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

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

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q40-Q45):

NEW QUESTION # 40
You are currently working on a project that requires the use of SQL and Python in a given note-book, what would be your approach

A. Use an All-purpose cluster for python, SQL endpoint for SQL
B. A single notebook can support multiple languages, use the magic command to switch between the two.
C. Create two separate notebooks, one for SQL and the second for Python
D. Use job cluster to run python and SQL Endpoint for SQL

Answer: B

Explanation:
Explanation
The answer is, A single notebook can support multiple languages, use the magic command to switch between the two.
Use %sql and %python magic commands within the same notebook

NEW QUESTION # 41
Which of the following results in the creation of an external table?

A. CREATE TABLE transactions (id int, desc string) LOCATION '/mnt/delta/transactions'
B. CREATE TABLE transactions (id int, desc string)
C. CREATE EXTERNAL TABLE transactions (id int, desc string)
D. CREATE TABLE transactions (id int, desc string) USING DELTA LOCATION EX-TERNAL
E. CREATE TABLE transactions (id int, desc string) TYPE EXTERNAL

Answer: A

Explanation:
Explanation
Answer is CREATE TABLE transactions (id int, desc string) USING DELTA LOCATION
'/mnt/delta/transactions'
Anytime a table is created using Location it is considered an external table, below is the current syntax.
Syntax
CREATE TABLE table_name ( column column_data_type...) USING format LOCATION "dbfs:/"

NEW QUESTION # 42
All records from an Apache Kafka producer are being ingested into a single Delta Lake table with the following schema:
key BINARY, value BINARY, topic STRING, partition LONG, offset LONG, timestamp LONG There are 5 unique topics being ingested. Only the "registration" topic contains Personal Identifiable Information (PII). The company wishes to restrict access to PII. The company also wishes to only retain records containing PII in this table for 14 days after initial ingestion. However, for non-PII information, it would like to retain these records indefinitely.
Which of the following solutions meets the requirements?

A. Data should be partitioned by the registration field, allowing ACLs and delete statements to be set for the PII directory.
B. Separate object storage containers should be specified based on the partition field, allowing isolation at the storage level.
C. All data should be deleted biweekly; Delta Lake's time travel functionality should be leveraged to maintain a history of non-PII information.
D. Data should be partitioned by the topic field, allowing ACLs and delete statements to leverage partition boundaries.
E. Because the value field is stored as binary data, this information is not considered PII and no special precautions should be taken.

Answer: D

Explanation:
Partitioning the data by the topic field allows the company to apply different access control policies and retention policies for different topics. For example, the company can use the Table Access Control feature to grant or revoke permissions to the registration topic based on user roles or groups. The company can also use the DELETE command to remove records from the registration topic that are older than 14 days, while keeping the records from other topics indefinitely. Partitioning by the topic field also improves the performance of queries that filter by the topic field, as they can skip reading irrelevant partitions. References:
* Table Access Control: https://docs.databricks.com/security/access-control/table-acls/index.html
* DELETE: https://docs.databricks.com/delta/delta-update.html#delete-from-a-table

NEW QUESTION # 43
Delete records from the transactions Delta table where transactionDate is greater than current timestamp?

A. DELETE FROM transactions where transactionDate > current_timestamp() KEEP_HISTORY
B. DELETE FROM transactions where transactionDate > current_timestamp()
C. DELETE FROM transactions FORMAT DELTA where transactionDate > cur-renct_timestmap()
D. DELETE FROM transactions if transctionDate > current_timestamp()
E. DELET FROM transactions where transactionDate GE current_timestamp()

Answer: B

NEW QUESTION # 44
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. 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.
B. 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.
C. 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.
D. 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.
E. 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.

Answer: A

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 # 45
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