

8%
Weight
4
Questions
8
Marks

Unity Catalog Namespace and Privileges

  • Three-level namespace (catalog.schema.table/volume)
  • Granting privileges on objects
  • Sharing settings for workspace objects
  • Table ownership

Data Protection Practices

  • Protecting PII
  • Storage best practices
  • Managed vs. external storage
  • Auditing access

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Learning Progress

Understanding of Databricks Data Intelligence Platform 85%
Managing Data 92%

Practice Test Scores

95%
Latest Score
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