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Passing the Databricks Certified Professional Data Engineer exam can be a significant achievement for individuals pursuing a career in big data and cloud computing. Databricks Certified Professional Data Engineer Exam certification demonstrates that the candidate has a deep understanding of Apache Spark and can apply that knowledge to design and implement scalable big data solutions. Additionally, this certification is recognized by many companies in the industry and can improve the candidate's chances of obtaining a high-paying job.
Databricks Certified Professional Data Engineer certification exam is a rigorous and challenging exam that requires a deep understanding of data engineering concepts and the Databricks platform. Candidates must have a strong foundation in computer science and data engineering, as well as practical experience using the Databricks platform. Databricks-Certified-Professional-Data-Engineer Exam consists of multiple-choice questions and hands-on exercises that test a candidate's ability to design, build, and maintain data pipelines using the Databricks platform.
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Databricks Certified Professional Data Engineer (DCPDE) is a certification program designed to validate the skills and knowledge of data professionals on the Databricks platform. Databricks Certified Professional Data Engineer Exam certification is aimed at professionals who design, build, and maintain data processing systems using Apache Spark and Databricks. The DCPDE certification demonstrates a comprehensive understanding of the Databricks platform and the ability to design and implement data processing solutions using Spark.
Databricks Certified Professional Data Engineer Exam Sample Questions (Q44-Q49):
NEW QUESTION # 44
You have written a notebook to generate a summary data set for reporting, Notebook was scheduled using the job cluster, but you realized it takes 8 minutes to start the cluster, what feature can be used to start the cluster in a timely fashion so your job can run immediatley?
- A. Disable auto termination so the cluster is always running
- B. Setup an additional job to run ahead of the actual job so the cluster is running second job starts
- C. Pin the cluster in the cluster UI page so it is always available to the jobs
- D. Use Databricks Premium edition instead of Databricks standard edition
- E. Use the Databricks cluster pools feature to reduce the startup time
Answer: E
Explanation:
Explanation
Cluster pools allow us to reserve VM's ahead of time, when a new job cluster is created VM are grabbed from the pool. Note: when the VM's are waiting to be used by the cluster only cost incurred is Azure. Databricks run time cost is only billed once VM is allocated to a cluster.
Here is a demo of how to setup a pool and follow some best practices,
Graphical user interface, text Description automatically generated
NEW QUESTION # 45
You are designing an analytical to store structured data from your e-commerce platform and un-structured data from website traffic and app store, how would you approach where you store this data?
- A. Use traditional data warehouse for structured data and use data lakehouse for un-structured data.
- B. Data lakehouse can only store unstructured data but cannot enforce a schema
- C. Traditional data warehouses are good for storing structured data and enforcing schema
- D. Data lakehouse can store structured and unstructured data and can enforce schema
Answer: D
Explanation:
Explanation
The answer is, Data lakehouse can store structured and unstructured data and can enforce schema What Is a Lakehouse? - The Databricks Blog Graphical user interface, text, application Description automatically generated
NEW QUESTION # 46
You are working on IOT data where each device has 5 reading in an array collected in Celsius, you were asked to covert each individual reading from Celsius to Fahrenheit, fill in the blank with an appropriate function that can be used in this scenario.
Schema: deviceId INT, deviceTemp ARRAY<double>
SELECT deviceId, __(deviceTempC,i-> (i * 9/5) + 32) as deviceTempF
FROM sensors
- A. FORALL
- B. ARRAYEXPR
- C. APPLY
- D. TRANSFORM
- E. MULTIPLY
Answer: D
Explanation:
Explanation
TRANSFORM -> Transforms elements in an array in expr using the function func.
1.transform(expr, func)
NEW QUESTION # 47
Which of the following describes a scenario in which a data engineer will want to use a Job cluster instead of
an all-purpose cluster?
- A. A data engineer needs to manually investigate a production error
- B. A Databricks SQL query needs to be scheduled for upward reporting
- C. An automated workflow needs to be run every 30 minutes
- D. An ad-hoc analytics report needs to be developed while minimizing compute costs
- E. A data team needs to collaborate on the development of a machine learning model
Answer: C
NEW QUESTION # 48
Which of the following programming languages can be used to build a Databricks SQL dashboard?
- A. R
- B. All of the above
- C. Scala
- D. Python
- E. SQL
Answer: E
NEW QUESTION # 49
......
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