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

Section	Weight	Objectives
Data Ingestion	15-20%	- Batch ingestion methods 1. DBR autoloader 2. Spark APIs for ingestion 3. Integration with external systems - Streaming ingestion 1. Kafka integration 2. Structured streaming fundamentals
Pipeline Development and Orchestration	10-15%	- Databricks workflows 1. Jobs and job scheduling 2. Task dependencies and orchestration 3. Monitoring and alerting
Delta Lake	20-25%	- Delta Lake fundamentals 1. Time travel and data versioning 2. ACID transactions 3. Optimize and Z-order - Delta Lake operations 1. Merge, update, delete operations 2. Schema evolution and enforcement 3. Delta Live Tables
Data Processing with Spark	25-30%	- Spark DataFrames and Spark SQL 1. DataFrame operations and transformations 2. Window functions 3. Spark SQL queries and functions - Python and SQL for data engineering 1. Built-in and user-defined functions 2. Spark APIs in Python 3. Performance optimization techniques
Data Warehouse and Lakehouse Architecture	15-20%	- Lakehouse architecture principles 1. Data governance fundamentals 2. Differences between data lake, data warehouse, and lakehouse 3. Bronze, silver, gold data layers

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Databricks Certified Professional Data Engineer Exam 認定 Databricks-Certified-Professional-Data-Engineer 試験問題 (Q83-Q88):

質問 # 83
Which statement regarding stream-static joins and static Delta tables is correct?

A. Each microbatch of a stream-static join will use the most recent version of the static Delta table as of the job's initialization.
B. Each microbatch of a stream-static join will use the most recent version of the static Delta table as of each microbatch.
C. Stream-static joins cannot use static Delta tables because of consistency issues.
D. The checkpoint directory will be used to track updates to the static Delta table.
E. The checkpoint directory will be used to track state information for the unique keys present in the join.

正解：B

解説：
Explanation
This is the correct answer because stream-static joins are supported by Structured Streaming when one of the tables is a static Delta table. A static Delta table is a Delta table that is not updated by any concurrent writes, such as appends or merges, during the execution of a streaming query. In this case, each microbatch of a stream-static join will use the most recent version of the static Delta table as of each microbatch, which means it will reflect any changes made to the static Delta table before the start of each microbatch. Verified References:[Databricks Certified Data Engineer Professional], under "Structured Streaming" section; Databricks Documentation, under "Stream and static joins" section.

質問 # 84
Which method can be used to determine the total wall-clock time it took to execute a query?

A. In the Spark UI, take the sum of all task durations that ran across all stages for all jobs associated with that query.
B. Open the Query Profiler associated with that query and use the Total wall-clock duration metric.
C. In the Spark UI, take the job duration of the longest-running job associated with that query.
D. Open the Query Profiler associated with that query and use the Aggregated task time metric.

正解：B

解説：
The Query Profiler in Databricks SQL and notebooks provides a detailed breakdown of query performance metrics. The "Total wall-clock duration" metric directly represents the total elapsed time from query start to completion, including all execution, planning, and waiting stages. In contrast, "Aggregated task time" reflects the cumulative duration across all parallel tasks, which does not equal the total elapsed wall time since tasks often run concurrently. Using job duration from Spark UI can underestimate or overestimate runtime when queries span multiple jobs. Therefore, the Query Profiler's total wall-clock duration is the officially documented method to determine actual query execution time.

質問 # 85
Define an external SQL table by connecting to a local instance of an SQLite database using JDBC

A. 1.CREATE TABLE users_jdbc
2.USING org.apache.spark.sql.jdbc.sqlite
3.OPTIONS (
4. url = "jdbc:/sqmple_db",
5. dbtable = "users"
6.)
B. 1.CREATE TABLE users_jdbc
2.USING SQLITE
3.OPTIONS (
4. url = "jdbc:/sqmple_db",
5. dbtable = "users"
6.)
C. 1.CREATE TABLE users_jdbc
2.USING SQL
3.URL = {server:"jdbc:/sqmple_db",dbtable: "users"}
D. 1.CREATE TABLE users_jdbc
2.USING SQL
3.OPTIONS (
4. url = "jdbc:sqlite:/sqmple_db",
5. dbtable = "users"
6.)
E. 1.CREATE TABLE users_jdbc
2.USING org.apache.spark.sql.jdbc
3.OPTIONS (
4. url = "jdbc:sqlite:/sqmple_db",
5. dbtable = "users"
6.)

正解：A

解説：
Explanation
The answer is,
1.CREATE TABLE users_jdbc
2.USING org.apache.spark.sql.jdbc
3.OPTIONS (
4. url = "jdbc:sqlite:/sqmple_db",
5. dbtable = "users"
6.)
Databricks runtime currently supports connecting to a few flavors of SQL Database including SQL Server, My SQL, SQL Lite and Snowflake using JDBC.
1.CREATE TABLE <jdbcTable>
2.USING org.apache.spark.sql.jdbc or JDBC
3.OPTIONS (
4. url = "jdbc:<databaseServerType>://<jdbcHostname>:<jdbcPort>",
5. dbtable " = <jdbcDatabase>.atable",
6. user = "<jdbcUsername>",
7. password = "<jdbcPassword>"
8.)
For more detailed documentation
SQL databases using JDBC - Azure Databricks | Microsoft Docs

質問 # 86
The data science team has created and logged a production using MLFlow. The model accepts a list of column names and returns a new column of type DOUBLE.
The following code correctly imports the production model, load the customer table containing the customer_id key column into a Dataframe, and defines the feature columns needed for the model.

Which code block will output DataFrame with the schema'' customer_id LONG, predictions DOUBLE''?

A. Df, map (lambda k:midel (x [columns]) ,select (''customer_id predictions'')
B. Df.apply(model, columns). Select (''customer_id, prediction''
C. Model, predict (df, columns)
D. Df. Select (''customer_id''.
Model (''columns) alias (''predictions'')

正解：C

解説：
Given the information that the model is registered with MLflow and assuming predict is the method used to apply the model to a set of columns, we use the model.predict() function to apply the model to the DataFrame df using the specified columns. The model.predict() function is designed to take in a DataFrame and a list of column names as arguments, applying the trained model to these features to produce a predictions column. When working with PySpark, this predictions column needs to be selected alongside the customer_id to create a new DataFrame with the schema customer_id LONG, predictions DOUBLE.
Reference:
MLflow documentation on using Python function models: https://www.mlflow.org/docs/latest/models.html#python-function-python PySpark MLlib documentation on model prediction: https://spark.apache.org/docs/latest/ml-pipeline.html#pipeline

質問 # 87
A Delta Lake table was created with the below query:

Consider the following query:
DROP TABLE prod.sales_by_store -
If this statement is executed by a workspace admin, which result will occur?

A. An error will occur because Delta Lake prevents the deletion of production data.
B. Data will be marked as deleted but still recoverable with Time Travel.
C. Nothing will occur until a COMMIT command is executed.
D. The table will be removed from the catalog but the data will remain in storage.
E. The table will be removed from the catalog and the data will be deleted.

正解：E

解説：
When a table is dropped in Delta Lake, the table is removed from the catalog and the data is deleted. This is because Delta Lake is a transactional storage layer that provides ACID guarantees. When a table is dropped, the transaction log is updated to reflect the deletion of the table and the data is deleted from the underlying storage. References:
* https://docs.databricks.com/delta/quick-start.html#drop-a-table
* https://docs.databricks.com/delta/delta-batch.html#drop-table

質問 # 88
......

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