DATABRICKS-CERTIFIED-PROFESSIONAL-DATA-ENGINEER RELIABLE TEST LABS & DATABRICKS-CERTIFIED-PROFESSIONAL-DATA-ENGINEER VCE TORRENT

Databricks-Certified-Professional-Data-Engineer Reliable Test Labs & Databricks-Certified-Professional-Data-Engineer Vce Torrent

Databricks-Certified-Professional-Data-Engineer Reliable Test Labs & Databricks-Certified-Professional-Data-Engineer Vce Torrent

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Databricks is a platform that offers a cloud-based environment for data engineering, data science, and machine learning. It is designed to simplify data processing and analysis, allowing users to collaborate on projects, access pre-built libraries, and scale their workloads. To ensure that users have the necessary skills and knowledge to work with Databricks, the company offers a certification program. One of the certifications available is the Databricks-Certified-Professional-Data-Engineer (Databricks Certified Professional Data Engineer) certification exam.

To prepare for the Databricks Certified Professional Data Engineer exam, candidates can take advantage of the resources provided by Databricks. The company offers training courses, certification study guides, and practice exams to help candidates prepare for the exam. These resources provide an overview of the exam topics and offer hands-on experience with the Databricks platform.

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q37-Q42):

NEW QUESTION # 37
Which of the following SQL statements can replace a python variable, when the notebook is set in SQL mode
1.table_name = "sales"
2.schema_name = "bronze"

  • A. spark.sql(f"SELECT * FROM {schema_name}.{table_name}")
  • B. spark.sql(f"SELECT * FROM {schem_name.table_name}")
  • C. spark.sql(f"SELECT * FROM f{schema_name.table_name}")
  • D. spark.sql("SELECT * FROM schema_name.table_name")
  • E. spark.sql(f"SELECT * FROM ${schema_name}.${table_name}")

Answer: A

Explanation:
Explanation
The answer is spark.sql(f"SELECT * FROM {schema_name}.{table_name}")


NEW QUESTION # 38
Which of the following is a correct statement on how the data is organized in the storage when when managing a DELTA table?

  • A. All of the data is broken down into one or many parquet files, but the log file is stored as a single json file, and every transaction creates a new data file(s) and log file gets appended.
  • B. All of the data is broken down into one or many parquet files, log files are broken down into one or many JSON files, and each transaction creates a new data file(s) and log file.
    (Correct)
  • C. All of the data is stored into one parquet file, log files are broken down into one or many json files.
  • D. All of the data and log are stored in a single parquet file
  • E. All of the data is broken down into one or many parquet files, log file is removed once the transaction is committed.

Answer: B

Explanation:
Explanation
Answer is
All of the data is broken down into one or many parquet files, log files are broken down into one or many json files, and each transaction creates a new data file(s) and log file.
here is sample layout of how DELTA table might look,


NEW QUESTION # 39
Which one of the following is not a Databricks lakehouse object?

  • A. Tables
  • B. Catalog
  • C. Stored Procedures
  • D. Database/Schemas
  • E. Functions
  • F. Views

Answer: C

Explanation:
Explanation
The answer is, Stored Procedures.
Databricks lakehouse does not support stored procedures.


NEW QUESTION # 40
The data engineering team is looking to add a new column to the table, but the QA team would like to test the change before implementing in production, which of the below options allow you to quickly copy the table from Prod to the QA environment, modify and run the tests?

  • A. DEEP CLONE
  • B. SHADOW CLONE
  • C. METADATA CLONE
  • D. ZERO COPY CLONE
  • E. SHALLOW CLONE

Answer: E

Explanation:
Explanation
The answer is SHALLOW CLONE
SHALLOW CLONE If you wish to create a copy of a table quickly to test out applying changes without the risk of modifying the current table, SHALLOW CLONE can be a good option. Shallow clones just copy the Delta transaction logs, meaning that the data doesn't move so it can be very quick.
1.CREATE OR REPLACE TABLE {new_table_name} SHALLOW CLONE
{source_table_name}|[LOCATION path]
DEEP CLONE fully copies data and metadata from a source table to a target. This copy occurs incre-mentally, so executing this command again can sync changes from the source to the target location. It copies all of the data and transaction logs this can take a long time based on the size of the table.
1.CREATE OR REPLACE TABLE {new_table_name} DEEP CLONE {source_table_name}|[LOCATION path]


NEW QUESTION # 41
A junior data engineer has been asked to develop a streaming data pipeline with a grouped aggregation using DataFrame df. The pipeline needs to calculate the average humidity and average temperature for each non-overlapping five-minute interval. Incremental state information should be maintained for 10 minutes for late-arriving data.
Streaming DataFrame df has the following schema:
"device_id INT, event_time TIMESTAMP, temp FLOAT, humidity FLOAT"
Code block:
Choose the response that correctly fills in the blank within the code block to complete this task.

  • A. withWatermark("event_time", "10 minutes")
  • B. awaitArrival("event_time", "10 minutes")
  • C. slidingWindow("event_time", "10 minutes")
  • D. await("event_time + '10 minutes'")
  • E. delayWrite("event_time", "10 minutes")

Answer: A

Explanation:
The correct answer is A. withWatermark("event_time", "10 minutes"). This is because the question asks for incremental state information to be maintained for 10 minutes for late-arriving data. The withWatermark method is used to define the watermark for late data. The watermark is a timestamp column and a threshold that tells the system how long to wait for late data. In this case, the watermark is set to 10 minutes. The other options are incorrect because they are not valid methods or syntax for watermarking in Structured Streaming. References:
* Watermarking: https://docs.databricks.com/spark/latest/structured-streaming/watermarks.html
* Windowed aggregations:
https://docs.databricks.com/spark/latest/structured-streaming/window-operations.html


NEW QUESTION # 42
......

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