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

Section	Objectives
Topic 1: Production Pipelines and Orchestration	- Build and manage workflows using Databricks Jobs - Automate ETL pipelines and scheduling - Pipeline reliability and fault tolerance
Topic 2: Security, Governance, Monitoring, and Optimization	- Cost optimization and performance tuning - Monitor and optimize Spark workloads - Implement Unity Catalog governance and access control
Topic 3: 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
Topic 4: Data Modeling and Storage	- Delta Lake table design and optimization - Design scalable data lakehouse architectures - Schema evolution and data partitioning strategies

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q216-Q221):

NEW QUESTION # 216
Spill occurs as a result of executing various wide transformations. However, diagnosing spill requires one to proactively look for key indicators.
Where in the Spark UI are two of the primary indicators that a partition is spilling to disk?

A. Executor's detail screen and Executor's log files
B. Stage's detail screen and Executor's files
C. Stage's detail screen and Query's detail screen
D. Driver's and Executor's log files

Answer: C

Explanation:
In Apache Spark's UI, indicators of data spilling to disk during the execution of wide transformations can be found in the Stage's detail screen and the Query's detail screen. These screens provide detailed metrics about each stage of a Spark job, including information about memory usage and spill data. If a task is spilling data to disk, it indicates that the data being processed exceeds the available memory, causing Spark to spill data to disk to free up memory. This is an important performance metric as excessive spill can significantly slow down the processing.
References:
* Apache Spark Monitoring and Instrumentation: Spark Monitoring Guide
* Spark UI Explained: Spark UI Documentation

NEW QUESTION # 217
The data engineer is using Spark's MEMORY_ONLY storage level.
Which indicators should the data engineer look for in the spark UI's Storage tab to signal that a cached table is not performing optimally?

A. The number of Cached Partitions> the number of Spark Partitions
B. Size on Disk is> 0
C. The RDD Block Name included the '' annotation signaling failure to cache
D. On Heap Memory Usage is within 75% of off Heap Memory usage

Answer: C

Explanation:
In the Spark UI's Storage tab, an indicator that a cached table is not performing optimally would be the presence of the _disk annotation in the RDD Block Name. This annotation indicates that some partitions of the cached data have been spilled to disk because there wasn't enough memory to hold them. This is suboptimal because accessing data from disk is much slower than from memory. The goal of caching is to keep data in memory for fast access, and a spill to disk means that this goal is not fully achieved.

NEW QUESTION # 218
When you drop a managed table using SQL syntax DROP TABLE table_name how does it impact metadata, history, and data stored in the table?

A. Drops table from meta store, drops metadata, history, and data in storage.
B. Drops table from meta store and data from storage but keeps metadata and history in storage
C. Drops table from meta store, meta data and history but keeps the data in storage
D. Drops table and history but keeps meta data and data in storage
E. Drops table but keeps meta data, history and data in storage

Answer: A

Explanation:
Explanation
For a managed table, a drop command will drop everything from metastore and storage.
See the below image to understand the differences between dropping an external table.
Diagram Description automatically generated

NEW QUESTION # 219
Which of the following commands can be used to query a delta table?

A. 1.%python
2.spark.sql("select * from table_name")
B. 1.%sql
2.Select * from table_name
C. 1.%python
2.execute.sql("select * from table")
D. 1.%python
2.delta.sql("select * from table")
E. Both A & B
(Correct)

Answer: E

Explanation:
Explanation
The answer is both options A and B
Options C and D are incorrect because there is no command in Spark called execute.sql or delta.sql

NEW QUESTION # 220
A table nameduser_ltvis being used to create a view that will be used by data analysts on various teams. Users in the workspace are configured into groups, which are used for setting up data access using ACLs.
Theuser_ltvtable has the following schema:
email STRING, age INT, ltv INT
The following view definition is executed:

An analyst who is not a member of the marketing group executes the following query:
SELECT * FROM email_ltv
Which statement describes the results returned by this query?

A. Only the email and ltv columns will be returned; the email column will contain the string
"REDACTED" in each row.
B. Three columns will be returned, but one column will be named "redacted" and contain only null values.
C. Only the email and itv columns will be returned; the email column will contain all null values.
D. The email and ltv columns will be returned with the values in user itv.
E. The email, age. and ltv columns will be returned with the values in user ltv.

Answer: A

Explanation:
Explanation
The code creates a view called email_ltv that selects the email and ltv columns from a table called user_ltv, which has the following schema: email STRING, age INT, ltv INT. The code alsouses the CASE WHEN expression to replace the email values with the string "REDACTED" if the user is not a member of the marketing group. The user who executes the query is not a member of the marketing group, so they will only see the email and ltv columns, and the email column will contain the string "REDACTED" in each row.
Verified References: [Databricks Certified Data Engineer Professional], under "Lakehouse" section; Databricks Documentation, under "CASE expression" section.

NEW QUESTION # 221
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