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

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

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Databricks Certified Professional Data Engineer Exam 認定 Databricks-Certified-Professional-Data-Engineer 試験問題 (Q123-Q128):

質問 # 123
At the end of the inventory process a file gets uploaded to the cloud object storage, you are asked to build a process to ingest data which of the following method can be used to ingest the data incrementally, the schema of the file is expected to change overtime ingestion process should be able to handle these changes automatically. Below is the auto loader command to load the data, fill in the blanks for successful execution of the below code.
1.spark.readStream
2..format("cloudfiles")
3..option("cloudfiles.format","csv)
4..option("_______", 'dbfs:/location/checkpoint/')
5..load(data_source)
6..writeStream
7..option("_______",' dbfs:/location/checkpoint/')
8..option("mergeSchema", "true")
9..table(table_name))

A. schemalocation, checkpointlocation
B. checkpointlocation, schemalocation
C. cloudfiles.schemalocation, cloudfiles.checkpointlocation
D. cloudfiles.schemalocation, checkpointlocation
E. checkpointlocation, cloudfiles.schemalocation

正解：D

解説：
Explanation
The answer is cloudfiles.schemalocation, checkpointlocation
When reading the data cloudfiles.schemalocation is used to store the inferred schema of the incoming data.
When writing a stream to recover from failures checkpointlocation is used to store the offset of the byte that was most recently processed.

質問 # 124
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 different input the for loop.
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 table names as keys.
D. Load the configuration values for these tables from a separate file, located at a path provided by a pipeline parameter.

正解：C

解説：
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.

質問 # 125
You are currently looking at a table that contains data from an e-commerce platform, each row contains a list of items(Item number) that were present in the cart, when the customer makes a change to the cart the entire information is saved as a separate list and appended to an existing list for the duration of the customer session, to identify all the items customer bought you have to make a unique list of items, you were asked to create a unique item's list that was added to the cart by the user, fill in the blanks of below query by choosing the appropriate higher-order function?
Note: See below sample data and expected output.
Schema: cartId INT, items Array<INT>

Fill in the blanks:
Fill in the blanks:
SELECT cartId, _(_(items)) FROM carts

A. ARRAY_DISTINCT, FLATTEN
B. ARRAY_DISTINCT, ARRAY_UNION
C. ARRAY_DISTINCT, ARRAY_FLATTEN
D. FLATTEN, ARRAY_DISTINCT
E. ARRAY_UNION, ARRAY_DISCINT

正解：A

解説：
Explanation
FLATTEN -> Transforms an array of arrays into a single array.
ARRAY_DISTINCT -> The function returns an array of the same type as the input argument where all duplicate values have been removed.
Table Description automatically generated

質問 # 126
Which of the following commands will return records from an existing Delta table my_table where duplicates
have been removed?

A. 1. SELECT DISTINCT *
2. FROM my_table;
B. 1. DROP DUPLICATES
2. FROM my_table;
C. 1. MERGE INTO my_table a
2. USING new_records b ON a.id = b.id
3. WHEN NOT MATCHED
4. THEN INSERT *;
D. 1. SELECT *
2. FROM my_table
3. WHERE duplicate = False;
E. 1. MERGE INTO my_table a
2. USING new_records b;

正解：A

質問 # 127
Which of the following benefits does Delta Live Tables provide for ELT pipelines over standard data pipelines
that utilize Spark and Delta Lake on Databricks?

A. The ability to perform batch and streaming queries
B. The ability to access previous versions of data tables
C. The ability to write pipelines in Python and/or SQL
D. The ability to declare and maintain data table dependencies
E. The ability to automatically scale compute resources

正解：D

質問 # 128
......

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