Free Preparation Discussions
MENU
Databricks Certified Data Engineer Professional Exam Questions
- Topic 1: Databricks Tooling: The Databricks Tooling topic encompasses the various features and functionalities of Delta Lake. This includes understanding the transaction log, Optimistic Concurrency Control, Delta clone, indexing optimizations, and strategies for partitioning data for optimal performance in the Databricks SQL service.
- Topic 2: Data Processing: The topic covers understanding partition hints, partitioning data effectively, controlling part-file sizes, updating records, leveraging Structured Streaming and Delta Lake, implementing stream-static joins and deduplication. Additionally, it delves into utilizing Change Data Capture and addressing performance issues related to small files.
- Topic 3: Data Modeling: It focuses on understanding the objectives of data transformations, using Change Data Feed, applying Delta Lake cloning, designing multiplex bronze tables. Lastly it discusses implementing incremental processing and data quality enforcement, implementing lookup tables, and implementing Slowly Changing Dimension tables, and implementing SCD Type 0, 1, and 2 tables.
- Topic 4: Security & Governance: It discusses creating Dynamic views to accomplishing data masking and using dynamic views to control access to rows and columns.
- Topic 5: Monitoring & Logging: This topic includes understanding the Spark UI, inspecting event timelines and metrics, drawing conclusions from various UIs, designing systems to control cost and latency SLAs for production streaming jobs, and deploying and monitoring both streaming and batch jobs.
- Topic 6: Testing & Deployment: It discusses adapting notebook dependencies to use Python file dependencies, leveraging Wheels for imports, repairing and rerunning failed jobs, creating jobs based on common use cases, designing systems to control cost and latency SLAs, configuring the Databricks CLI, and using the REST API to clone a job, trigger a run, and export the run output.
Free Databricks Databricks Certified Data Engineer Professional Exam Actual Questions
Note: Premium Questions for Databricks Certified Data Engineer Professional were last updated On Nov. 18, 2025 (see below)
A task orchestrator has been configured to run two hourly tasks. First, an outside system writes Parquet data to a directory mounted at /mnt/raw_orders/. After this data is written, a Databricks job containing the following code is executed:
(spark.readStream
.format("parquet")
.load("/mnt/raw_orders/")
.withWatermark("time", "2 hours")
.dropDuplicates(["customer_id", "order_id"])
.writeStream
.trigger(once=True)
.table("orders")
)
Assume that the fields customer_id and order_id serve as a composite key to uniquely identify each order, and that the time field indicates when the record was queued in the source system. If the upstream system is known to occasionally enqueue duplicate entries for a single order hours apart, which statement is correct?
Comprehensive and Detailed Explanation From Exact Extract:
Exact extract: ''dropDuplicates with watermark performs stateful deduplication on the keys within the watermark delay.''
Exact extract: ''Records older than the event-time watermark are considered late and may be dropped.''
Exact extract: ''trigger(once) processes all available data once and then stops.''
The watermark of 2 hours bounds the deduplication state. Duplicate orders within the 2-hour window are removed; duplicates arriving later than 2 hours behind the corresponding first event are considered late and are ignored, so they won't appear, but any orders that themselves arrive later than the watermark will be dropped and thus be missing.
===========
How are the operational aspects of Lakeflow Declarative Pipelines different from Spark Structured Streaming?
Comprehensive and Detailed Explanation From Exact Extract of Databricks Data Engineer Documents:
Databricks documentation explains that Lakeflow Declarative Pipelines build upon Structured Streaming but add higher-level orchestration and automation capabilities. They automatically manage dependencies, materialization, and recovery across multi-stage data flows without requiring external orchestration tools such as Airflow or Azure Data Factory. In contrast, Structured Streaming operates at a lower level, where developers must manually handle orchestration, retries, and dependencies between streaming jobs. Both support Delta Lake outputs and schema evolution; however, Lakeflow Declarative Pipelines simplify management by declaratively defining transformations and data quality expectations. Hence, the correct distinction is A --- automated orchestration and management in Lakeflow Declarative Pipelines.
A user wants to use DLT expectations to validate that a derived table report contains all records from the source, included in the table validation_copy.
The user attempts and fails to accomplish this by adding an expectation to the report table definition.
Which approach would allow using DLT expectations to validate all expected records are present in this table?
To validate that all records from the source are included in the derived table, creating a view that performs a left outer join between the validation_copy table and the report table is effective. The view can highlight any discrepancies, such as null values in the report table's key columns, indicating missing records. This view can then be referenced in DLT (Delta Live Tables) expectations for the report table to ensure data integrity. This approach allows for a comprehensive comparison between the source and the derived table.
Databricks Documentation on Delta Live Tables and Expectations: Delta Live Tables Expectations
The data engineering team maintains the following code:
Assuming that this code produces logically correct results and the data in the source tables has been de-duplicated and validated, which statement describes what will occur when this code is executed?
This is the correct answer because it describes what will occur when this code is executed. The code uses three Delta Lake tables as input sources: accounts, orders, and order_items. These tables are joined together using SQL queries to create a view called new_enriched_itemized_orders_by_account, which contains information about each order item and its associated account details. Then, the code uses write.format(''delta'').mode(''overwrite'') to overwrite a target table called enriched_itemized_orders_by_account using the data from the view. This means that every time this code is executed, it will replace all existing data in the target table with new data based on the current valid version of data in each of the three input tables. Verified Reference: [Databricks Certified Data Engineer Professional], under ''Delta Lake'' section; Databricks Documentation, under ''Write to Delta tables'' section.
To reduce storage and compute costs, the data engineering team has been tasked with curating a series of aggregate tables leveraged by business intelligence dashboards, customer-facing applications, production machine learning models, and ad hoc analytical queries.
The data engineering team has been made aware of new requirements from a customer-facing application, which is the only downstream workload they manage entirely. As a result, an aggregate table used by numerous teams across the organization will need to have a number of fields renamed, and additional fields will also be added.
Which of the solutions addresses the situation while minimally interrupting other teams in the organization without increasing the number of tables that need to be managed?
This is the correct answer because it addresses the situation while minimally interrupting other teams in the organization without increasing the number of tables that need to be managed. The situation is that an aggregate table used by numerous teams across the organization will need to have a number of fields renamed, and additional fields will also be added, due to new requirements from a customer-facing application. By configuring a new table with all the requisite fields and new names and using this as the source for the customer-facing application, the data engineering team can meet the new requirements without affecting other teams that rely on the existing table schema and name. By creating a view that maintains the original data schema and table name by aliasing select fields from the new table, the data engineering team can also avoid duplicating data or creating additional tables that need to be managed. Verified Reference: [Databricks Certified Data Engineer Professional], under ''Lakehouse'' section; Databricks Documentation, under ''CREATE VIEW'' section.
- Select Question Types you want
- Set your Desired Pass Percentage
- Allocate Time (Hours : Minutes)
- Create Multiple Practice tests with Limited Questions
- Customer Support
Domitila
3 days agoCassi
11 days agoChau
19 days agoNadine
26 days agoSharee
1 month agoNiesha
1 month agoMary
2 months agoMing
2 months agoDante
2 months agoMargot
2 months agoLindsey
2 months agoRyan
2 months agoFernanda
4 months agoStacey
5 months agoRosann
5 months agoMarti
5 months agoEllen
6 months agoEmmett
6 months agoCherry
7 months agoAlana
7 months agoJovita
8 months agoBeatriz
8 months agoLeslie
8 months agoMichael
9 months agoLaurena
9 months agoRemedios
9 months agoDana
10 months agoBrittni
10 months agoLaurel
10 months agoNidia
10 months agoLezlie
11 months agoDana
11 months agoRenato
11 months agoYaeko
11 months agoDean
12 months agoSon
12 months agoAlex
12 months agoEffie
1 year agoMaybelle
1 year agoStefany
1 year agoHeike
1 year agoGearldine
1 year agoMisty
1 year agoCharlesetta
1 year agoAlesia
1 year agoAretha
1 year agoGary
1 year agoMozell
1 year agoSharen
1 year agoIsabella
1 year agoSheridan
1 year agoAdolph
1 year agoJaime
1 year agoElmira
1 year agoJesusita
1 year agoRichelle
1 year agoDenny
1 year agoAlysa
1 year agoHerman
2 years agoThad
2 years ago