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

Section	Weight	Objectives
Topic 1: Databricks Lakehouse Platform	24%	- Unity Catalog - Lakehouse Architecture - Delta Lake - Data Management
Topic 2: Data Quality and Governance	12%	- Data Lineage - Data Quality - Governance
Topic 3: Data Modeling and Storage	20%	- Storage Optimization - File Formats - Data Modeling
Topic 4: Data Processing	28%	- Structured Streaming - Spark SQL - Data Transformation - ETL Pipelines
Topic 5: Monitoring and Troubleshooting	16%	- Troubleshooting - Monitoring - Performance Optimization

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Databricks-Certified-Data-Engineer-Professional Fragen&Antworten - Databricks-Certified-Data-Engineer-Professional Prüfungsmaterialien

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Databricks Certified Data Engineer Professional Exam Databricks-Certified-Data-Engineer-Professional Prüfungsfragen mit Lösungen (Q216-Q221):

216. Frage
A junior data engineer is migrating a workload from a relational database system to the Databricks Lakehouse. The source system uses a star schema, leveraging foreign key constrains and multi-table inserts to validate records on write.
Which consideration will impact the decisions made by the engineer while migrating this workload?

A. Committing to multiple tables simultaneously requires taking out multiple table locks and can lead to a state of deadlock.
B. Foreign keys must reference a primary key field; multi-table inserts must leverage Delta Lake's upsert functionality.
C. All Delta Lake transactions are ACID compliance against a single table, and Databricks does not enforce foreign key constraints.
D. Databricks supports Spark SQL and JDBC; all logic can be directly migrated from the source system without refactoring.
E. Databricks only allows foreign key constraints on hashed identifiers, which avoid collisions in highly-parallel writes.

Antwort: C

217. Frage
Given the following PySpark code snippet in a Databricks notebook:
filtered_df = spark.read.format("delta").load("/mnt/data/large_table")
\
.filter("event_date > '2024-01-01'")
filtered_df.count()
The data engineer notices from the Query Profiler that the scan operator for filtered_df is reading almost all files, despite the filter being applied.
What is the probable reason for poor data skipping?

A. The filter condition involves a data type excluded from data skipping support.
B. The filter is executed only after the full data scan, preventing data skipping.
C. The Delta table lacks optimization that enables dynamic file pruning.
D. The event_date column is outside the table's partitioning and Z-ordering scheme.

Antwort: D

Begründung:
Delta Lake's data skipping relies on partitioning and clustering (such as Z-ordering) on the filtered columns. If event_date is neither a partition column nor included in the table's clustering strategy, Spark must scan most files because file-level statistics cannot be effectively used to prune irrelevant data.

218. Frage
The data engineer team has been tasked with configured connections to an external database that does not have a supported native connector with Databricks. The external database already has data security configured by group membership. These groups map directly to user group already created in Databricks that represent various teams within the company. A new login credential has been created for each group in the external database. The Databricks Utilities Secrets module will be used to make these credentials available to Databricks users. Assuming that all the credentials are configured correctly on the external database and group membership is properly configured on Databricks, which statement describes how teams can be granted the minimum necessary access to using these credentials?

A. "Read" permissions should be set on a secret scope containing only those credentials that will be used by a given team.
B. "Manage" permission should be set on a secret scope containing only those credentials that will be used by a given team.
C. No additional configuration is necessary as long as all users are configured as administrators in the workspace where secrets have been added.
D. "Read'' permissions should be set on a secret key mapped to those credentials that will be used by a given team.

Antwort: A

Begründung:
In Databricks, using the Secrets module allows for secure management of sensitive information such as database credentials. Granting 'Read' permissions on a secret key that maps to database credentials for a specific team ensures that only members of that team can access these credentials. This approach aligns with the principle of least privilege, granting users the minimum level of access required to perform their jobs, thus enhancing security.

219. Frage
A Spark job is taking longer than expected. Using the Spark UI, a data engineer notes that the Min, Median, and Max Durations for tasks in a particular stage show the minimum and median time to complete a task as roughly the same, but the max duration for a task to be roughly 100 times as long as the minimum.
Which situation is causing increased duration of the overall job?

A. Task queueing resulting from improper thread pool assignment.
B. Skew caused by more data being assigned to a subset of spark-partitions.
C. Credential validation errors while pulling data from an external system.
D. Network latency due to some cluster nodes being in different regions from the source data
E. Spill resulting from attached volume storage being too small.

Antwort: B

Begründung:
This is the correct answer because skew is a common situation that causes increased duration of the overall job. Skew occurs when some partitions have more data than others, resulting in uneven distribution of work among tasks and executors. Skew can be caused by various factors, such as skewed data distribution, improper partitioning strategy, or join operations with skewed keys. Skew can lead to performance issues such as long-running tasks, wasted resources, or even task failures due to memory or disk spills.

220. Frage
A data engineer is testing a collection of mathematical functions, one of which calculates the area under a curve as described by another function.
Which kind of the test does the above line exemplify?

A. End-to-end
B. Unit
C. Integration
D. Manual
E. functional

Antwort: B

Begründung:
A unit test is designed to verify the correctness of a small, isolated piece of code, typically a single function. Testing a mathematical function that calculates the area under a curve is an example of a unit test because it is testing a specific, individual function to ensure it operates as expected.

221. Frage
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