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Databricks Certified Professional Data Engineer Exam Databricks-Certified-Professional-Data-Engineer Prüfungsfragen mit Lösungen (Q112-Q117):

112. Frage
A query is taking too long to run. After investigating the Spark UI, the data engineer discovered a significant amount of disk spill. The compute instance being used has a core-to-memory ratio of 1:2.
What are the two steps the data engineer should take to minimize spillage? (Choose 2 answers)

A. Increase spark.sql.files.maxPartitionBytes.
B. Choose a compute instance with a higher core-to-memory ratio.
C. Reduce spark.sql.files.maxPartitionBytes.
D. Choose a compute instance with more disk space.
E. Choose a compute instance with more network bandwidth.

Antwort: B,C

Begründung:
Comprehensive and Detailed Explanation From Exact Extract of Databricks Data Engineer Documents:
Databricks recommends addressing disk spilling-which occurs when Spark tasks run out of memory-by increasing memory per core and controlling partition size. Selecting an instance type with a higher memory-to-core ratio (A) provides each task with more available RAM, directly reducing the chance of spilling to disk. Additionally, reducing spark.sql.files.maxPartitionBytes (D) creates smaller partitions, preventing any single task from holding too much data in memory. Increasing partition size (C) or disk capacity (B) does not solve memory bottlenecks, and bandwidth (E) affects network I/O, not spill behavior. Therefore, the correct actions are A and D.

113. Frage
Which statement describes Delta Lake Auto Compaction?

A. An asynchronous job runs after the write completes to detect if files could be further compacted; if yes, an optimize job is executed toward a default of 1 GB.
B. Data is queued in a messaging bus instead of committing data directly to memory; all data is committed from the messaging bus in one batch once the job is complete.
C. Optimized writes use logical partitions instead of directory partitions; because partition boundaries are only represented in metadata, fewer small files are written.
D. Before a Jobs cluster terminates, optimize is executed on all tables modified during the most recent job.
E. An asynchronous job runs after the write completes to detect if files could be further compacted; if yes, an optimize job is executed toward a default of 128 MB.

Antwort: E

Begründung:
Explanation
This is the correct answer because it describes the behavior of Delta Lake Auto Compaction, which is a feature that automatically optimizes the layout of Delta Lake tables by coalescing small files into larger ones. Auto Compaction runs as an asynchronous job after a write to a table has succeeded and checks if files within a partition can be further compacted. If yes, it runs an optimize job with a default target file size of 128 MB.
Auto Compaction only compacts files that have not been compacted previously. Verified References:
[Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "Auto Compaction for Delta Lake on Databricks" section.
"Auto compaction occurs after a write to a table has succeeded and runs synchronously on the cluster that has performed the write. Auto compaction only compacts files that haven't been compacted previously."
https://learn.microsoft.com/en-us/azure/databricks/delta/tune-file-size

114. Frage
What is a method of installing a Python package scoped at the notebook level to all nodes in the currently active cluster?

A. Use &sh install in a notebook cell
B. Run source env/bin/activate in a notebook setup script
C. Install libraries from PyPi using the cluster UI
D. Use &Pip install in a notebook cell

Antwort: C

Begründung:
Installing a Python package scoped at the notebook level to all nodes in the currently active cluster in Databricks can be achieved by using the Libraries tab in the cluster UI. This interface allows you to install libraries across all nodes in the cluster. While the %pip command in a notebook cell would only affect the driver node, using the cluster UI ensures that the package is installed on all nodes.
Reference:
Databricks Documentation on Libraries: Libraries

115. Frage
Which configuration parameter directly affects the size of a spark-partition upon ingestion of data into Spark?

A. spark.sql.adaptive.advisoryPartitionSizeInBytes
B. spark.sql.files.openCostInBytes
C. spark.sql.autoBroadcastJoinThreshold
D. spark.sql.adaptive.coalescePartitions.minPartitionNum
E. spark.sql.files.maxPartitionBytes

Antwort: E

Begründung:
This is the correct answer because spark.sql.files.maxPartitionBytes is a configuration parameter that directly affects the size of a spark-partition upon ingestion of data into Spark. This parameter configures the maximum number of bytes to pack into a single partition when reading files from file-based sources such as Parquet, JSON and ORC. The default value is 128 MB, which means each partition will be roughly 128 MB in size, unless there are too many small files or only one large file. Verified Reference: [Databricks Certified Data Engineer Professional], under "Spark Configuration" section; Databricks Documentation, under "Available Properties - spark.sql.files.maxPartitionBytes" section.

116. Frage
A platform engineer is creating catalogs and schemas for the development team to use.
The engineer has created an initial catalog, catalog_A, and initial schema, schema_A. The engineer has also granted USE CATALOG, USE SCHEMA, and CREATE TABLE to the development team so that the engineer can begin populating the schema with new tables.
Despite being owner of the catalog and schema, the engineer noticed that they do not have access to the underlying tables in Schema_A.
What explains the engineer's lack of access to the underlying tables?

A. Permissions explicitly given by the table creator are the only way the Platform Engineer could access the underlying tables in their schema.
B. The platform engineer needs to execute a REFRESH statement as the table permissions did not automatically update for owners.
C. The owner of the schema does not automatically have permission to tables within the schema, but can grant them to themselves at anypoint.
D. Users granted with USE CATALOG can modify the owner's permissions to downstream tables.

Antwort: C

Begründung:
In Databricks, catalogs, schemas (or databases), and tables are managed through the Unity Catalog or Hive Metastore, depending on the environment. Permissions and ownership within these structures are governed by access control lists (ACLs).
* Catalog and Schema Ownership:When a platform engineer creates a catalog (such as catalog_A) and schema (such as schema_A), they automatically become the owner of those entities. This ownership gives them control over granting permissions for those entities (i.e., granting the USE CATALOG and USE SCHEMA privileges to others). However, ownership of the catalog or schema doesnot automaticallyextend to ownership or permission of individual tables within that schema.
* Table Permissions:For tables within a schema, the permission model is more granular. The table creator (i.e., whoever creates the table) is automatically assigned as the owner of that table. In this case, the platform engineer owns the schema but does not automatically inherit permissions to any table created within the schema unless explicitly granted by the table's owner or unless they grant permissions to themselves.
* Why the Engineer Lacks Access:The platform engineer notices that they do not have access to the underlying tables in schema_A despite being the owner of the schema. This occurs because the schema's ownership does not cascade to the tables. The engineer must either:
* Grant permissions to themselves for the tables in schema_A, or
* Be granted permissions by whoever created the tables within the schema.
* Resolution:As the owner of the schema, the platform engineer can easily grant themselves the required permissions (such as SELECT, INSERT, etc.) for the tables in the schema. This explains why the owner of a schema may not automatically have access to the tables and must take explicit steps to acquire those permissions.
References
* Databricks Unity Catalog Documentation: Manage Permissions
* [Databricks Permissions and Ownership](https://docs.databricks.com/security/access-control
/workspace-acl.html#permissions

117. Frage
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