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

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

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q53-Q58):

NEW QUESTION # 53
Which of the following Auto loader structured streaming commands successfully performs a hop from the landing area into Bronze?

A. 1.spark\
2..readStream\
3..format("cloudFiles")\
4..option("cloudFiles.format","csv")\
5..option("cloudFiles.schemaLocation", checkpoint_directory)\
6..load("landing")\
7..writeStream.option("checkpointLocation", checkpoint_directory)\
8..table(raw)
(Correct)
B. 1.spark\
2..read\
3..load(rawSalesLocation) \
4..writeStream\
5..option("checkpointLocation", checkpointPath) \
6..outputMode("append")\
7..table("uncleanedSales")
C. 1.spark\
2..read\
3..format("cloudFiles")\
4..option("cloudFiles.format","csv")\
5..option("cloudFiles.schemaLocation", checkpoint_directory)\
6..load("landing")\
7..writeStream.option("checkpointLocation", checkpoint_directory)\
8..table(raw)
D. 1.spark\
2..readStream\
3..format("csv")\
4..option("cloudFiles.schemaLocation", checkpoint_directory)\
5..load("landing")\
6..writeStream.option("checkpointLocation", checkpoint_directory)\
7..table(raw)
E. 1.spark\
2..readStream\
3..load(rawSalesLocation)\
4..writeStream \
5..option("checkpointLocation", checkpointPath).outputMode("append")\
6..table("uncleanedSales")

Answer: A

Explanation:
Explanation
The answer is
1.spark\
2..readStream\
3..format("cloudFiles") \# use Auto loader
4..option("cloudFiles.format","csv") \ # csv format files
5..option("cloudFiles.schemaLocation", checkpoint_directory)\
6..load('landing')\
7..writeStream.option("checkpointLocation", checkpoint_directory)\
8..table(raw)
Note: if you chose the below option which is incorrect because it does not have readStream
1.spark.read.format("cloudFiles")
2..option("cloudFiles.format","csv")
3....
4...
5...
Exam focus: Please review the below image and understand the role of each layer(bronze, silver, gold) in medallion architecture, you will see varying questions targeting each layer and its purpose.
Sorry I had to add the watermark some people in Udemy are copying my content.
A diagram of a house Description automatically generated with low confidence

NEW QUESTION # 54
The data governance team is reviewing user for deleting records for compliance with GDPR. The following logic has been implemented to propagate deleted requests from the user_lookup table to the user aggregate table.

Assuming that user_id is a unique identifying key and that all users have requested deletion have been removed from the user_lookup table, which statement describes whether successfully executing the above logic guarantees that the records to be deleted from the user_aggregates table are no longer accessible and why?

A. No: the Delta Lake DELETE command only provides ACID guarantees when combined with the MERGE INTO command
B. No: the change data feed only tracks inserts and updates not deleted records.
C. Yes: Delta Lake ACID guarantees provide assurance that the DELETE command successed fully and permanently purged these records.
D. No: files containing deleted records may still be accessible with time travel until a BACUM command is used to remove invalidated data files.

Answer: D

Explanation:
The DELETE operation in Delta Lake is ACID compliant, which means that once the operation is successful, the records are logically removed from the table. However, the underlying files that contained these records may still exist and be accessible via time travel to older versions of the table. To ensure that these records are physically removed and compliance with GDPR is maintained, a VACUUM command should be used to clean up these data files after a certain retention period. The VACUUM command will remove the files from the storage layer, and after this, the records will no longer be accessible.

NEW QUESTION # 55
Two of the most common data locations on Databricks are the DBFS root storage and external object storage mounted with dbutils.fs.mount().
Which of the following statements is correct?

A. Neither the DBFS root nor mounted storage can be accessed when using %sh in a Databricks notebook.
B. By default, both the DBFS root and mounted data sources are only accessible to workspace administrators.
C. The DBFS root stores files in ephemeral block volumes attached to the driver, while mounted directories will always persist saved data to external storage between sessions.
D. The DBFS root is the most secure location to store data, because mounted storage volumes must have full public read and write permissions.
E. DBFS is a file system protocol that allows users to interact with files stored in object storage using syntax and guarantees similar to Unix file systems.

Answer: E

Explanation:
DBFS is a file system protocol that allows users to interact with files stored in object storage using syntax and guarantees similar to Unix file systems1. DBFS is not a physical file system, but a layer over the object storage that provides a unified view of data across different data sources1. By default, the DBFS root is accessible to all users in the workspace, and the access to mounted data sources depends on the permissions of the storage account or container2. Mounted storage volumes do not need to have full public read and write permissions, but they do require a valid connection string or access key to be provided when mounting3. Both the DBFS root and mounted storage can be accessed when using %sh in a Databricks notebook, as long as the cluster has FUSE enabled4. The DBFS root does not store files in ephemeral block volumes attached to the driver, but in the object storage associated with the workspace1. Mounted directories will persist saved data to external storage between sessions, unless they are unmounted or deleted3. Reference: DBFS, Work with files on Azure Databricks, Mounting cloud object storage on Azure Databricks, Access DBFS with FUSE

NEW QUESTION # 56
An external object storage container has been mounted to the location/mnt/finance_eda_bucket.
The following logic was executed to create a database for the finance team:

After the database was successfully created and permissions configured, a member of the finance team runs the following code:

If all users on the finance team are members of thefinancegroup, which statement describes how thetx_sales table will be created?

A. A managed table will be created in the DBFS root storage container.
B. An managed table will be created in the storage container mounted to /mnt/finance eda bucket.
C. An external table will be created in the storage container mounted to /mnt/finance eda bucket.
D. A logical table will persist the query plan to the Hive Metastore in the Databricks control plane.
E. A logical table will persist the physical plan to the Hive Metastore in the Databricks control plane.

Answer: B

Explanation:
https://docs.databricks.com/en/lakehouse/data-objects.html

NEW QUESTION # 57
A junior data engineer seeks to leverage Delta Lake's Change Data Feed functionality to create a Type 1 table representing all of the values that have ever been valid for all rows in abronzetable created with the propertydelta.enableChangeDataFeed = true. They plan to execute the following code as a daily job:

Which statement describes the execution and results of running the above query multiple times?

A. Each time the job is executed, only those records that have been inserted or updated since the last execution will be appended to the target table giving the desired result.
B. Each time the job is executed, the target table will be overwritten using the entire history of inserted or updated records, giving the desired result.
C. Each time the job is executed, newly updated records will be merged into the target table, overwriting previous values with the same primary keys.
D. Each time the job is executed, the differences between the original and current versions are calculated; this may result in duplicate entries for some records.
E. Each time the job is executed, the entire available history of inserted or updated records will be appended to the target table, resulting in many duplicate entries.

Answer: E

Explanation:
Reading table's changes, captured by CDF, using spark.read means that you are reading them as a static source. So, each time you run the query, all table's changes (starting from the specified startingVersion) will be read.

NEW QUESTION # 58
......

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