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Databricks Databricks-Certified-Data-Engineer-Professional Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Data Ingestion and Processing | - Batch and streaming ingestion with Auto Loader - Structured Streaming fundamentals - ETL pipeline design patterns |
| Topic 2: Delta Lake and Data Management | - Schema evolution and enforcement - Delta Lake transactions and ACID properties - Time travel and versioning |
| Topic 3: Databricks Lakehouse Platform Architecture | - Medallion architecture (Bronze, Silver, Gold) - Workspace and cluster architecture - Data governance concepts (Unity Catalog basics) |
| Topic 4: Production Pipelines and Orchestration | - Databricks Workflows - Error handling and recovery strategies - Job scheduling and monitoring |
| Topic 5: Data Modeling and Transformation | - Dimensional modeling concepts - Spark SQL transformations - Performance optimization techniques |
Databricks Certified Data Engineer Professional Sample Questions:
A Spark job is taking longer than expected. Using the Spark UI, a data engineer notes that the Min, Median, and Max Durations for tasks in a particular stage show the minimum and median time to complete a task as roughly the same, but the max duration for a task to be roughly 100 times as long as the minimum.
Which situation is causing increased duration of the overall job?
- A. Task queueing resulting from improper thread pool assignment.
- B. Credential validation errors while pulling data from an external system.
- C. Spill resulting from attached volume storage being too small.
- D. Skew caused by more data being assigned to a subset of spark-partitions.
- E. Network latency due to some cluster nodes being in different regions from the source data
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Given the following PySpark code snippet in a Databricks notebook:
filtered_df = spark.read.format("delta").load("/mnt/data/large_table")
\
.filter("event_date > '2024-01-01'")
filtered_df.count()
The data engineer notices from the Query Profiler that the scan operator for filtered_df is reading almost all files, despite the filter being applied.
What is the probable reason for poor data skipping?
- A. The filter is executed only after the full data scan, preventing data skipping.
- B. The event_date column is outside the table's partitioning and Z-ordering scheme.
- C. The filter condition involves a data type excluded from data skipping support.
- D. The Delta table lacks optimization that enables dynamic file pruning.
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The data engineer team has been tasked with configured connections to an external database that does not have a supported native connector with Databricks. The external database already has data security configured by group membership. These groups map directly to user group already created in Databricks that represent various teams within the company. A new login credential has been created for each group in the external database. The Databricks Utilities Secrets module will be used to make these credentials available to Databricks users. Assuming that all the credentials are configured correctly on the external database and group membership is properly configured on Databricks, which statement describes how teams can be granted the minimum necessary access to using these credentials?
- A. "Read" permissions should be set on a secret scope containing only those credentials that will be used by a given team.
- B. No additional configuration is necessary as long as all users are configured as administrators in the workspace where secrets have been added.
- C. "Read'' permissions should be set on a secret key mapped to those credentials that will be used by a given team.
- D. "Manage" permission should be set on a secret scope containing only those credentials that will be used by a given team.
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What is a method of installing a Python package scoped at the notebook level to all nodes in the currently active cluster?
- A. Install libraries from PyPi using the cluster UI
- B. Run source env/bin/activate in a notebook setup script
- C. Use &sh install in a notebook cell
- D. Use &Pip install in a notebook cell
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A data governance team at a large enterprise is improving data discoverability across its organization. The team has hundreds of tables in their Databricks Lakehouse with thousands of columns that lack proper documentation. Many of these tables were created by different teams over several years, with missing context about column meanings and business logic. The data governance team needs to quickly generate comprehensive column descriptions for all existing tables to meet compliance requirements and improve data literacy across the organization. They want to leverage modern capabilities to automatically generate meaningful descriptions rather than manually documenting each column, which would take months to complete. Which approach should the team use in Databricks to automatically generate column comments and descriptions for existing tables?
- A. Navigate to the table in Databricks Catalog Explorer, select the table schema view, and use the AI Generate option which leverages artificial intelligence to automatically create meaningful column descriptions based on column names, data types, sample values, and data patterns.
- B. Use Delta Lake's DESCRIBE HISTORY command to analyze table evolution and infer column purposes from historical changes.
- C. Use the DESCRIBE TABLE command to extract existing schema information and manually write descriptions based on column names and data types.
- D. Write custom PySpark code using df.describe() and df.schema to programmatically generate basic statistical descriptions for each column.
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