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The Databricks Certified Professional Data Engineer exam is a valuable certification for professionals who want to showcase their expertise in big data processing using Databricks. The certification demonstrates that the candidate has the necessary skills and knowledge to design and implement scalable data pipelines using Databricks. The certification also provides a competitive advantage to professionals in the job market and opens up new career opportunities in the field of big data engineering.

The Databricks Certified Professional Data Engineer certification exam is a valuable certification for data professionals who want to demonstrate their skills in building and managing complex data solutions on the Databricks platform. The exam is designed to test the knowledge and skills required to design, implement, and manage data engineering workflows in a collaborative environment. Candidates who pass the exam will have a deep understanding of Databricks technologies and will be able to build and manage scalable and reliable data solutions.

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There is no doubt that we all dream of working for top companies around the globe. Some people make it through but some keep on thinking about how to break that glass. If you are among those who belong to the latter category, you should start the preparations for the Databricks-Certified-Professional-Data-Engineer Databricks Certified Professional Data Engineer Exam certification exam to improve your knowledge, expertise and crack even the toughest interview easily.

Databricks is an American-based technology company that develops software that manages big data via Apache Spark. Their software helps different organizations to process and analyze large data sets efficiently. Therefore, there is a need to certify professionals to handle these technologies. The Databricks Certified Professional Data Engineer (Databricks-Certified-Professional-Data-Engineer) Certification Exam is designed to validate the knowledge and proficiency of data engineers in designing and building effective and scalable data engineering solutions on the Databricks platform.

Databricks Certified Professional Data Engineer Exam Sample Questions (Q144-Q149):

NEW QUESTION # 144
What is the purpose of the silver layer in a Multi hop architecture?

A. Optimized query performance for business-critical dataB. Replaces a traditional data lakeC. Refined views with aggregated dataD. Eliminates duplicate data, quarantines bad dataE. Efficient storage and querying of full, unprocessed history of data

Answer: D

Explanation:
Explanation
Medallion Architecture - Databricks
Silver Layer:
1. Reduces data storage complexity, latency, and redundency
2. Optimizes ETL throughput and analytic query performance
3. Preserves grain of original data (without aggregation)
4. Eliminates duplicate records
5. production schema enforced
6. Data quality checks, quarantine corrupt data
Exam focus: Please review the below image and understand the role of each layer(bronze, silver, gold) in medallion architecture, you will see varying questions targeting each layer and its purpose.
Sorry I had to add the watermark some people in Udemy are copying my content.
A diagram of a house Description automatically generated with low confidence

NEW QUESTION # 145
A data engineering manager has noticed that each of the queries in a Databricks SQL dashboard takes a few
minutes to update when they manually click the "Refresh" button. They are curious why this might be
occurring, so a team member provides a variety of reasons on why the delay might be occurring.
Which of the following reasons fails to explain why the dashboard might be taking a few minutes to update?

A. The queries attached to the dashboard might take a few minutes to run under normal circumstancesB. The Job associated with updating the dashboard might be using a non-pooled endpointC. The queries attached to the dashboard might all be connected to their own, unstarted Databricks clustersD. The queries attached to the dashboard might first be checking to determine if new data is availableE. The SQL endpoint being used by each of the queries might need a few minutes to start up

Answer: B

NEW QUESTION # 146
Which of the following SQL keywords can be used to append new rows to an existing Delta table?

A. UPDATEB. INSERT INTOC. DELETED. UNIONE. COPY

Answer: B

NEW QUESTION # 147
At the end of the inventory process a file gets uploaded to the cloud object storage, you are asked to build a process to ingest data which of the following method can be used to ingest the data incrementally, the schema of the file is expected to change overtime ingestion process should be able to handle these changes automatically. Below is the auto loader command to load the data, fill in the blanks for successful execution of the below code.
1.spark.readStream
2..format("cloudfiles")
3..option("cloudfiles.format","csv)
4..option("_______", 'dbfs:/location/checkpoint/')
5..load(data_source)
6..writeStream
7..option("_______",' dbfs:/location/checkpoint/')
8..option("mergeSchema", "true")
9..table(table_name))

A. checkpointlocation, cloudfiles.schemalocationB. schemalocation, checkpointlocationC. checkpointlocation, schemalocationD. cloudfiles.schemalocation, cloudfiles.checkpointlocationE. cloudfiles.schemalocation, checkpointlocation

Answer: E

Explanation:
Explanation
The answer is cloudfiles.schemalocation, checkpointlocation
When reading the data cloudfiles.schemalocation is used to store the inferred schema of the incoming data.
When writing a stream to recover from failures checkpointlocation is used to store the offset of the byte that was most recently processed.

NEW QUESTION # 148
When working with AUTO LOADER you noticed that most of the columns that were inferred as part of loading are string data types including columns that were supposed to be integers, how can we fix this?

A. Provide schema hintsB. Provide the schema of the target table in the cloudfiles.schemalocationC. Correct the incoming data by explicitly casting the data typesD. Provide the schema of the source table in the cloudfiles.schemalocationE. Update the checkpoint location

Answer: A

Explanation:
Explanation
The answer is, Provide schema hints.
1.spark.readStream \
2.format("cloudFiles") \
3.option("cloudFiles.format", "csv") \
4.option("header", "true") \
5.option("cloudFiles.schemaLocation", schema_location) \
6.option("cloudFiles.schemaHints", "id int, description string")
7.load(raw_data_location)
8.writeStream \
9.option("checkpointLocation", checkpoint_location) \
10.start(target_delta_table_location)option("cloudFiles.schemaHints", "id int, description string")
# Here we are providing a hint that id column is int and the description is a string When cloudfiles.schemalocation is used to store the output of the schema inference during the load process, with schema hints you can enforce data types for known columns ahead of time.

NEW QUESTION # 149
......

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