Table of Contents
- Exam Overview
- The Four Domains
- Domain 1: Set Up and Configure an Azure Databricks Environment (15-20%)
- Domain 2: Secure and Govern Unity Catalog Objects (15-20%)
- Domain 3: Prepare and Process Data (30-35%)
- Domain 4: Deploy and Maintain Data Pipelines and Workloads (30-35%)
- The 6-Week Study Plan
- Key Concepts You MUST Know (Quick Reference)
- Exam-Day Tips
- Practice Questions (Sample Scenarios)
- Resources
- Full DP-750 Coverage
- Wrapping Up
The DP-750 (Implementing Data Engineering Solutions Using Azure Databricks) is Microsoft’s newest certification for Databricks Data Engineers. It validates your ability to set up Azure Databricks environments, govern data with Unity Catalog, ingest and transform data with PySpark and SQL, and deploy production pipelines with Lakeflow Jobs and Asset Bundles.
This post is your study companion — every exam objective mapped to the DriveDataScience post that covers it, a recommended study plan, exam-day tips, and the key concepts you MUST know for each domain.
This guide is based on the official skills measured as of March 11, 2026. The exam has four domains with different weights. You need a score of 700 or higher to pass.
Exam Overview
| Detail | Info |
|---|---|
| Exam code | DP-750 |
| Full name | Implementing Data Engineering Solutions Using Azure Databricks |
| Certification | Microsoft Certified: Azure Databricks Data Engineer Associate |
| Passing score | 700 / 1000 |
| Duration | 100-120 minutes |
| Question types | Multiple choice, scenario-based, drag-and-drop |
| Prerequisite | None (hands-on Databricks experience recommended) |
| Cost | $165 USD |
| Renewal | Annual (free online assessment) |
| Status | GA (beta ended May 2026) |
The Four Domains
| Domain | Weight | Focus |
|---|---|---|
| 1. Set up & Configure Environment | 15-20% | Compute types, clusters, SQL warehouses, Photon, libraries, Unity Catalog objects |
| 2. Secure & Govern Unity Catalog | 15-20% | Privileges, RLS, column masks, Key Vault, service principals, lineage, Delta Sharing |
| 3. Prepare & Process Data | 30-35% | Ingestion, Delta Lake, AutoLoader, Lakeflow, PySpark transforms, data quality, SCD |
| 4. Deploy & Maintain Pipelines | 30-35% | Workflows, Lakeflow Jobs, Git, DABs, CI/CD, monitoring, Spark UI, optimization |
The takeaway: 60-70% of the exam is Domains 3 and 4 — building and deploying data pipelines. Environment setup and governance are important but weighted lower.
—
Domain 1: Set Up and Configure an Azure Databricks Environment (15-20%)
Select and Configure Compute
| Exam Objective | Read This Post | Key Concepts |
|---|---|---|
| Choose compute type (job, serverless, warehouse, classic, shared) | Databricks Intro & dbutils | Cluster types, all-purpose vs job clusters, serverless vs classic |
| Configure compute (CPU, autoscaling, termination, pooling) | Databricks Intro & dbutils | Autoscaling min/max, auto-termination timeout, instance pools |
| Configure Photon, runtime/Spark version, ML runtime | Delta Lake & PySpark Optimization | Photon acceleration (C++ engine), runtime versions, AQE |
| Install libraries for compute | Databricks Intro & dbutils | Cluster libraries, notebook-scoped (%pip install), init scripts |
| SQL Warehouses (serverless, pro, classic) | Databricks SQL & Warehouses | Serverless vs pro vs classic, warehouse sizing, auto-stop, query history |
What to memorize: Job clusters = ephemeral (created per job, destroyed after). All-purpose = interactive (persists for development). Serverless = fully managed (no cluster config). Photon = C++ vectorized engine for faster SQL/DataFrame operations. Instance pools = pre-provisioned VMs for faster cluster startup.
Create and Organize Objects in Unity Catalog
| Exam Objective | Read This Post | Key Concepts |
|---|---|---|
| Create catalogs, schemas, volumes | Unity Catalog Deep Dive | Three-level namespace: catalog.schema.table, metastore hierarchy |
| Create catalogs, schemas, volumes | File Storage (Volumes, DBFS) | Managed vs external volumes, DBFS deprecation, external locations |
| Tables, views, materialized views | External Tables & Unity Catalog | Managed vs external tables, when to use each, DDL operations |
| Foreign catalogs and connections | Unity Catalog Deep Dive | Lakehouse Federation, foreign catalogs for external databases |
| DDL operations on managed/external tables | Reading/Writing Formats | CREATE TABLE, CREATE OR REPLACE, DROP, ALTER TABLE |
What to memorize: Unity Catalog hierarchy: Metastore > Catalog > Schema > Table/View/Volume. Managed tables = Databricks manages storage location AND lifecycle. External tables = you manage the storage location, Databricks manages metadata only. DROP on managed table deletes data. DROP on external table deletes metadata only (data stays). Volumes = managed file storage (replaces DBFS).
—
Domain 2: Secure and Govern Unity Catalog Objects (15-20%)
Secure Unity Catalog Objects
| Exam Objective | Read This Post | Key Concepts |
|---|---|---|
| Grant privileges to principals | Unity Catalog Deep Dive | GRANT SELECT/MODIFY/CREATE on catalog/schema/table, inheritance |
| Table/column-level access, row-level security | Unity Catalog Deep Dive | Column masks, row filters, GRANT SELECT on specific columns |
| Access Azure Key Vault secrets | Secret Scopes & Key Vault | Azure Key Vault-backed scopes, dbutils.secrets.get(), scope ACLs |
| Service principal authentication | Secret Scopes & Key Vault | Service principal for automated jobs, OAuth, client ID/secret |
| Service principal authentication | Connecting to Blob/ADLS | Service principal for ADLS access, OAuth2, Spark config |
| Managed identity authentication | Connecting to Blob/ADLS | Access connector, managed identity for passthrough |
Govern Unity Catalog Objects
| Exam Objective | Read This Post | Key Concepts |
|---|---|---|
| Table/column descriptions for data discovery | Unity Catalog Deep Dive | COMMENT ON, table properties, Catalog Explorer |
| Attribute-based access control (ABAC) with tags | Unity Catalog Deep Dive | Tags, tag-based policies, compliance tagging |
| Row filters and column masks | Unity Catalog Deep Dive | CREATE FUNCTION for row filter, column mask functions |
| Data retention policies | Delta Lake Deep Dive | VACUUM, time travel retention, TBLPROPERTIES |
| Data retention policies | Delta Lake Advanced | VACUUM retention, predictive optimization, deletion vectors, Change Data Feed |
| Data lineage tracking in Catalog Explorer | Unity Catalog Deep Dive | Lineage graph, owner, history, dependencies |
| Delta Sharing | Unity Catalog Deep Dive | Share objects, recipients, activation links |
What to memorize: GRANT inheritance: granting SELECT on a catalog grants it on all schemas and tables within. Row filters = SQL functions that return TRUE/FALSE per row. Column masks = SQL functions that return masked value per column. VACUUM default retention = 7 days. Delta Sharing = open protocol for sharing data across organizations without copying.
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Domain 3: Prepare and Process Data (30-35%)
Design and Implement Data Modeling
| Exam Objective | Read This Post | Key Concepts |
|---|---|---|
| Design ingestion logic and source config | AutoLoader (cloudFiles) | File notification vs directory listing, schema inference, rescue column |
| Choose ingestion tool (Lakeflow Connect, notebooks, ADF) | Reading/Writing Formats | spark.read, spark.write, format options for CSV/Parquet/JSON/Delta |
| Choose ingestion tool (Lakeflow Connect, notebooks, ADF) | PySpark Reading from APIs & Databases | JDBC parallel reads, API ingestion, partitioned reads |
| Choose data table format (Parquet, Delta, CSV, JSON, Iceberg) | Reading/Writing Formats | Delta as default, when to use each format, format conversion |
| Data partitioning scheme | Delta Lake & PySpark Optimization | partitionBy, Z-ORDER, liquid clustering |
| Slowly changing dimensions (SCD types) | SCD Type 1 & 2 with Delta MERGE | SCD Type 1 (overwrite), SCD Type 2 (history rows), Delta MERGE |
| Clustering strategy (liquid, Z-ORDER, deletion vectors) | Delta Lake & PySpark Optimization | Z-ORDER for multi-column, liquid clustering (auto), deletion vectors |
| Clustering strategy (liquid, Z-ORDER, deletion vectors) | Delta Lake Advanced | Liquid clustering, deletion vectors, UniForm (Iceberg), predictive optimization, TBLPROPERTIES |
| Managed vs unmanaged tables | External Tables & Unity Catalog | Decision matrix, DROP behavior, storage ownership |
Ingest Data into Unity Catalog
| Exam Objective | Read This Post | Key Concepts |
|---|---|---|
| Ingest using notebooks (batch + streaming) | Reading/Writing Formats | spark.read (batch), spark.readStream (streaming), write modes |
| Ingest using SQL (CTAS, CREATE OR REPLACE, COPY INTO) | Reading/Writing Formats | SQL DDL for table creation and data loading |
| Ingest using CDC feed | SCD Type 1 & 2 with Delta MERGE | Delta MERGE for upserts, CDF (Change Data Feed) |
| Ingest using CDC feed | Delta Lake Deep Dive | Change Data Feed (CDF), table_changes(), versioning |
| Ingest using Spark Structured Streaming | Streaming with Databricks | readStream, writeStream, checkpoints, watermarks, output modes |
| Ingest from Azure Event Hubs | Streaming with Databricks | Event Hubs + Kafka connector, eventhubs-spark library |
| Ingest using AutoLoader (Lakeflow Spark) | AutoLoader (cloudFiles) | cloudFiles format, schema evolution, rescue column, checkpointing |
| Ingest using Lakeflow Spark Declarative Pipelines | Lakeflow Declarative Pipelines | Streaming tables, materialized views, expectations, APPLY CHANGES, medallion architecture |
| Ingest using Lakeflow Connect | Lakeflow Connect | Managed SaaS connectors, database connectors with CDC, ingestion gateway, incremental ingestion |
Cleanse, Transform, and Load Data
| Exam Objective | Read This Post | Key Concepts |
|---|---|---|
| Profile data and assess distributions | PySpark Data Cleaning & Validation | describe(), summary(), approxCountDistinct(), null analysis |
| Identify/resolve duplicates, missing, null values | PySpark Data Cleaning & Validation | dropDuplicates(), fillna(), coalesce(), when/otherwise |
| Transform: filter, group, aggregate | PySpark Transformations | filter(), groupBy(), agg(), select(), withColumn() |
| Transform: join, union, intersect, except | PySpark Joins | Inner, left, right, full, anti, semi, cross, self joins |
| Transform: denormalize, pivot, unpivot | PySpark Transformations | pivot(), unpivot(), explode(), flatten nested structures |
| Transform: window functions | PySpark Window Functions | ROW_NUMBER, RANK, LAG, LEAD, running totals, PARTITION BY |
| Load: merge, insert, append | SCD Type 1 & 2 with Delta MERGE | Delta MERGE (WHEN MATCHED/NOT MATCHED), insertInto, append mode |
| UDFs and higher-order functions | PySpark UDFs & Higher-Order Functions | UDF, pandas_udf, transform(), filter(), aggregate() on arrays |
Implement Data Quality
| Exam Objective | Read This Post | Key Concepts |
|---|---|---|
| Validation checks (nullability, cardinality, range) | Data Quality Framework | Validation rules, quarantine pattern, quality metrics |
| Schema enforcement and schema drift | AutoLoader (cloudFiles) | Schema enforcement (reject bad schema), schema evolution (accept new columns) |
| Pipeline expectations in Lakeflow Spark Declarative Pipelines | Lakeflow Declarative Pipelines | EXPECT constraints (warn, drop, fail), expectation metrics, event log monitoring |
| Schema enforcement and schema drift | Delta Lake Deep Dive | mergeSchema, overwriteSchema, schema evolution in MERGE |
What to memorize: AutoLoader modes: file notification (scalable, uses cloud events) vs directory listing (simple, polls directory). Schema inference reads sample files. Rescue column captures data that does not match schema. Schema evolution adds new columns automatically with cloudFiles.schemaEvolutionMode = "addNewColumns". MERGE syntax: MERGE INTO target USING source ON condition WHEN MATCHED THEN UPDATE WHEN NOT MATCHED THEN INSERT.
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Domain 4: Deploy and Maintain Data Pipelines and Workloads (30-35%)
Design and Implement Data Pipelines
| Exam Objective | Read This Post | Key Concepts |
|---|---|---|
| Design pipeline order of operations | Workflows & Jobs | Task dependencies, multi-task jobs, DAG visualization |
| Create pipeline using notebooks | Workflows & Jobs | Notebook tasks, parameters, task values, precedence constraints |
| Error handling in pipelines | Workflows & Jobs | Retry policies, conditional tasks, email notifications |
| Lakeflow Spark Declarative Pipelines (DLT) | Streaming with Databricks | Delta Live Tables concepts, expectations, medallion architecture |
| Create pipeline using Lakeflow Spark Declarative Pipelines | Lakeflow Declarative Pipelines | Streaming tables, materialized views, triggered vs continuous modes, CDC with APPLY CHANGES |
Implement Lakeflow Jobs
| Exam Objective | Read This Post | Key Concepts |
|---|---|---|
| Create and configure a job | Workflows & Jobs | Job creation, task types (notebook, SQL, Python, JAR), compute |
| Configure job triggers and scheduling | Workflows & Jobs | Cron schedule, file arrival trigger, continuous trigger |
| Configure alerts and automatic restarts | Workflows & Jobs | Email/webhook alerts, retry on failure, max concurrent runs |
Implement Development Lifecycle
| Exam Objective | Read This Post | Key Concepts |
|---|---|---|
| Git version control best practices | Git Integration & CI/CD | Repos, branching, pull requests, conflict resolution |
| Testing strategy (unit, integration, E2E, UAT) | Git Integration & CI/CD | Test notebooks, CI pipeline with pytest, staging environments |
| Configure and package Databricks Asset Bundles | Asset Bundles (DABs) | databricks.yml, bundle validate, bundle deploy, environments |
| Deploy bundles using CLI and REST APIs | Asset Bundles (DABs) | databricks bundle deploy –target prod, GitHub Actions integration |
Monitor, Troubleshoot, and Optimize
| Exam Objective | Read This Post | Key Concepts |
|---|---|---|
| Monitor cluster consumption and cost | Databricks SQL & Warehouses | Query history, cluster events, cost attribution |
| Troubleshoot Spark jobs (DAG, Spark UI, query profile) | Lazy Evaluation | DAG visualization, Catalyst optimizer, execution plan |
| Troubleshoot Spark jobs (DAG, Spark UI, query profile) | PySpark Architecture | Driver/executor, shuffle, stages, tasks, Spark UI tabs |
| Resolve caching, skewing, spilling, shuffle issues | Delta Lake & PySpark Optimization | cache(), repartition(), broadcast joins, AQE skew handling |
| Optimize Delta tables (OPTIMIZE, VACUUM) | Delta Lake Deep Dive | OPTIMIZE (compact files), VACUUM (remove old versions), DESCRIBE HISTORY |
| Implement log streaming with Azure Monitor | Monitoring, Azure Monitor & AI/BI Genie | Diagnostic settings, log categories, Log Analytics KQL queries, Azure Monitor alerts, system tables |
| Optimize Delta tables (OPTIMIZE, VACUUM) | Delta Lake & PySpark Optimization | Z-ORDER, AQE, broadcast joins, partition pruning |
What to memorize: OPTIMIZE compacts small files into larger ones. VACUUM removes files older than retention period (default 7 days). Z-ORDER co-locates related data within files for filter performance. Liquid clustering = automatic, replaces manual partitioning + Z-ORDER. DABs use databricks.yml to define jobs, pipelines, and environments as code. databricks bundle deploy --target prod deploys to production.
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The 6-Week Study Plan
Week 1: Foundations (Domain 1)
Read and practice: Databricks Intro & dbutils, Connecting to Blob/ADLS, Secret Scopes & Key Vault, File Storage (Volumes, DBFS). Focus on: creating clusters, choosing compute types, installing libraries, configuring Spark settings. Hands-on: create an all-purpose cluster, a job cluster, and a serverless SQL warehouse.
Week 2: Unity Catalog (Domain 2)
Read and practice: Unity Catalog Deep Dive, External Tables & Unity Catalog. Focus on: three-level namespace, GRANT privileges, row filters, column masks, lineage, Delta Sharing. Hands-on: create catalog > schema > managed table > external table. Set up a row filter and column mask.
Week 3: Data Ingestion (Domain 3a)
Read and practice: Reading/Writing Formats, AutoLoader, Delta Lake Deep Dive, Streaming with Databricks. Focus on: spark.read/write, cloudFiles, CTAS, COPY INTO, readStream/writeStream, checkpoints. Hands-on: ingest CSV/JSON/Parquet into Delta tables. Set up an AutoLoader stream with schema evolution.
Week 4: Data Transformation (Domain 3b)
Read and practice: PySpark Transformations, Joins, Window Functions, Data Cleaning, UDFs, SCD Type 1 & 2 with Delta MERGE. Focus on: filter, groupBy, join types, window functions, MERGE syntax, data quality checks. Hands-on: build a complete bronze > silver > gold pipeline with MERGE upserts.
Week 5: Pipelines & CI/CD (Domain 4)
Read and practice: Workflows & Jobs, Git Integration & CI/CD, Asset Bundles (DABs). Focus on: multi-task jobs, triggers, retry policies, Repos, DABs deployment. Hands-on: create a multi-task workflow, connect to Git, deploy with DABs.
Week 6: Optimization & Review
Read and practice: Delta Lake & PySpark Optimization, Lazy Evaluation, PySpark Architecture, Databricks SQL & Warehouses. Focus on: OPTIMIZE, VACUUM, Z-ORDER, Spark UI, DAG analysis, AQE. Take the Microsoft free practice assessment. Re-read this study guide. Focus on weak areas.
Key Concepts You MUST Know (Quick Reference)
Compute Types Decision
Interactive development? -> All-purpose cluster
Automated ETL job? -> Job cluster (ephemeral)
SQL analytics/dashboards? -> SQL Warehouse (serverless)
Need Photon acceleration? -> Enable on cluster or warehouse
ML training with GPU? -> ML runtime + GPU nodesUnity Catalog Hierarchy
Metastore (one per region)
-> Catalog (dev, staging, prod)
-> Schema (bronze, silver, gold)
-> Table / View / Materialized View
-> Volume (managed file storage)
-> Function (UDF, row filter, column mask)AutoLoader Modes
Directory listing: Simple, polls directory, good for < 10K files/day
File notification: Scalable, uses cloud events (SQS/EventGrid), good for high volume
Schema inference: Reads sample of files, stores schema in checkpoint
Schema evolution: addNewColumns (add new), failOnNewColumns (reject), rescue (capture)Delta Table Commands
OPTIMIZE Compact small files into larger ones (1 GB target)
VACUUM Remove files older than retention (default 7 days)
Z-ORDER Co-locate data by frequently filtered columns
DESCRIBE HISTORY Show all operations on a table (versioning)
RESTORE Roll back to a previous version (time travel)
MERGE INTO Upsert: update existing + insert new rowsSpark Troubleshooting
Slow query? Check Spark UI > Stages > look for shuffle read/write size
Data skew? One task takes 10x longer than others > repartition or salting
Spilling? "Spill to disk" in Spark UI > increase executor memory or reduce partitions
Too many files? OPTIMIZE + autoOptimize.optimizeWrite = true
OOM error? Increase driver/executor memory or reduce partition sizeDABs Commands
databricks bundle init Create a new bundle project
databricks bundle validate Check YAML syntax and references
databricks bundle deploy Deploy to target environment
databricks bundle run Run a job/pipeline from the bundle
databricks bundle destroy Remove deployed resourcesExam-Day Tips
1. 60-70% is about building and deploying pipelines (Domains 3+4) — spend most of your study time here, not on workspace configuration.
2. Know Delta MERGE syntax cold — at least 3-5 questions will involve MERGE scenarios (SCD, upsert, CDC). Practice writing MERGE statements from memory.
3. AutoLoader questions are almost guaranteed — know the difference between file notification and directory listing, schema inference vs schema evolution, and the rescue column.
4. Unity Catalog permissions flow downward — GRANT on a catalog applies to all schemas and tables within. This is tested frequently.
5. DABs vs manual deployment — know when to use Asset Bundles (CI/CD, multiple environments) vs manual UI deployment (quick prototyping).
6. Read scenario questions carefully — “MOST cost-effective” vs “FASTEST” lead to different answers. Job clusters are cheapest (auto-terminate). All-purpose clusters are fastest (already running).
7. Spark UI and DAG questions — you will be asked to identify bottlenecks from Spark UI screenshots or descriptions. Know what shuffle, spill, and skew look like.
8. This exam is practical — it does not ask “What is Z-ORDER?” It asks “A query on a 500 GB Delta table filtered by date and region takes 10 minutes. The table has 50,000 small files. What two actions would improve performance?” (Answer: OPTIMIZE + Z-ORDER BY date, region).
Practice Questions (Sample Scenarios)
Q1: A data engineer needs to ingest CSV files that arrive continuously in an ADLS Gen2 container. The schema may change over time (new columns added). What should they use?
A) COPY INTO with schema evolution B) AutoLoader with cloudFiles.schemaEvolutionMode = "addNewColumns" C) Spark Structured Streaming with a fixed schema D) Azure Data Factory Copy Activity
Answer: B — AutoLoader with schema evolution mode handles continuous file ingestion and automatically adds new columns as they appear. COPY INTO is batch, not continuous. Fixed schema streaming would fail on new columns.
Q2: A team has a Delta table with 100,000 small files (1-5 MB each). Queries are slow. What two actions should they take?
A) Run OPTIMIZE and enable autoOptimize.optimizeWrite B) Run VACUUM with 0 hour retention C) Add more executor nodes D) Convert to Parquet format
Answer: A — OPTIMIZE compacts small files into ~1 GB files. autoOptimize.optimizeWrite prevents future small files. VACUUM with 0 retention is dangerous (breaks time travel). More nodes does not fix the small file problem. Converting to Parquet loses Delta features.
Q3: A data engineer needs to implement row-level security so that users in the “sales_east” group can only see orders from the East region. Where should they define this?
A) A SQL function applied as a row filter on the table in Unity Catalog B) A view with a WHERE clause C) A cluster-level Spark configuration D) An Azure AD conditional access policy
Answer: A — Unity Catalog row filters use SQL functions (CREATE FUNCTION) applied to tables. The filter is enforced at the data layer regardless of how the table is accessed (notebook, SQL, BI tool). A view works but can be bypassed by accessing the table directly.
Q4: A data engineering team wants to deploy notebooks and job definitions from dev to staging to production. They need version control, environment-specific configurations, and automated CI/CD. What should they use?
A) Databricks Repos with manual deployment B) Databricks Asset Bundles with GitHub Actions C) Azure Data Factory deployment pipelines D) Export/import notebooks as DBC files
Answer: B — Asset Bundles (DABs) provide declarative YAML configuration, environment-specific targets (dev/staging/prod), and CLI commands that integrate with GitHub Actions for automated CI/CD. Repos alone do not handle deployment. ADF pipelines are a different tool.
Q5: A Spark job takes 45 minutes. Looking at the Spark UI, one stage has 200 tasks but one task took 40 minutes while the other 199 finished in under 1 minute. What is the most likely issue and fix?
A) Data skew — repartition by a different column or use AQE skew join optimization B) Insufficient memory — increase executor memory C) Too many shuffle partitions — reduce to 10 D) Network bottleneck — use a larger cluster
Answer: A — One task taking 40x longer than others is classic data skew — one partition has far more data than the rest. Enable AQE (spark.sql.adaptive.skewJoin.enabled = true) or manually salt the skewed key.
Resources
| Resource | Link |
|---|---|
| Official exam page | DP-750 on Microsoft Learn |
| Official study guide | Skills measured (March 2026) |
| Microsoft Learn path | DP-750 learning path |
| Databricks documentation | Azure Databricks docs |
| DriveDataScience Databricks posts | All 23 Databricks posts |
| DriveDataScience PySpark posts | All 10 PySpark posts |
| Exam sandbox | Try the exam interface |
Full DP-750 Coverage
All DP-750 exam objectives are now covered by DriveDataScience posts. Our 22 Databricks posts and 10 PySpark posts provide comprehensive coverage across all four domains, including the newest features: Lakeflow Declarative Pipelines, Lakeflow Connect, liquid clustering, deletion vectors, UniForm (Iceberg compatibility), Azure Monitor integration, and AI/BI Genie.
Wrapping Up
The DP-750 is a practical, scenario-based exam that tests your ability to build real data engineering solutions on Azure Databricks. Our 23 Databricks posts and 10 PySpark posts cover the vast majority of objectives — from Unity Catalog governance to Delta MERGE upserts to Asset Bundles CI/CD. Focus your study time on Domains 3 and 4 (60-70% of the exam), practice Delta MERGE and AutoLoader from memory, and supplement with Microsoft Learn for the newest features (Lakeflow, liquid clustering).
Good luck on your DP-750!
The complete DriveDataScience learning paths: – All Databricks Posts (23 posts) – All PySpark Posts (10 posts) – DP-700 Study Guide (Fabric)
Naveen Vuppula is a Senior Data Engineering Consultant and app developer based in Ontario, Canada. He writes about Python, SQL, AWS, Azure, and everything data engineering at DriveDataScience.com.