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

Section	Objectives
Data Ingestion and Processing	- Structured Streaming fundamentals - Batch and streaming ingestion with Auto Loader - ETL pipeline design patterns
Delta Lake and Data Management	- Schema evolution and enforcement - Time travel and versioning - Delta Lake transactions and ACID properties
Data Modeling and Transformation	- Spark SQL transformations - Performance optimization techniques - Dimensional modeling concepts
Databricks Lakehouse Platform Architecture	- Workspace and cluster architecture - Medallion architecture (Bronze, Silver, Gold) - Data governance concepts (Unity Catalog basics)
Production Pipelines and Orchestration	- Job scheduling and monitoring - Error handling and recovery strategies - Databricks Workflows

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最新的 Databricks Certification Databricks-Certified-Data-Engineer-Professional 免費考試真題 (Q231-Q236):

問題 #231
A nightly job ingests data into a Delta Lake table using the following code:

The next step in the pipeline requires a function that returns an object that can be used to manipulate new records that have not yet been processed to the next table in the pipeline.
Which code snippet completes this function definition?

A.
B. return spark.read.option("readChangeFeed", "true").table ("bronze")
C. return spark.readStream.table("bronze")
D. return spark.readStream.load("bronze")
E. def new_records():

答案：A

解題說明：
https://docs.databricks.com/en/delta/delta-change-data-feed.html
Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from

問題 #232
A developer has successfully configured their credentials for Databricks Repos and cloned a remote Git repository. They do not have privileges to make changes to the main branch, which is the only branch currently visible in their workspace. Which approach allows this user to share their code updates without the risk of overwriting the work of their teammates?

A. Use repos to merge all difference and make a pull request back to the remote repository.
B. Use Repos to pull changes from the remote Git repository; commit and push changes to a branch that appeared as changes were pulled.
C. Use Repos to create a new branch commit all changes and push changes to the remote Git repertory.
D. Use repos to create a fork of the remote repository commit all changes and make a pull request on the source repository
E. Use Repos to merge all differences and make a pull request back to the remote repository.

答案：C

解題說明：
In Databricks Repos, when a user does not have privileges to make changes directly to the main branch of a cloned remote Git repository, the recommended approach is to create a new branch within the Databricks workspace. The developer can then make changes in this new branch, commit those changes, and push the new branch to the remote Git repository. This workflow allows for isolated development without affecting the main branch, enabling the developer to propose changes via a pull request from the new branch to the main branch in the remote repository. This method adheres to common Git collaboration workflows, fostering code review and collaboration while ensuring the integrity of the main branch.

問題 #233
Which statement describes the correct use of pyspark.sql.functions.broadcast?

A. It marks a column as small enough to store in memory on all executors, allowing a broadcast join.
B. It marks a DataFrame as small enough to store in memory on all executors, allowing a broadcast join.
C. It caches a copy of the indicated table on attached storage volumes for all active clusters within a Databricks workspace.
D. It marks a column as having low enough cardinality to properly map distinct values to available partitions, allowing a broadcast join.
E. It caches a copy of the indicated table on all nodes in the cluster for use in all future queries during the cluster lifetime.

答案：B

解題說明：
https://spark.apache.org/docs/3.1.3/api/python/reference/api/pyspark.sql.functions.broadcast.html The broadcast function in PySpark is used in the context of joins. When you mark a DataFrame with broadcast, Spark tries to send this DataFrame to all worker nodes so that it can be joined with another DataFrame without shuffling the larger DataFrame across the nodes. This is particularly beneficial when the DataFrame is small enough to fit into the memory of each node. It helps to optimize the join process by reducing the amount of data that needs to be shuffled across the cluster, which can be a very expensive operation in terms of computation and time.
The pyspark.sql.functions.broadcast function in PySpark is used to hint to Spark that a DataFrame is small enough to be broadcast to all worker nodes in the cluster. When this hint is applied, Spark can perform a broadcast join, where the smaller DataFrame is sent to each executor only once and joined with the larger DataFrame on each executor. This can significantly reduce the amount of data shuffled across the network and can improve the performance of the join operation. In a broadcast join, the entire smaller DataFrame is sent to each executor, not just a specific column or a cached version on attached storage. This function is particularly useful when one of the DataFrames in a join operation is much smaller than the other, and can fit comfortably in the memory of each executor node.

問題 #234
A data engineering team needs to create a SQL Alert that monitors data quality across multiple columns in their customer table. They want to trigger an alert when both the percentage of customers with missing email addresses exceeds 15% AND the percentage of customers with invalid phone number formats exceeds 10%. Which SQL query pattern is appropriate for implementing this multi-column alert condition?

A. SELECT COUNT (*) FROM customers WHERE email IS NULL OR phone_format_invalid = true
B. SELECT CASE WHEN email_null_pct >15 AND phone_invalid_pct> 10 THEN 1 ELSE 0 END FROM (SELECT (COUNT (CASE WHEN email IS NULL THEN 1 END) * 100.0 / COUNT (*)) as phone_invalid_pct FROM customers) metrics
C. SELECT email, phone FROM customers WHERE email IS NULL AND phone NOT RLIKE ''[0-9-
+()\\s]+$'
D. SELECT email_null_pct, phone_invalid_pct FROM (SELECT (COUNT(CASE WHEN email IS NULL THEN 1 END) *
100.0/COUNT (*)) as email_null_pct, (COUNT(CASE WHEN phone NOT RLIKE ''[0-9-+()\\s]+$' THEN 1 END)*
100.0/COUNT (*)) as phone_invalid_pct FROM customers)

答案：D

解題說明：
This pattern computes independent percentage metrics for each data quality condition in a single aggregated query. By calculating the percentage of missing emails and invalid phone formats as separate columns, it enables the SQL Alert to evaluate a compound condition where both thresholds must be exceeded before triggering.

問題 #235
A new data engineer notices that a critical field was omitted from an application that writes its Kafka source to Delta Lake. This happened even though the critical field was in the Kafka source.
That field was further missing from data written to dependent, long-term storage. The retention threshold on the Kafka service is seven days. The pipeline has been in production for three months.
Which describes how Delta Lake can help to avoid data loss of this nature in the future?

A. The Delta log and Structured Streaming checkpoints record the full history of the Kafka producer.
B. Data can never be permanently dropped or deleted from Delta Lake, so data loss is not possible under any circumstance.
C. Delta Lake automatically checks that all fields present in the source data are included in the ingestion layer.
D. Delta Lake schema evolution can retroactively calculate the correct value for newly added fields, as long as the data was in the original source.
E. Ingestine all raw data and metadata from Kafka to a bronze Delta table creates a permanent, replayable history of the data state.

答案：E

解題說明：
This is the correct answer because it describes how Delta Lake can help to avoid data loss of this nature in the future. By ingesting all raw data and metadata from Kafka to a bronze Delta table, Delta Lake creates a permanent, replayable history of the data state that can be used for recovery or reprocessing in case of errors or omissions in downstream applications or pipelines.
Delta Lake also supports schema evolution, which allows adding new columns to existing tables without affecting existing queries or pipelines. Therefore, if a critical field was omitted from an application that writes its Kafka source to Delta Lake, it can be easily added later and the data can be reprocessed from the bronze table without losing any information.

問題 #236
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