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

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

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Databricks Certified Data Engineer Professional Exam 認定 Databricks-Certified-Data-Engineer-Professional 試験問題 (Q127-Q132):

質問 # 127
Assuming that the Databricks CLI has been installed and configured correctly, which Databricks CLI command can be used to upload a custom Python Wheel to object storage mounted with the DBFS for use with a production job?

A. jobs
B. fs
C. workspace
D. configure
E. libraries

正解：B

解説：
https://docs.databricks.com/en/archive/dev-tools/cli/dbfs-cli.html

質問 # 128
A Delta Lake table in the Lakehouse named customer_parsams is used in churn prediction by the machine learning team. The table contains information about customers derived from a number of upstream sources. Currently, the data engineering team populates this table nightly by overwriting the table with the current valid values derived from upstream data sources.
Immediately after each update succeeds, the data engineer team would like to determine the difference between the new version and the previous of the table. Given the current implementation, which method can be used?
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A. Execute a query to calculate the difference between the new version and the previous version using Delta Lake's built-in versioning and time travel functionality.
B. Execute DESCRIBE HISTORY customer_churn_params to obtain the full operation metrics for the update, including a log of all records that have been added or modified.
C. Parse the Spark event logs to identify those rows that were updated, inserted, or deleted.
D. Parse the Delta Lake transaction log to identify all newly written data files.

正解：A

解説：
Delta Lake provides built-in versioning and time travel capabilities, allowing users to query previous snapshots of a table. This feature is particularly useful for understanding changes between different versions of the table. In this scenario, where the table is overwritten nightly, you can use Delta Lake's time travel feature to execute a query comparing the latest version of the table (the current state) with its previous version. This approach effectively identifies the differences (such as new, updated, or deleted records) between the two versions. The other options do not provide a straightforward or efficient way to directly compare different versions of a Delta Lake table.

質問 # 129
Which distribution does Databricks support for installing custom Python code packages?

A. CRAM
B. CRAN
C. sbt
D. nom
E. Wheels
F. jars

正解：D

解説：
https://learn.microsoft.com/en-us/azure/databricks/workflows/jobs/how-to/use-python-wheels-in- workflows

質問 # 130
A data pipeline uses Structured Streaming to ingest data from kafka to Delta Lake. Data is being stored in a bronze table, and includes the Kafka_generated timesamp, key, and value. Three months after the pipeline is deployed the data engineering team has noticed some latency issued during certain times of the day.
A senior data engineer updates the Delta Table's schema and ingestion logic to include the current timestamp (as recoded by Apache Spark) as well the Kafka topic and partition. The team plans to use the additional metadata fields to diagnose the transient processing delays.
Which limitation will the team face while diagnosing this problem?

A. Updating the table schema will invalidate the Delta transaction log metadata.
B. New fields cannot be added to a production Delta table.
C. New fields will not be computed for historic records.
D. Spark cannot capture the topic partition fields from the kafka source.
E. Updating the table schema requires a default value provided for each file added.

正解：C

解説：
When adding new fields to a Delta table's schema, these fields will not be retrospectively applied to historical records that were ingested before the schema change. Consequently, while the team can use the new metadata fields to investigate transient processing delays moving forward, they will be unable to apply this diagnostic approach to past data that lacks these fields.

質問 # 131
The business reporting tem requires that data for their dashboards be updated every hour. The total processing time for the pipeline that extracts transforms and load 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 Structured Streaming job with a trigger interval of 60 minutes.
B. Schedule a job to execute the pipeline once an hour on a new job cluster.
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.

正解：B

解説：
Scheduling a job to execute the data processing pipeline once an hour on a new job cluster is the most cost-effective solution given the scenario. Job clusters are ephemeral in nature; they are spun up just before the job execution and terminated upon completion, which means you only incur costs for the time the cluster is active. Since the total processing time is only 10 minutes, a new job cluster created for each hourly execution minimizes the running time and thus the cost, while also fulfilling the requirement for hourly data updates for the business reporting team's dashboards.

質問 # 132
......

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