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Section 1
Platform architecture, Delta Lake, and Unity Catalog
Preview
Preview: Databricks Intelligence Platform
Preview includes- 4 of 5 lesson topics
- 1 overview segment
- 3 core concepts
- 2 exam tips
Lesson Topics
- Platform architecture, Delta Lake, and Unity Catalog
- Core behavior and terminology
- Scenario decision rules
- Operational failure modes
Overview
Know how the Databricks platform separates control, compute, storage, and governance while presenting a governed lakehouse to data engineering, analytics, and AI workloads.
Core Concepts
- Account-level services manage identities, workspaces, Unity Catalog metastores, and usage; a workspace is the primary collaboration and compute boundary.
- The control plane hosts Databricks backend services and the web application; customer data processing occurs in a compute plane.
- Classic compute runs in the customer cloud account; serverless compute is provisioned and managed by Databricks.
Exam Tips
- Answer from the stated requirement and operational constraint; do not choose a product merely because it is familiar.
- Do not describe Unity Catalog as the compute plane or Delta Lake as the control plane.
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Section 2
Choose the right compute
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Section 3
Batch, streaming, incremental, and Lakeflow Connect ingestion
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Section 4
COPY INTO incremental file loading
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Section 5
Auto Loader: discovery, schema, rescue, and checkpoints
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Section 6
Lakeflow Connect enterprise ingestion
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Section 7
Custom JDBC, ODBC, and REST ingestion
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Section 8
Choose among ingestion methods
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Section 9
Semi-structured and unstructured ingestion
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Section 10
Clean Bronze into trusted Silver
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Section 11
Joins, broadcast joins, cross joins, and unions
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Section 12
Manipulate columns, rows, and nested structures
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Section 13
Deduplication and aggregation
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Section 14
Basic Spark tuning and re-measurement
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Section 15
Gold tables, views, materialized views, and streaming tables
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Section 16
Data quality checks and pipeline expectations
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Section 17
Retries, run-if conditions, branches, and loops
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Section 18
Lakeflow Jobs task types and DAG dependencies
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Section 19
Schedules, file-arrival triggers, and table-update triggers
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Section 20
Time-based vs data-driven trigger decisions
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Section 21
Git folders: branches, commits, pushes, pulls, and PR workflow
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Section 22
Bundle variables, targets, and overrides
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Section 23
Declarative Automation Bundles
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Section 24
Databricks CLI bundle lifecycle
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Section 25
Job run history and performance baselines
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Section 26
Interpret the Lakeflow Jobs UI
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Section 27
Spark UI: skew, shuffle, and spill
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Section 28
Liquid Clustering and predictive optimization
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Section 29
Cluster startup, dependency conflicts, and OOM diagnosis
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Section 30
Managed vs external tables and lifecycle
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Section 31
Unity Catalog privileges and hierarchy
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Section 32
Row filters and column masks
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Section 33
Unity Catalog ABAC
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