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Die Databricks Certified Professional Data Engineer-Prüfung ist eine praktische Prüfung, bei der der Kandidat eine Reihe von Aufgaben mit Databricks durchführen muss. Die Prüfung bewertet die Fähigkeit des Kandidaten, Datenpipelines zu entwerfen und umzusetzen, mit Datenquellen und -senken zu arbeiten und Transformationen mit Databricks durchzuführen. Die Prüfung testet auch die Fähigkeit des Kandidaten, Datenpipelines für Leistung und Zuverlässigkeit zu optimieren und abzustimmen.

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Databricks Certified Professional Data Engineer Exam Databricks-Certified-Professional-Data-Engineer Prüfungsfragen mit Lösungen (Q100-Q105):

100. Frage
A data engineer wants to reflector the following DLT code, which includes multiple definition with very similar code:

In an attempt to programmatically create these tables using a parameterized table definition, the data engineer writes the following code.

The pipeline runs an update with this refactored code, but generates a different DAG showing incorrect configuration values for tables.
How can the data engineer fix this?

A. Convert the list of configuration values to a dictionary of table settings, using table names as keys.
B. Wrap the loop inside another table definition, using generalized names and properties to replace with those from the inner table
C. Convert the list of configuration values to a dictionary of table settings, using different input the for loop.
D. Load the configuration values for these tables from a separate file, located at a path provided by a pipeline parameter.

Antwort: A

Begründung:
The issue with the refactored code is that it tries to use string interpolation to dynamically create table names within the dlc.table decorator, which will not correctly interpret the table names. Instead, by using a dictionary with table names as keys and their configurations as values, the data engineer can iterate over the dictionary items and use the keys (table names) to properly configure the table settings. This way, the decorator can correctly recognize each table name, and the corresponding configuration settings can be applied appropriately.

101. Frage
When using the complete mode to write stream data, how does it impact the target table?

A. Target table is overwritten for each batch
B. Entire stream waits for complete data to write
C. Stream must complete to write the data
D. Delta commits transaction once the stream is stopped
E. Target table cannot be updated while stream is pending

Antwort: A

Begründung:
Explanation
The answer is Target table is overwritten for each batch
Complete mode - The whole Result Table will be outputted to the sink after every trigger. This is supported for aggregation queries

102. Frage
An upstream source writes Parquet data as hourly batches to directories named with the current date. A nightly batch job runs the following code to ingest all data from the previous day as indicated by thedatevariable:

Assume that the fieldscustomer_idandorder_idserve as a composite key to uniquely identify each order.
If the upstream system is known to occasionally produce duplicate entries for a single order hours apart, which statement is correct?

A. Each write to the orders table will run deduplication over the union of new and existing records, ensuring no duplicate records are present.
B. Each write to the orders table will only contain unique records, and only those records without duplicates in the target table will be written.
C. Each write to the orders table will only contain unique records, but newly written records may have duplicates already present in the target table.
D. Each write to the orders table will only contain unique records; if existing records with the same key are present in the target table, these records will be overwritten.
E. Each write to the orders table will only contain unique records; if existing records with the same key are present in the target table, the operation will tail.

Antwort: C

Begründung:
This is the correct answer because the code uses the dropDuplicates method to remove any duplicate records within each batch of data before writing to the orders table. However, this method does not check for duplicates across different batches or in the target table, so it is possible that newly written records may have duplicates already present in the target table. To avoid this, a better approach would be to use Delta Lake and perform an upsert operation using mergeInto. Verified References: [Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "DROP DUPLICATES" section.

103. Frage
A data engineer is testing a collection of mathematical functions, one of which calculates the area under a curve as described by another function.
Which kind of the test does the above line exemplify?

A. functional
B. Integration
C. Manual
D. Unit

Antwort: D

Begründung:
A unit test is designed to verify the correctness of a small, isolated piece of code, typically a single function.
Testing a mathematical function that calculates the area under a curve is an example of a unit test because it is testing a specific, individual function to ensure it operates as expected.
References:
* Software Testing Fundamentals: Unit Testing

104. Frage
The data engineering team maintains the following code:

Assuming that this code produces logically correct results and the data in the source tables has been de- duplicated and validated, which statement describes what will occur when this code is executed?

A. An incremental job will detect if new rows have been written to any of the source tables; if new rows are detected, all results will be recalculated and used to overwrite the enriched_itemized_orders_by_account table.
B. A batch job will update the enriched_itemized_orders_by_account table, replacing only those rows that have different values than the current version of the table, using accountID as the primary key.
C. The enriched_itemized_orders_by_account table will be overwritten using the current valid version of data in each of the three tables referenced in the join logic.
D. An incremental job will leverage information in the state store to identify unjoined rows in the source tables and write these rows to the enriched_iteinized_orders_by_account table.
E. No computation will occur until enriched_itemized_orders_by_account is queried; upon query materialization, results will be calculated using the current valid version of data in each of the three tables referenced in the join logic.

Antwort: C

Begründung:
The provided PySpark code performs the following operations:
* Reads Data from silver_customer_sales Table:
* The code starts by accessing the silver_customer_sales table using the spark.table method.
* Groups Data by customer_id:
* The .groupBy( " customer_id " ) function groups the data based on the customer_id column.
* Aggregates Data:
* The .agg() function computes several aggregate metrics for each customer_id:
* F.min( " sale_date " ).alias( " first_transaction_date " ): Determines the earliest sale date for the customer.
* F.max( " sale_date " ).alias( " last_transaction_date " ): Determines the latest sale date for the customer.
* F.mean( " sale_total " ).alias( " average_sales " ): Calculates the average sale amount for the customer.
* F.countDistinct( " order_id " ).alias( " total_orders " ): Counts the number of unique orders placed by the customer.
* F.sum( " sale_total " ).alias( " lifetime_value " ): Calculates the total sales amount (lifetime value) for the customer.
* Writes Data to gold_customer_lifetime_sales_summary Table:
* The .write.mode( " overwrite " ).table( " gold_customer_lifetime_sales_summary " ) command writes the aggregated data to the gold_customer_lifetime_sales_summary table.
* The mode( " overwrite " ) specifies that the existing data in the
gold_customer_lifetime_sales_summary table will be completely replaced by the new aggregated data.
Conclusion:
When this code is executed, it reads all records from the silver_customer_sales table, performs the specified aggregations grouped by customer_id, and then overwrites the entire gold_customer_lifetime_sales_summary table with the aggregated results. Therefore, option D accurately describes this process: " The gold_customer_lifetime_sales_summary table will be overwritten by aggregated values calculated from all records in the silver_customer_sales table as a batch job. " References:
PySpark DataFrame groupBy
PySpark Basics

105. Frage
......

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Databricks-Certified-Professional-Data-Engineer Fragenpool - Databricks-Certified-Professional-Data-Engineer Zertifikatsdemo

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Echte und neueste Databricks-Certified-Professional-Data-Engineer Fragen und Antworten der Databricks Databricks-Certified-Professional-Data-Engineer Zertifizierungsprüfung

Databricks Certified Professional Data Engineer Exam Databricks-Certified-Professional-Data-Engineer Prüfungsfragen mit Lösungen (Q100-Q105):

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