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Databricks Databricks-Certified-Professional-Data-Engineer Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Data Ingestion & Acquisition	7%	- Auto Loader and streaming ingestion - Connecting to diverse data sources - Schema inference and evolution
Topic 2: Debugging and Deploying	10%	- CI/CD and DevOps practices - Troubleshooting pipelines and errors - Deployment using bundles, CLI, and APIs
Topic 3: Data Sharing and Federation	5%	- Unity Catalog data sharing - Cross-workspace and cross-cloud access
Topic 4: Data Transformation, Cleansing, and Quality	10%	- Standardization and normalization - Handling missing or inconsistent data - Data validation and quality checks
Topic 5: Data Governance	7%	- Data lineage and metadata tracking - Unity Catalog management - Policy enforcement
Topic 6: Monitoring and Alerting	10%	- Pipeline observability and logging - Setting up alerts and notifications - Performance and health monitoring
Topic 7: Cost & Performance Optimisation	13%	- Storage optimization (partitioning, Z-order, indexing) - Cluster configuration and scaling - Query optimization and caching
Topic 8: Ensuring Data Security and Compliance	10%	- Data encryption and masking - Compliance standards implementation - Access control and permissions
Topic 9: Data Modelling	6%	- Medallion Architecture implementation - Delta Lake table design - Schema design and management
Topic 10: Developing Code for Data Processing using Python and SQL	22%	- Data transformation and aggregation - Integration with Databricks APIs and tools - Batch and incremental processing logic

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최신 Databricks Certification Databricks-Certified-Professional-Data-Engineer 무료샘플문제 (Q49-Q54):

질문 # 49
A data engineer is optimizing a managed Delta table that suffers from data skew and frequently changing query filter columns. The engineer wants to avoid costly data rewrites when query patterns evolve. The table size is under 1 TB.
How should the data engineer meet this requirement?

A. Use Hive-style partitioning, as it provides efficient data skipping and is easy to change partition columns at any time.
B. Combine partitioning and Z-ordering to maximize flexibility and minimize maintenance as query patterns change.
C. Apply Z-ordering, since it allows flexible reorganization of data layout without rewriting existing files and adapts easily to new filter columns.
D. Enable liquid clustering, as it efficiently handles data skew, allows clustering keys to be changed without rewriting existing data, and adapts to evolving query patterns.

정답：D

설명：
Comprehensive and Detailed Explanation From Exact Extract of Databricks Data Engineer Documents:
The Databricks documentation describes Liquid Clustering as the recommended data layout optimization for evolving workloads. Unlike traditional partitioning or Z-ordering, Liquid Clustering dynamically maintains data organization without rewriting existing files when clustering keys change. It handles data skew automatically and supports flexible re-clustering based on query patterns. Partitioning and Z-ordering require full data rewrites whenever key structures change, making them expensive for tables with frequently evolving access patterns. For tables under a few terabytes, Liquid Clustering offers the best balance between scalability, adaptability, and maintenance efficiency. Thus, option C aligns with Databricks' best practice for modern adaptive layout optimization.

질문 # 50
A Delta Lake table representing metadata about content posts from users has the following schema:
user_id LONG, post_text STRING, post_id STRING, longitude FLOAT, latitude FLOAT, post_time TIMESTAMP, date DATE This table is partitioned by the date column. A query is run with the following filter:
longitude < 20 & longitude > -20
Which statement describes how data will be filtered?

A. Statistics in the Delta Log will be used to identify data files that might include records in the filtered range.
B. The Delta Engine will use row-level statistics in the transaction log to identify the flies that meet the filter criteria.
C. The Delta Engine will scan the parquet file footers to identify each row that meets the filter criteria.
D. No file skipping will occur because the optimizer does not know the relationship between the partition column and the longitude.
E. Statistics in the Delta Log will be used to identify partitions that might Include files in the filtered range.

정답：A

설명：
This is the correct answer because it describes how data will be filtered when a query is run with the following filter: longitude < 20 & longitude > -20. The query is run on a Delta Lake table that has the following schema:
user_id LONG, post_text STRING, post_id STRING, longitude FLOAT, latitude FLOAT, post_time TIMESTAMP, date DATE. This table is partitioned by the date column. When a query is run on a partitioned Delta Lake table, Delta Lake uses statistics in the Delta Log to identify data files that might include records in the filtered range. The statistics include information such as min and max values for each column in each data file. By using these statistics, Delta Lake can skip reading data files that do not match the filter condition, which can improve query performance and reduce I/O costs. Verified References: [Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "Data skipping" section.

질문 # 51
Which of the following features of data lakehouse can help you meet the needs of both workloads?

A. Data lakehouse requires very little data modeling.
B. Data lakehouse fully exists in the cloud.
C. Data lakehouse provides autoscaling for compute clusters.
D. Data lakehouse can store unstructured data and support ACID transactions.
E. Data lakehouse combines compute and storage for simple governance.

정답：D

설명：
Explanation
The answer is A data lakehouse stores unstructured data and is ACID-compliant,

질문 # 52
If E1 and E2 are two events, how do you represent the conditional probability given that E2 occurs given that
E1 has occurred?

A. P(E2)/(P(E1+E2)
B. P(E1)/P(E2)
C. P(E1+E2)/P(E1)
D. P(E2)/P(E1)

정답：D

질문 # 53
The business intelligence team has a dashboard configured to track various summary metrics for retail stories. This includes total sales for the previous day alongside totals and averages for a variety of time periods. The fields required to populate this dashboard have the following schema:

For Demand forecasting, the Lakehouse contains a validated table of all itemized sales updated incrementally in near real-time. This table named products_per_order, includes the following fields:

Because reporting on long-term sales trends is less volatile, analysts using the new dashboard only require data to be refreshed once daily. Because the dashboard will be queried interactively by many users throughout a normal business day, it should return results quickly and reduce total compute associated with each materialization.
Which solution meets the expectations of the end users while controlling and limiting possible costs?

A. Define a view against the products_per_order table and define the dashboard against this view.
B. Populate the dashboard by configuring a nightly batch job to save the required to quickly update the dashboard with each query.
C. Use Structure Streaming to configure a live dashboard against the products_per_order table within a Databricks notebook.
D. Use the Delta Cache to persists the products_per_order table in memory to quickly the dashboard with each query.

정답：B

질문 # 54
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