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

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

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최신 Databricks Certification Databricks-Certified-Professional-Data-Engineer 무료샘플문제 (Q114-Q119):

질문 # 114
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?

A. Define a temporary table that perform a left outer join on validation_copy and report, and define an expectation that no report key values are null
B. Define a SQL UDF that performs a left outer join on two tables, and check if this returns null values for report key values in a DLT expectation for the report table.
C. Define a function that performs a left outer join on validation_copy and report and report, and check against the result in a DLT expectation for the report table
D. Define a view that performs a left outer join on validation_copy and report, and reference this view in DLT expectations for the report table

정답：D

설명：
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.
References:
* Databricks Documentation on Delta Live Tables and Expectations: Delta Live Tables Expectations

질문 # 115
A production workload incrementally applies updates from an external Change Data Capture feed to a Delta Lake table as an always-on Structured Stream job. When data was initially migrated for this table, OPTIMIZE was executed and most data files were resized to 1 GB. Auto Optimize and Auto Compaction were both turned on for the streaming production job. Recent review of data files shows that most data files are under 64 MB, although each partition in the table contains at least 1 GB of data and the total table size is over 10 TB.
Which of the following likely explains these smaller file sizes?

A. Databricks has autotuned to a smaller target file size based on the overall size of data in the table
B. Databricks has autotuned to a smaller target file size to reduce duration of MERGE operations
C. Databricks has autotuned to a smaller target file size based on the amount of data in each partition
D. Z-order indices calculated on the table are preventing file compaction C Bloom filler indices calculated on the table are preventing file compaction

정답：B

설명：
Explanation
This is the correct answer because Databricks has a feature called Auto Optimize, which automatically optimizes the layout of Delta Lake tables by coalescing small files into larger ones and sorting data within each file by a specified column. However, Auto Optimize also considers the trade-off between file size and merge performance, and may choose a smaller target file size to reduce the duration of merge operations, especially for streaming workloads that frequently update existing records. Therefore, it is possible that Auto Optimize has autotuned to a smaller target file size based on the characteristics of the streaming production job. Verified References: [Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "Auto Optimize" section.https://docs.databricks.com/en/delta/tune-file-size.html#autotune-table 'Autotune file size based on workload'

질문 # 116
What is the purpose of the bronze layer in a Multi-hop architecture?

A. Contains aggregated data that is to be consumed into Silver
B. Can be used to eliminate duplicate records
C. Provides efficient storage and querying of full unprocessed history of data
D. Perform data quality checks, corrupt data quarantined
E. Used as a data source for Machine learning applications.

정답：C

설명：
Explanation
The answer is Provides efficient storage and querying of full unprocessed history of data Medallion Architecture - Databricks Bronze Layer:
1.Raw copy of ingested data
2.Replaces traditional data lake
3.Provides efficient storage and querying of full, unprocessed history of data
4.No schema is applied at this layer
Exam focus: Please review the below image and understand the role of each layer(bronze, silver, gold) in medallion architecture, you will see varying questions targeting each layer and its purpose.
Sorry I had to add the watermark some people in Udemy are copying my content.

질문 # 117
A Delta Lake table with Change Data Feed (CDF) enabled in the Lakehouse named customer_churn_params is used in churn prediction by the machine learning team. The table contains information about customers derived from a number of upstream sources. Currently, the data engineering team populates this table nightly by overwriting the table with the current valid values derived from upstream data sources. The churn prediction model used by the ML team is fairly stable in production. The team is only interested in making predictions on records that have changed in the past 24 hours. Which approach would simplify the identification of these changed records?

A. Replace the current overwrite logic with a MERGE statement to modify only those records that have changed; write logic to make predictions on the changed records identified by the Change Data Feed.
B. Convert the batch job to a Structured Streaming job using the complete output mode; configure a Structured Streaming job to read from the customer_churn_params table and incrementally predict against the churn model.
C. Modify the overwrite logic to include a field populated by calling current_timestamp() as data are being written; use this field to identify records written on a particular date.
D. Apply the churn model to all rows in the customer_churn_params table, but implement logic to perform an upsert into the predictions table that ignores rows where predictions have not changed.

정답：A

설명：
Comprehensive and Detailed Explanation From Exact Extract:
Exact extract: "Change data feed (CDF) provides row-level change information for Delta tables." Exact extract: "Use table_changes to query the set of rows that were inserted, updated, or deleted between two versions (or timestamps)." Exact extract: "MERGE INTO updates and inserts only the rows that changed."

질문 # 118
A data engineer is attempting to execute the following PySpark code:
df = spark.read.table( " sales " )
result = df.groupBy( " region " ).agg(sum( " revenue " ))
However, upon inspecting the execution plan and profiling the Spark job, they observe excessive data shuffling during the aggregation phase.
Which technique should be applied to reduce shuffling during the groupBy aggregation operation?

A. Caching the DataFrame df.
B. Repartition by region before aggregation.
C. Use broadcast join.
D. Use coalesce() after the aggregation.

정답：B

설명：
Databricks documents that shuffle occurs when Spark redistributes data across partitions for grouping or joining. To optimize aggregation performance, repartitioning by the grouping key (region) ensures rows with the same key are co-located in the same partition, thus minimizing shuffle movement. Caching improves reuse of DataFrames but does not reduce shuffle volume. coalesce() reduces the number of partitions after computation and cannot prevent shuffle. Broadcast joins are unrelated to single-table aggregations. The recommended practice for reducing shuffle in aggregation is explicit repartitioning by the grouping column.

질문 # 119
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