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

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

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q25-Q30):

NEW QUESTION # 25
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 Structured Streaming read against the daily_store_sales table and use upsert logic to update results in the store_sales_summary table.
B. 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.
C. 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.
D. 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.
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 Reference: [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 # 26
A data engineer is configuring a pipeline that will potentially see late-arriving, duplicate records.
In addition to de-duplicating records within the batch, which of the following approaches allows the data engineer to deduplicate data against previously processed records as it is inserted into a Delta table?

A. Rely on Delta Lake schema enforcement to prevent duplicate records.
B. Perform an insert-only merge with a matching condition on a unique key.
C. Set the configuration delta.deduplicate = true.
D. VACUUM the Delta table after each batch completes.
E. Perform a full outer join on a unique key and overwrite existing data.

Answer: B

NEW QUESTION # 27
Which configuration parameter directly affects the size of a spark-partition upon ingestion of data into Spark?

A. spark.sql.adaptive.advisoryPartitionSizeInBytes
B. spark.sql.files.maxPartitionBytes
C. spark.sql.autoBroadcastJoinThreshold
D. spark.sql.adaptive.coalescePartitions.minPartitionNum
E. spark.sql.files.openCostInBytes

Answer: B

Explanation:
This is the correct answer because spark.sql.files.maxPartitionBytes is a configuration parameter that directly affects the size of a spark-partition upon ingestion of data into Spark. This parameter configures the maximum number of bytes to pack into a single partition when reading files from file-based sources such as Parquet, JSON and ORC. The default value is 128 MB, which means each partition will be roughly 128 MB in size, unless there are too many small files or only one large file. Verified References: [Databricks Certified Data Engineer Professional], under "Spark Configuration" section; Databricks Documentation, under "Available Properties - spark.sql.files.maxPartitionBytes" section.

NEW QUESTION # 28
The business reporting team requires that data for their dashboards be updated every hour. The total processing time for the pipeline that extracts, transforms, and loads the data for their pipeline runs in 10 minutes. Assuming normal operating conditions, which configuration will meet their service-level agreement requirements with the lowest cost?

A. Schedule a job to execute the pipeline once an hour on a new job cluster.
B. Schedule a Structured Streaming job with a trigger interval of 60 minutes.
C. Configure a job that executes every time new data lands in a given directory.
D. Schedule a job to execute the pipeline once an hour on a dedicated interactive cluster.

Answer: A

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
Exact extract: "Job clusters are created for a job run and terminate when the job completes." Exact extract: "All-purpose (interactive) clusters are intended for interactive development and collaboration." An hourly batch that completes in ~10 minutes is best run on ephemeral job clusters so compute exists only during execution and auto-terminates afterward, minimizing cost while meeting the hourly SLA. Interactive clusters persist (incurring idle cost). Structured Streaming for an hourly refresh adds continuous compute cost with no benefit here. Event-driven triggers are unnecessary for a simple hourly SLA.
Reference:

NEW QUESTION # 29
The following code has been migrated to a Databricks notebook from a legacy workload:

The code executes successfully and provides the logically correct results, however, it takes over 20 minutes to extract and load around 1 GB of data.
Which statement is a possible explanation for this behavior?

A. %sh executes shell code on the driver node. The code does not take advantage of the worker nodes or Databricks optimized Spark.
B. Python will always execute slower than Scala on Databricks. The run.py script should be refactored to Scala.
C. Instead of cloning, the code should use %sh pip install so that the Python code can get executed in parallel across all nodes in a cluster.
D. %sh does not distribute file moving operations; the final line of code should be updated to use %fs instead.
E. %sh triggers a cluster restart to collect and install Git. Most of the latency is related to cluster startup time.

Answer: A

Explanation:
Explanation
https://www.databricks.com/blog/2020/08/31/introducing-the-databricks-web-terminal.html The code is using %sh to execute shell code on the driver node. This means that the code is not taking advantage of the worker nodes or Databricks optimized Spark. This is why the code is taking longer to execute. A better approach would be to use Databricks libraries and APIs to read and write data from Git and DBFS, and to leverage the parallelism and performance of Spark. For example, you can use the Databricks Connect feature to run your Python code on a remote Databricks cluster, or you can use the Spark Git Connector to read data from Git repositories as Spark DataFrames.

NEW QUESTION # 30
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