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

Section	Weight	Objectives
Topic 1: Data Ingestion	15-20%	- Streaming ingestion 1. Kafka integration 2. Structured streaming fundamentals - Batch ingestion methods 1. Integration with external systems 2. Spark APIs for ingestion 3. DBR autoloader
Topic 2: Data Warehouse and Lakehouse Architecture	15-20%	- Lakehouse architecture principles 1. Data governance fundamentals 2. Differences between data lake, data warehouse, and lakehouse 3. Bronze, silver, gold data layers
Topic 3: Data Processing with Spark	25-30%	- Python and SQL for data engineering 1. Performance optimization techniques 2. Built-in and user-defined functions 3. Spark APIs in Python - Spark DataFrames and Spark SQL 1. DataFrame operations and transformations 2. Spark SQL queries and functions 3. Window functions
Topic 4: Delta Lake	20-25%	- Delta Lake operations 1. Schema evolution and enforcement 2. Delta Live Tables 3. Merge, update, delete operations - Delta Lake fundamentals 1. Optimize and Z-order 2. Time travel and data versioning 3. ACID transactions
Topic 5: Pipeline Development and Orchestration	10-15%	- Databricks workflows 1. Jobs and job scheduling 2. Task dependencies and orchestration 3. Monitoring and alerting

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Databricks Databricks-Certified-Professional-Data-Engineer Practice Exams Questions

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q93-Q98):

NEW QUESTION # 93
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 maintained but marked as no longer current and new values are inserted.
C. The customers table is implemented as a Type 2 table; old values are overwritten and new customers are appended.
D. The customers table is implemented as a Type 1 table; old values are overwritten by new values and no history is maintained.

Answer: B

Explanation:
The provided MERGE statement is a classic implementation of a Type 2 SCD in a data warehousing context. In this approach, historical data is preserved by keeping old records (marking them as not current) and adding new records for changes. Specifically, when a match is found and there's a change in the address, the existing record in the customers table is updated to mark it as no longer current (current = false), and an end date is assigned (end_date = staged_updates.effective_date). A new record for the customer is then inserted with the updated information, marked as current. This method ensures that the full history of changes to customer information is maintained in the table, allowing for time-based analysis of customer data.
Reference: Databricks documentation on implementing SCDs using Delta Lake and the MERGE statement (https://docs.databricks.com/delta/delta-update.html#upsert-into-a-table-using-merge).

NEW QUESTION # 94
The data engineering team maintains a table of aggregate statistics through batch nightly updates. This includes total sales for the previous day alongside totals and averages for a variety of time periods including the 7 previous days, year-to-date, and quarter-to-date. This table is named store_saies_summary and the schema is as follows:

The table daily_store_sales contains all the information needed to update store_sales_summary . The schema for this table is:
store_id INT, sales_date DATE, total_sales FLOAT
If daily_store_sales is implemented as a Type 1 table and the total_sales column might be adjusted after manual data auditing, which approach is the safest to generate accurate reports in the store_sales_summary table?

A. Implement the appropriate aggregate logic as a batch read against the daily_store_sales table and append new rows nightly to the store_sales_summary table.
B. Implement the appropriate aggregate logic as a Structured Streaming read against the daily_store_sales table and use upsert logic to update results in the store_sales_summary table.
C. Implement the appropriate aggregate logic as a batch read against the daily_store_sales table and use upsert logic to update results in the store_sales_summary table.
D. Implement the appropriate aggregate logic as a batch read against the daily_store_sales table and overwrite the store_sales_summary table with each Update.
E. Use Structured Streaming to subscribe to the change data feed for daily_store_sales and apply changes to the aggregates in the store_sales_summary table with each update.

Answer: E

Explanation:
The daily_store_sales table contains all the information needed to update store_sales_summary. The schema of the table is:
store_id INT, sales_date DATE, total_sales FLOAT
The daily_store_sales table is implemented as a Type 1 table, which means that old values are overwritten by new values and no history is maintained. The total_sales column might be adjusted after manual data auditing, which means that the data in the table may change over time.
The safest approach to generate accurate reports in the store_sales_summary table is to use Structured Streaming to subscribe to the change data feed for daily_store_sales and apply changes to the aggregates in the store_sales_summary table with each update. Structured Streaming is a scalable and fault-tolerant stream processing engine built on Spark SQL. Structured Streaming allows processing data streams as if they were tables or DataFrames, using familiar operations such as select, filter, groupBy, or join. Structured Streaming also supports output modes that specify how to write the results of a streaming query to a sink, such as append, update, or complete. Structured Streaming can handle both streaming and batch data sources in a unified manner.
The change data feed is a feature of Delta Lake that provides structured streaming sources that can subscribe to changes made to a Delta Lake table. The change data feed captures both data changes and schema changes as ordered events that can be processed by downstream applications or services. The change data feed can be configured with different options, such as starting from a specific version or timestamp, filtering by operation type or partition values, or excluding no-op changes.
By using Structured Streaming to subscribe to the change data feed for daily_store_sales, one can capture and process any changes made to the total_sales column due to manual data auditing. By applying these changes to the aggregates in the store_sales_summary table with each update, one can ensure that the reports are always consistent and accurate with the latest data. Verified References: [Databricks Certified Data Engineer Professional], under "Spark Core" section; Databricks Documentation, under "Structured Streaming" section; Databricks Documentation, under "Delta Change Data Feed" section.

NEW QUESTION # 95
What is true for Delta Lake?

A. Views in the Lakehouse maintain a valid cache of the most recent versions of source tables at all times.
B. Delta Lake automatically collects statistics on the first 32 columns of each table, which are leveraged in data skipping based on query filters.
C. Z-ORDER can only be applied to numeric values stored in Delta Lake tables.
D. Primary and foreign key constraints can be leveraged to ensure duplicate values are never entered into a dimension table.

Answer: B

Explanation:
* Delta Lake automatically collects statistics on the first 32 columns of each table. These statistics help optimize query performance through data skipping, which allows Databricks to scan only relevant parts of a table.
* This feature significantly improves query efficiency, especially when dealing with large datasets.
Why Other Options Are Incorrect:
* Option A: Views do not cache the most recent versions of the source table; they are recomputed when queried.
* Option C: Z-ORDER can be applied to any data type, including strings, to optimize read performance.
* Option D: Delta Lake does not enforce primary or foreign key constraints.
Reference: Delta Lake Optimization

NEW QUESTION # 96
A data engineer is configuring Delta Sharing for a Databricks-to-Databricks scenario to optimize read performance. The recipient needs to perform time travel queries and streaming reads on shared sales data.
Which configuration will provide the optimal performance while enabling these capabilities?

A. Share tables WITHOUT HISTORY and enable partitioning for better query performance.
B. Share the entire schema WITHOUT HISTORY and rely on recipient-side caching for performance.
C. Share tables WITH HISTORY, ensure tables don't have partitioning enabled, and enable CDF before sharing.
D. Use the open sharing protocol instead of Databricks-to-Databricks sharing for better performance.

Answer: C

Explanation:
Comprehensive and Detailed Explanation From Exact Extract of Databricks Data Engineer Documents:
The official Delta Sharing guidance specifies that in order for recipients to use time travel queries and streaming reads, providers must share Delta tables WITH HISTORY. Sharing history ensures the Delta log is included, which enables efficient access to table snapshots and incremental data streams. Additionally, Change Data Feed (CDF) must be enabled prior to sharing if downstream consumers require streaming CDC queries. Without history, recipients cannot perform time travel or streaming queries. Open sharing supports static Delta tables but lacks streaming support. Therefore, sharing tables WITH HISTORY and enabling CDF is the required configuration for both performance and functionality.

NEW QUESTION # 97
Create a schema called bronze using location '/mnt/delta/bronze', and check if the schema exists before creating.

A. if IS_SCHEMA('bronze'): CREATE SCHEMA bronze LOCATION '/mnt/delta/bronze'
B. CREATE SCHEMA IF NOT EXISTS bronze LOCATION '/mnt/delta/bronze'
C. Cannot create schema without a database
D. Schema creation is not available in metastore, it can only be done in Unity catalog UI
E. CREATE SCHEMA bronze IF NOT EXISTS LOCATION '/mnt/delta/bronze'

Answer: B

Explanation:
Explanation
https://docs.databricks.com/sql/language-manual/sql-ref-syntax-ddl-create-schema.html
1.CREATE SCHEMA [ IF NOT EXISTS ] schema_name [ LOCATION schema_directory ]

NEW QUESTION # 98
......

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