DP-750 — Microsoft Certified: Azure Databricks Data Engineer Associate Exam Blueprint

How to Use This Exam Blueprint

Use this independent Exam Blueprint as a practical study map for Microsoft DP-750, the exam for Microsoft Certified: Azure Databricks Data Engineer Associate (DP-750). It is designed for final review and gap-finding, not as a replacement for hands-on Azure Databricks practice.

For each topic area, ask:

• Can I explain the concept without notes?
• Can I choose the right Azure Databricks feature for a scenario?
• Can I recognize a bad design and correct it?
• Can I troubleshoot a failed pipeline, slow query, permission error, or data-quality issue?
• Can I connect Azure Databricks concepts with Azure storage, security, monitoring, and production operations?

If an area feels familiar but you cannot apply it in a scenario, mark it for more practice.

Readiness areas at a glance

Core DP-750 skill checklist

Azure Databricks platform foundations

Be ready to show that you understand how Azure Databricks is used as a data engineering platform, not just as a notebook tool.

• Explain the role of an Azure Databricks workspace.
• Distinguish interactive development from scheduled production execution.
• Compare all-purpose compute, job compute, and SQL warehouse-style query execution at a practical level.
• Identify when notebooks, jobs, workflows, and SQL are appropriate.
• Explain how Azure Databricks interacts with cloud storage such as Azure Data Lake Storage.
• Recognize why compute configuration affects performance, cost, isolation, and reliability.
• Understand the difference between workspace-level objects and governed data objects.
• Explain how libraries, runtime versions, policies, and environment configuration can affect pipeline behavior.
• Identify where secrets, credentials, and service identities should be handled rather than hard-coded.

Can you do this?

Lakehouse design and data organization

DP-750 readiness requires more than knowing that Delta tables exist. You should be able to design a maintainable data layout.

• Explain bronze, silver, and gold layers and what each layer typically stores.
• Identify raw, cleansed, conformed, aggregated, and serving data responsibilities.
• Choose managed or external tables based on ownership, lifecycle, governance, and storage needs.
• Explain how catalogs, schemas, and tables organize data assets.
• Use consistent naming, pathing, and environment separation.
• Identify when to preserve raw data versus overwrite or transform in place.
• Explain how incremental processing reduces cost and improves timeliness.
• Recognize when a pipeline should be replayable from raw data.
• Plan for schema drift, late-arriving data, duplicates, and bad records.
• Connect data modeling choices to downstream analytics and reporting needs.

Delta Lake table operations

Know the purpose and risks of Delta operations. Scenario questions often test judgment: when to merge, when to overwrite, when to optimize, and when not to vacuum.

• Explain ACID transaction benefits for concurrent reads and writes.
• Use Delta tables for reliable batch and streaming workloads.
• Explain schema enforcement and schema evolution.
• Use MERGE logic for upserts, slowly changing records, or deduplication scenarios.
• Understand append, overwrite, update, delete, and merge semantics.
• Use table history to investigate changes.
• Explain time travel at a practical level.
• Understand the purpose of compaction and optimization.
• Explain why small files can degrade performance.
• Understand the purpose and risk of vacuuming old files.
• Recognize when partitioning, clustering, data skipping, or statistics can improve query performance.
• Avoid over-partitioning high-cardinality columns.
• Understand constraints and expectations as data-quality controls where applicable.

Delta operation readiness table

Data ingestion and incremental processing

Be ready to choose an ingestion pattern from scenario details: source type, latency, file arrival pattern, schema drift, volume, idempotency, and failure recovery.

• Distinguish batch ingestion from streaming ingestion.
• Explain when incremental ingestion is preferable to full reloads.
• Use checkpointing concepts for streaming or incremental file processing.
• Identify why idempotent writes matter when jobs retry.
• Handle duplicate files, duplicate records, and late-arriving data.
• Explain schema inference, schema location, and controlled schema evolution.
• Choose raw ingestion into bronze before cleansing when replay and audit matter.
• Know how bad records can be captured, quarantined, or rejected.
• Explain when event streaming is appropriate versus scheduled file ingestion.
• Recognize when watermarks, windows, and state management are relevant.
• Understand how secrets and credentials should be used for source connections.
• Validate source-to-target row counts and quality checks after ingestion.

Ingestion decision cues

    flowchart TD
	    A[New data source] --> B{Latency requirement?}
	    B -->|Near real time| C[Streaming or continuous incremental ingestion]
	    B -->|Scheduled batch| D[Batch or trigger-based ingestion]
	    C --> E{Can records arrive late or out of order?}
	    E -->|Yes| F[Use event-time logic, watermarking, deduplication]
	    E -->|No| G[Use simpler append or merge pattern]
	    D --> H{Full reload or changed data only?}
	    H -->|Changed data only| I[Incremental load with merge/upsert]
	    H -->|Full extract| J[Replace, compare, or merge based on target needs]
	    F --> K[Write bronze, validate, promote to silver/gold]
	    G --> K
	    I --> K
	    J --> K

Spark SQL, PySpark, and transformation logic

For DP-750, you should be comfortable reading both SQL-style and DataFrame-style logic. You do not need to turn every problem into code, but you should understand what the code is doing.

• Write and interpret joins, filters, aggregations, and window functions.
• Distinguish inner, left, right, full, semi, and anti join behavior.
• Use window functions for ranking, deduplication, running totals, and latest-record selection.
• Explain when dropDuplicates is insufficient without a deterministic rule.
• Understand null handling in filters, joins, and aggregations.
• Apply business rules consistently across batch and streaming paths.
• Avoid unnecessary UDFs when built-in Spark functions can perform better.
• Understand lazy evaluation at a practical level.
• Recognize transformations that cause shuffles.
• Explain when broadcast joins may help and when they may be unsafe.
• Validate transformation outputs with row counts, constraints, and sample checks.
• Understand how to parameterize paths, catalog names, dates, and environments.

Transformation checks

Orchestration, jobs, and pipeline operations

The exam identity is data engineering, so be ready for production pipeline thinking: dependencies, retries, monitoring, permissions, parameters, and promotion.

• Create a mental model of a multi-task workflow.
• Explain task dependencies and failure behavior.
• Use parameters instead of hard-coded values.
• Know why job compute is often preferable for repeatable scheduled workloads.
• Configure retries and notifications based on failure impact.
• Separate development, test, and production environments.
• Track job run history and logs.
• Understand how pipeline code is promoted through source control.
• Identify when a task should fail fast versus quarantine bad data.
• Explain how to rerun failed tasks without corrupting target data.
• Recognize when a pipeline should be split into independent tasks.
• Understand the role of declarative or managed pipeline features where used in Azure Databricks.

Governance, security, and Unity Catalog concepts

Security questions often test boundaries: workspace access, data access, storage access, identity, and secret handling are related but not identical.

• Explain the purpose of centralized data governance in Azure Databricks.
• Understand catalogs, schemas, tables, views, volumes, and other governed objects at a conceptual level.
• Distinguish data-plane access from workspace UI access.
• Explain why least privilege matters for pipelines and users.
• Use groups or service identities rather than assigning broad permissions to individuals.
• Recognize when a storage credential or external location pattern is needed.
• Avoid hard-coding secrets, tokens, connection strings, or keys in notebooks.
• Understand how views can be used to restrict or shape data access.
• Recognize row-level, column-level, masking, or filtering patterns where applicable.
• Explain lineage and audit value for production data pipelines.
• Know how ownership affects the ability to manage objects.
• Troubleshoot permission errors by checking both Databricks permissions and Azure resource access.

Security decision checks

Performance tuning and optimization

Performance readiness means reading symptoms and choosing targeted fixes. Avoid “bigger cluster” as the default answer.

• Explain how partition pruning, data skipping, clustering, and file organization affect reads.
• Identify small-file problems and when compaction helps.
• Recognize over-partitioning and skewed partitioning.
• Use query plans or Spark UI clues to reason about slow jobs.
• Understand shuffle-heavy operations such as joins, aggregations, and repartitions.
• Explain skew and possible mitigations.
• Know when caching can help repeated reads and when it wastes memory.
• Compare scaling compute with optimizing data layout.
• Identify when statistics or table maintenance can improve query planning.
• Understand that streaming performance depends on source rate, trigger interval, state, checkpointing, and sink throughput.
• Avoid collecting large datasets to the driver.
• Avoid Python-side loops over large distributed datasets.

Monitoring, troubleshooting, and reliability

Be ready to diagnose pipeline failures from available artifacts: job history, task logs, Spark UI, query history, table history, event logs, and data-quality results.

• Locate job run failures and identify failed tasks.
• Read error messages for permission, schema, path, memory, timeout, and dependency issues.
• Use table history to investigate recent writes.
• Compare expected and actual row counts.
• Validate that checkpoints are not accidentally deleted or reused incorrectly.
• Identify malformed records or schema drift as ingestion failure causes.
• Explain how retries interact with idempotent writes.
• Recognize when a failure is data-related versus infrastructure-related.
• Use logging and metrics to support root-cause analysis.
• Define alerts for failed jobs, late data, quality failures, and performance regressions.
• Know how to communicate impact: failed load, stale table, partial refresh, or incorrect data.

DevOps, deployment, and lifecycle management

A data engineer associate should be able to reason about repeatable delivery, not just interactive exploration.

• Use source control for notebooks, scripts, SQL, and configuration.
• Separate code from environment-specific settings.
• Promote artifacts through development, test, and production.
• Use job definitions or deployment artifacts rather than manual recreation.
• Parameterize catalog, schema, storage path, checkpoint path, and run date.
• Manage dependencies and libraries consistently.
• Use reviews and testing for transformation logic.
• Validate data quality before publishing curated tables.
• Protect production data from accidental overwrite.
• Plan rollback or recovery using versioned code and table history where applicable.
• Understand how automated deployment reduces configuration drift.

SQL and PySpark artifact checks

You do not need to memorize every syntax variation, but you should recognize the intent, risks, and expected result of common data engineering patterns.

Delta merge pattern

MERGE INTO catalog.silver.customers AS target
USING catalog.bronze.customer_updates AS source
ON target.customer_id = source.customer_id
WHEN MATCHED AND source.updated_at > target.updated_at THEN
  UPDATE SET *
WHEN NOT MATCHED THEN
  INSERT *

Be ready to explain:

• What column or business key determines a match.
• Why the timestamp condition matters.
• What happens if the source has multiple rows for the same key.
• Whether the operation is idempotent if rerun.
• How you would validate the target after the merge.

Incremental file ingestion pattern

(
    spark.readStream
        .format("cloudFiles")
        .option("cloudFiles.format", "json")
        .option("cloudFiles.schemaLocation", schema_path)
        .load(raw_path)
        .writeStream
        .option("checkpointLocation", checkpoint_path)
        .trigger(availableNow=True)
        .toTable("catalog.bronze.events")
)

Be ready to explain:

• Why a schema location is used.
• Why a checkpoint location is required.
• What happens if the checkpoint is deleted.
• Why the bronze target is useful before cleansing.
• How schema drift should be controlled before promotion to silver.

Window-based deduplication pattern

CREATE OR REPLACE TABLE catalog.silver.latest_orders AS
SELECT *
FROM (
    SELECT
        *,
        ROW_NUMBER() OVER (
            PARTITION BY order_id
            ORDER BY updated_at DESC
        ) AS rn
    FROM catalog.bronze.orders
)
WHERE rn = 1;

Be ready to explain:

• Why ROW_NUMBER provides deterministic latest-record selection only if the ordering is sufficient.
• What additional tie-breaker may be needed.
• Whether this is a full rebuild or should be adapted for incremental processing.
• How late-arriving records could affect the result.

Table investigation commands

Scenario and decision-point checks

Use these prompts to test whether you can choose the best option under constraints.

Common weak areas and traps

Final-week DP-750 review checklist

Quick self-assessment scorecard

Rate each area from 1 to 3:

• 1 = I recognize terms but cannot apply them
• 2 = I can solve basic questions but struggle with scenarios
• 3 = I can explain tradeoffs and troubleshoot

If any area is a 1, review it before taking full-length practice. If an area is a 2, focus on scenario questions that force you to choose between two plausible designs.

Practical next step

Turn this Exam Blueprint into a study tracker. For each missed practice question, tag it to one of the readiness areas above, write the reason you missed it, and practice a similar Azure Databricks scenario until you can explain the correct choice, the rejected alternatives, and the production tradeoff.