Databricks Databricks-Certified-Professional-Data-Engineer Zertifizierungsprüfung & Databricks-Certified-Professional-Data-Engineer Testengine

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

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

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Databricks-Certified-Professional-Data-Engineer Testengine & Databricks-Certified-Professional-Data-Engineer Examsfragen

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Databricks Certified Professional Data Engineer Exam Databricks-Certified-Professional-Data-Engineer Prüfungsfragen mit Lösungen (Q116-Q121):

116. Frage
Which of the following data workloads will utilize a Silver table as its source?

A. A job that ingests raw data from a streaming source into the Lakehouse
B. A job that cleans data by removing malformatted records
C. A job that queries aggregated data that already feeds into a dashboard
D. A job that aggregates cleaned data to create standard summary statistics
E. A job that enriches data by parsing its timestamps into a human-readable format

Antwort: D

117. Frage
An upstream system has been configured to pass the date for a given batch of data to the Databricks Jobs API as a parameter. The notebook to be scheduled will use this parameter to load data with the following code:
df = spark.read.format("parquet").load(f"/mnt/source/(date)")
Which code block should be used to create the date Python variable used in the above code block?

A. dbutils.widgets.text("date", "null") date = dbutils.widgets.get("date")
B. date = dbutils.notebooks.getParam("date")
C. date = spark.conf.get("date")
D. import sys
date = sys.argv[1]
E. input_dict = input()
date= input_dict["date"]

Antwort: A

Begründung:
Explanation
The code block that should be used to create the date Python variable used in the above code block is:
dbutils.widgets.text("date", "null") date = dbutils.widgets.get("date") This code block uses the dbutils.widgets API to create and get a text widget named "date" that can accept a string value as a parameter1. The default value of the widget is "null", which means that if no parameter is passed, the date variable will be "null". However, if a parameter is passed through the Databricks Jobs API, the date variable will be assigned the value of the parameter. For example, if the parameter is "2021-11-01", the date variable will be "2021-11-01". This way, the notebook can use the date variable to load data from the specified path.
The other options are not correct, because:
Option A is incorrect because spark.conf.get("date") is not a valid way to get a parameter passed through the Databricks Jobs API. The spark.conf API is used to get or set Spark configuration properties, not notebook parameters2.
Option B is incorrect because input() is not a valid way to get a parameter passed through the Databricks Jobs API. The input() function is used to get user input from the standard input stream, not from the API request3.
Option C is incorrect because sys.argv1 is not a valid way to get a parameter passed through the Databricks Jobs API. The sys.argv list is used to get the command-line arguments passed to a Python script, not to a notebook4.
Option D is incorrect because dbutils.notebooks.getParam("date") is not a valid way to get a parameter passed through the Databricks Jobs API. The dbutils.notebooks API is used to get or set notebook parameters when running a notebook as a job or as a subnotebook, not when passing parameters through the API5.
References: Widgets, Spark Configuration, input(), sys.argv, Notebooks

118. Frage
Which of the statements is correct when choosing between lakehouse and Datawarehouse?

A. Traditional Data warehouses are the preferred choice if we need to support ACID, Lakehouse does not support ACID.
B. Traditional Data warehouses can serve low query latency with high reliability for BI workloads
C. Traditional Data warehouses have special indexes which are optimized for Machine learning
D. Lakehouse replaces the current dependency on data lakes and data warehouses uses an open standard storage format and supports low latency BI workloads.
E. SQL support is only available for Traditional Datawarehouse's, Lakehouses support Python and Scala

Antwort: D

Begründung:
Explanation
The lakehouse replaces the current dependency on data lakes and data warehouses for modern data companies that desire:
* Open, direct access to data stored in standard data formats.
* Indexing protocols optimized for machine learning and data science.
* Low query latency and high reliability for BI and advanced analytics.

119. Frage
Which of the following locations hosts the driver and worker nodes of a Databricks-managed clus-ter?

A. Databricks web application
B. Control plane
C. JDBC data source
D. Databricks Filesystem
E. Data plane

Antwort: E

Begründung:
Explanation
The answer is Data Plane, which is where compute(all-purpose, Job Cluster, DLT) are stored this is generally a customer cloud account, there is one exception SQL Warehouses, currently there are 3 types of SQL Warehouse compute available(classic, pro, serverless), in classic and pro compute is located in customer cloud account but serverless computed is located in Databricks cloud account.
Diagram, timeline Description automatically generated

120. Frage
The following code has been migrated to a Databricks notebook from a legacy workload:

The code executes successfully and provides the logically correct results, however, it takes over 20 minutes to extract and load around 1 GB of data.
Which statement is a possible explanation for this behavior?

A. %sh does not distribute file moving operations; the final line of code should be updated to use %fs instead.
B. %sh executes shell code on the driver node. The code does not take advantage of the worker nodes or Databricks optimized Spark.
C. %sh triggers a cluster restart to collect and install Git. Most of the latency is related to cluster startup time.
D. Python will always execute slower than Scala on Databricks. The run.py script should be refactored to Scala.
E. Instead of cloning, the code should use %sh pip install so that the Python code can get executed in parallel across all nodes in a cluster.

Antwort: B

Begründung:
https://www.databricks.com/blog/2020/08/31/introducing-the-databricks-web-terminal.html The code is using %sh to execute shell code on the driver node. This means that the code is not taking advantage of the worker nodes or Databricks optimized Spark. This is why the code is taking longer to execute. A better approach would be to use Databricks libraries and APIs to read and write data from Git and DBFS, and to leverage the parallelism and performance of Spark. For example, you can use the Databricks Connect feature to run your Python code on a remote Databricks cluster, or you can use the Spark Git Connector to read data from Git repositories as Spark DataFrames.

121. Frage
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