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

Section	Objectives
Data Ingestion and Transformation	- Handle batch and streaming data pipelines - Transform and clean datasets using Spark SQL and DataFrame APIs - Ingest data using Apache Spark and Databricks
Production Pipelines and Orchestration	- Automate ETL pipelines and scheduling - Pipeline reliability and fault tolerance - Build and manage workflows using Databricks Jobs
Data Modeling and Storage	- Delta Lake table design and optimization - Schema evolution and data partitioning strategies - Design scalable data lakehouse architectures
Security, Governance, Monitoring, and Optimization	- Monitor and optimize Spark workloads - Cost optimization and performance tuning - Implement Unity Catalog governance and access control

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Databricks Certified Professional Data Engineer Exam 認定 Databricks-Certified-Professional-Data-Engineer 試験問題 (Q200-Q205):

質問 # 200
A query is taking too long to run. After investigating the Spark UI, the data engineer discovered a significant amount of disk spill. The compute instance being used has a core-to-memory ratio of 1:2.
What are the two steps the data engineer should take to minimize spillage? (Choose 2 answers)

A. Increase spark.sql.files.maxPartitionBytes.
B. Choose a compute instance with more disk space.
C. Reduce spark.sql.files.maxPartitionBytes.
D. Choose a compute instance with more network bandwidth.
E. Choose a compute instance with a higher core-to-memory ratio.

正解：C、E

解説：
Comprehensive and Detailed Explanation From Exact Extract of Databricks Data Engineer Documents:
Databricks recommends addressing disk spilling-which occurs when Spark tasks run out of memory-by increasing memory per core and controlling partition size. Selecting an instance type with a higher memory-to-core ratio (A) provides each task with more available RAM, directly reducing the chance of spilling to disk. Additionally, reducing spark.sql.files.maxPartitionBytes (D) creates smaller partitions, preventing any single task from holding too much data in memory. Increasing partition size (C) or disk capacity (B) does not solve memory bottlenecks, and bandwidth (E) affects network I/O, not spill behavior. Therefore, the correct actions are A and D.

質問 # 201
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 the date variable:

Assume that the fields customer_id and order_id serve 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 only contain unique records, but newly written records may have duplicates already present in the target table.
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; if existing records with the same key are present in the target table, these records will be overwritten.
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, the operation will tail.
E. Each write to the orders table will run deduplication over the union of new and existing records, ensuring no duplicate records are present.

正解：A

解説：
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 Reference: [Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "DROP DUPLICATES" section.

質問 # 202
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 2 table; old values are maintained but marked as no longer current and new values are inserted.
B. The customers table is implemented as a Type 2 table; old values are overwritten and new customers are appended.
C. The customers table is implemented as a Type 1 table; old values are overwritten by new values and no history is maintained.
D. The customers table is implemented as a Type 0 table; all writes are append only with no changes to existing values.

正解：A

解説：
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.
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 ).

質問 # 203
A data engineer needs to dynamically create a table name string using three Python varia-bles: region, store,
and year. An example of a table name is below when region = "nyc", store = "100", and year = "2021":
nyc100_sales_2021
Which of the following commands should the data engineer use to construct the table name in Py-thon?

A. "{region}+{store}+_sales_+{year}"
B. f"{region}+{store}+_sales_+{year}"
C. f"{region}{store}_sales_{year}"
D. "{region}{store}_sales_{year}"
E. "{region}+{store}+"_sales_"+{year}"

正解：C

質問 # 204
An hourly batch job is configured to ingest data files from a cloud object storage container where each batch represent all records produced by the source system in a given hour. The batch job to process these records into the Lakehouse is sufficiently delayed to ensure no late-arriving data is missed. Theuser_idfield represents a unique key for the data, which has the following schema:
user_id BIGINT, username STRING, user_utc STRING, user_region STRING, last_login BIGINT, auto_pay BOOLEAN, last_updated BIGINT New records are all ingested into a table namedaccount_historywhich maintains a full record of all data in the same schema as the source. The next table in the system is namedaccount_currentand is implemented as a Type 1 table representing the most recent value for each uniqueuser_id.
Assuming there are millions of user accounts and tens of thousands of records processed hourly, which implementation can be used to efficiently update the describedaccount_currenttable as part of each hourly batch job?

A. Overwrite the account current table with each batch using the results of a query against the account history table grouping by user id and filtering for the max value of last updated.
B. Use Auto Loader to subscribe to new files in the account history directory; configure a Structured Streaminq trigger once job to batch update newly detected files into the account current table.
C. Use Delta Lake version history to get the difference between the latest version of account history and one version prior, then write these records to account current.
D. Filter records in account history using the last updated field and the most recent hour processed, making sure to deduplicate on username; write a merge statement to update or insert the most recent value for each username.
E. Filter records in account history using the last updated field and the most recent hour processed, as well as the max last iogin by user id write a merge statement to update or insert the most recent value for each user id.

正解：E

解説：
This is the correct answer because it efficiently updates the account current table with only the most recent value for each user id. The code filters records in account history using the last updated field and the most recent hour processed, which means it will only process the latest batch of data. It also filters by the max last login by user id, which means it will only keep the most recent record for each user id within that batch. Then, it writes a merge statement to update or insert the most recent value for each user id into account current, which means it will perform an upsert operation based on the user id column. Verified References: [Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "Upsert into a table using merge" section.

質問 # 205
......

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