Exam Name: Databricks Certified Data Engineer Professional Exam
Certification Provider: Databricks
Corresponding Certification: Databricks Certification
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Databricks Databricks-Certified-Data-Engineer-Professional Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Data Ingestion and Processing | - Structured Streaming fundamentals - ETL pipeline design patterns - Batch and streaming ingestion with Auto Loader |
| Production Pipelines and Orchestration | - Job scheduling and monitoring - Databricks Workflows - Error handling and recovery strategies |
| Databricks Lakehouse Platform Architecture | - Workspace and cluster architecture - Data governance concepts (Unity Catalog basics) - Medallion architecture (Bronze, Silver, Gold) |
| Delta Lake and Data Management | - Time travel and versioning - Delta Lake transactions and ACID properties - Schema evolution and enforcement |
| Data Modeling and Transformation | - Spark SQL transformations - Dimensional modeling concepts - Performance optimization techniques |
Databricks Certified Data Engineer Professional Sample Questions:
The view updates represents an incremental batch of all newly ingested data to be inserted or updated in the customers table.
The following logic is used to process these records.
MERGE INTO customers
USING (
SELECT updates.customer_id as merge_ey, updates .*
FROM updates
UNION ALL
SELECT NULL as merge_key, updates .*
FROM updates JOIN customers
ON updates.customer_id = customers.customer_id
WHERE customers.current = true AND updates.address <> customers.address ) staged_updates ON customers.customer_id = mergekey WHEN MATCHED AND customers. current = true AND customers.address <> staged_updates.address THEN UPDATE SET current = false, end_date = staged_updates.effective_date WHEN NOT MATCHED THEN INSERT (customer_id, address, current, effective_date, end_date) VALUES (staged_updates.customer_id, staged_updates.address, true, staged_updates.effective_date, null) Which statement describes this implementation?
- A. The customers table is implemented as a Type 0 table; all writes are append only with no changes to existing values.
- B. The customers table is implemented as a Type 2 table; old values are overwritten and new customers are appended.
- C. The customers table is implemented as a Type 1 table; old values are overwritten by new values and no history is maintained.
- D. The customers table is implemented as a Type 2 table; old values are maintained but marked as no longer current and new values are inserted.
Correct Answer: D 🗳️
Explanation: Only visible for PassLeader members. You can sign-up / login (it's free).
A data engineer is setting up a pipeline to ingest data from a message bus system that occasionally delivers duplicate messages. The duplicate messages can be a week apart. The target is a Databricks Delta Lake table where each record should appear exactly once. Which Databricks ingestion pattern should be implemented to handle potential duplicates where events can arrive outside of the configured watermark?
- A. Use Delta Lake's change data feed to filter duplicate records
- B. Implement a write operation using MERGE INTO with a unique key
- C. Use Delta Lake time travel to identify and remove duplicates
- D. Configure Structured Streaming with dropDuplicates transformation
Correct Answer: B 🗳️
Explanation: Only visible for PassLeader members. You can sign-up / login (it's free).
A transactions table has been liquid clustered on the columns product_id, user_id, and event_date. Which operation lacks support for cluster on write?
- A. spark.write.format('delta').mode('append')
- B. spark.writestream.format('delta').mode('append')
- C. INSERT INTO operations
- D. CTAS and RTAS statements
Correct Answer: B 🗳️
Explanation: Only visible for PassLeader members. You can sign-up / login (it's free).
What is the first line of a Databricks Python notebook when viewed in a text editor?
- A. # MAGIC %python
- B. %python
- C. # Databricks notebook source
- D. // Databricks notebook source
- E. -- Databricks notebook source
Correct Answer: C 🗳️
Explanation: Only visible for PassLeader members. You can sign-up / login (it's free).
A Data Engineer is building a simple data pipeline using Lakeflow Declarative Pipelines (LDP) in Databricks to ingest customer data. The raw customer data is stored in a cloud storage location in JSON format. The task is to create Lakeflow Declarative Pipelines that read the raw JSON data and write it into a Delta table for further processing. Which code snippet will correctly ingest the raw JSON data and create a Delta table using LDP?
- A. import dlt
@dlt.table
def raw_customers():
return spark.read.format("csv").load("s3://my-bucket/raw-customers/") - B. import dlt
@dlt.table
def raw_customers():
return spark.read.json("s3://my-bucket/raw-customers/") - C. import dlt
@dlt.table
def raw_customers():
return spark.read.format("parquet").load("s3://my-bucket/raw-customers/") - D. import dlt
@dlt.view
def raw_customers():
return spark.format.json("s3://my-bucket/raw-customers/")
Correct Answer: B 🗳️
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