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

Section	Weight	Objectives
Data Modeling and Storage	20%	- File Formats - Data Modeling - Storage Optimization
Data Quality and Governance	12%	- Data Lineage - Governance - Data Quality
Databricks Lakehouse Platform	24%	- Lakehouse Architecture - Delta Lake - Unity Catalog - Data Management
Monitoring and Troubleshooting	16%	- Monitoring - Troubleshooting - Performance Optimization
Data Processing	28%	- Spark SQL - Structured Streaming - ETL Pipelines - Data Transformation

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Databricks Certified Data Engineer Professional Exam Sample Questions (Q160-Q165):

NEW QUESTION # 160
A data engineer, User A, has promoted a new pipeline to production by using the REST API to programmatically create several jobs. A DevOps engineer, User B, has configured an external orchestration tool to trigger job runs through the REST API. Both users authorized the REST API calls using their personal access tokens.
Which statement describes the contents of the workspace audit logs concerning these events?

A. Because User A created the jobs, their identity will be associated with both the job creation events and the job run events.
B. Because the REST API was used for job creation and triggering runs, user identity will not be captured in the audit logs.
C. Because the REST API was used for job creation and triggering runs, a Service Principal will be automatically used to identity these events.
D. Because User B last configured the jobs, their identity will be associated with both the job creation events and the job run events.
E. Because these events are managed separately, User A will have their identity associated with the job creation events and User B will have their identity associated with the job run events.

Answer: E

Explanation:
The events are that a data engineer, User A, has promoted a new pipeline to production by using the REST API to programmatically create several jobs, and a DevOps engineer, User B, has configured an external orchestration tool to trigger job runs through the REST API. Both users authorized the REST API calls using their personal access tokens. The workspace audit logs are logs that record user activities in a Databricks workspace, such as creating, updating, or deleting objects like clusters, jobs, notebooks, or tables. The workspace audit logs also capture the identity of the user who performed each activity, as well as the time and details of the activity.
Because these events are managed separately, User A will have their identity associated with the job creation events and User B will have their identity associated with the job run events in the workspace audit logs.

NEW QUESTION # 161
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
Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from 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 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. 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.
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: C

NEW QUESTION # 162
While reviewing a query's execution in the Databricks Query Profiler, a data engineer observes that the Top Operators panel shows a Sort operator with high Time Spent and Memory Peak metrics. The Spark UI also reports frequent data spilling. How should the data engineer address this issue?

A. Switch to a broadcast join to reduce memory usage.
B. Increase the number of shuffle partitions to better distribute data.
C. Repartition the DataFrame to a single partition before sorting.
D. Convert the sort operation to a filter operation.

Answer: B

Explanation:
Increasing the number of shuffle partitions distributes the data across more tasks, reducing per- task memory pressure during the sort operation. This helps mitigate spilling by lowering memory peak usage per task and improves overall sort performance in large-scale distributed queries.

NEW QUESTION # 163
A CHECK constraint has been successfully added to the Delta table named activity_details using the following logic:

A batch job is attempting to insert new records to the table, including a record where latitude =
45.50 and longitude = 212.67.
Which statement describes the outcome of this batch insert?

A. The write will insert all records except those that violate the table constraints; the violating records will be recorded to a quarantine table.
B. The write will include all records in the target table; any violations will be indicated in the boolean column named valid_coordinates.
C. The write will fail completely because of the constraint violation and no records will be inserted into the target table.
D. The write will fail when the violating record is reached; any records previously processed will be recorded to the target table.
E. The write will insert all records except those that violate the table constraints; the violating records will be reported in a warning log.

Answer: C

Explanation:
The CHECK constraint is used to ensure that the data inserted into the table meets the specified conditions. In this case, the CHECK constraint is used to ensure that the latitude and longitude values are within the specified range. If the data does not meet the specified conditions, the write operation will fail completely and no records will be inserted into the target table. This is because Delta Lake supports ACID transactions, which means that either all the data is written or none of it is written. Therefore, the batch insert will fail when it encounters a record that violates the constraint, and the target table will not be updated.

NEW QUESTION # 164
A Delta Lake table was created with the below query:

Realizing that the original query had a typographical error, the below code was executed:
ALTER TABLE prod.sales_by_stor RENAME TO prod.sales_by_store
Which result will occur after running the second command?

A. The table reference in the metastore is updated and all data files are moved.
B. The table reference in the metastore is updated and no data is changed.
C. A new Delta transaction log Is created for the renamed table.
D. All related files and metadata are dropped and recreated in a single ACID transaction.
E. The table name change is recorded in the Delta transaction log.

Answer: B

Explanation:
The query uses the CREATE TABLE USING DELTA syntax to create a Delta Lake table from an existing Parquet file stored in DBFS. The query also uses the LOCATION keyword to specify the path to the Parquet file as /mnt/finance_eda_bucket/tx_sales.parquet. By using the LOCATION keyword, the query creates an external table, which is a table that is stored outside of the default warehouse directory and whose metadata is not managed by Databricks. An external table can be created from an existing directory in a cloud storage system, such as DBFS or S3, that contains data files in a supported format, such as Parquet or CSV.
The result that will occur after running the second command is that the table reference in the metastore is updated and no data is changed. The metastore is a service that stores metadata about tables, such as their schema, location, properties, and partitions. The metastore allows users to access tables using SQL commands or Spark APIs without knowing their physical location or format. When renaming an external table using the ALTER TABLE RENAME TO command, only the table reference in the metastore is updated with the new name; no data files or directories are moved or changed in the storage system. The table will still point to the same location and use the same format as before. However, if renaming a managed table, which is a table whose metadata and data are both managed by Databricks, both the table reference in the metastore and the data files in the default warehouse directory are moved and renamed accordingly.

NEW QUESTION # 165
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