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| Certification Vendor: | Databricks |
|---|---|
| Exam Name: | Databricks Certified Professional Data Engineer Exam |
| Exam Number: | Databricks-Certified-Professional-Data-Engineer |
| Exam Duration: | 90 minutes |
| Available Languages: | English |
| Certificate Validity Period: | 2 years |
| Exam Price: | $200 USD |
| Exam Format: | Multiple Choice, Multiple Select |
| Passing Score: | 70% |
| Real Exam Qty: | 60 |
| Related Certifications: | Databricks Certified Associate Developer Databricks Certified Data Analyst Associate |
| Sample Questions: | Databricks Databricks-Certified-Professional-Data-Engineer Sample Questions |
| Exam Way: | Online proctored exam (Pearson VUE) |
| Pre Condition: | Recommended: 6+ months of hands-on experience with Databricks and data engineering concepts; familiarity with Python or Scala and SQL is strongly recommended |
| Official Syllabus URL: | https://www.databricks.com/learn/certification/professional-data-engineer |
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The Databricks Databricks-Certified-Professional-Data-Engineer Exam consists of multiple-choice questions and hands-on exercises designed to test the candidate's knowledge and skills in working with Databricks. Candidates who pass the exam will be awarded the Databricks Certified Professional Data Engineer certification, which is recognized by employers worldwide as a validation of the candidate's expertise and proficiency in building and maintaining data pipelines using Databricks. Overall, the Databricks Certified Professional Data Engineer certification exam is a valuable credential for anyone looking to advance their career in big data engineering and analytics.
NEW QUESTION # 55
The data engineering team has configured a Databricks SQL query and alert to monitor the values in a Delta Lake table. The recent_sensor_recordings table contains an identifying sensor_id alongside the timestamp and temperature for the most recent 5 minutes of recordings.
The below query is used to create the alert:
The query is set to refresh each minute and always completes in less than 10 seconds. The alert is set to trigger when mean (temperature) > 120. Notifications are triggered to be sent at most every 1 minute.
If this alert raises notifications for 3 consecutive minutes and then stops, which statement must be true?
Answer: A
Explanation:
This is the correct answer because the query is using a GROUP BY clause on the sensor_id column, which means it will calculate the mean temperature for each sensor separately. The alert will trigger when the mean temperature for any sensor is greater than 120, which means at least one sensor had an average temperature above 120 for three consecutive minutes. The alert will stop when the mean temperature for all sensors drops below 120. Verified Reference: [Databricks Certified Data Engineer Professional], under "SQL Analytics" section; Databricks Documentation, under "Alerts" section.
NEW QUESTION # 56
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?
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 # 57
A data engineer is creating a data ingestion pipeline to understand where customers are taking their rented bicycles during use. The engineer noticed that over time, data being transmitted from the bicycle sensors fails to include key details like latitude and longitude. Downstream analysts need both the clean records and the quarantined records available for separate processing.
The data engineer already has this code:
import dlt
from pyspark.sql.functions import expr
rules = {
" valid_lat " : " (lat IS NOT NULL) " ,
" valid_long " : " (long IS NOT NULL) "
}
quarantine_rules = " NOT(Databricks-Certified-Professional-Data-Engineer) " .format( " AND " .join(rules.values()))
@dlt.view
def raw_trips_data():
return spark.readStream.table( " ride_and_go.telemetry.trips " )
How should the data engineer meet the requirements to capture good and bad data?
Answer: A
Explanation:
Databricks documents a quarantine pattern for Lakeflow Spark Declarative Pipelines in which you create a dataset containing both valid and invalid rows, add an is_quarantined flag based on your rule set, and then use that dataset for separate downstream processing paths. The documented pattern uses a boolean quarantine expression and preserves all rows while tracking quality metrics through expectations. ( Databricks Documentation ) Option A is the only choice that matches that documented design: it keeps all records, marks invalid rows with is_quarantined , and applies expectations to capture data quality metrics. Option B drops invalid rows, which fails the requirement to keep quarantined records available. Option C captures only bad rows and loses the clean path. Option D also drops invalid rows and therefore does not preserve both good and quarantined records for separate downstream use. ( Databricks Documentation )
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NEW QUESTION # 58
A junior member of the data engineering team is exploring the language interoperability of Databricks notebooks. The intended outcome of the below code is to register a view of all sales that occurred in countries on the continent of Africa that appear in the geo_lookup table.
Before executing the code, running SHOW TABLES on the current database indicates the database contains only two tables: geo_lookup and sales.
Which statement correctly describes the outcome of executing these command cells in order in an interactive notebook?
Answer: A
Explanation:
This is the correct answer because Cmd 1 is written in Python and uses a list comprehension to extract the country names from the geo_lookup table and store them in a Python variable named countries af. This variable will contain a list of strings, not a PySpark DataFrame or a SQL view. Cmd 2 is written in SQL and tries to create a view named sales af by selecting from the sales table where city is in countries af. However, this command will fail because countries af is not a valid SQL entity and cannot be used in a SQL query. To fix this, a better approach would be to use spark.sql() to execute a SQL query in Python and pass the countries af variable as a parameter. Verified Reference: [Databricks Certified Data Engineer Professional], under "Language Interoperability" section; Databricks Documentation, under "Mix languages" section.
NEW QUESTION # 59
What is the best way to query external csv files located on DBFS Storage to inspect the data using SQL?
Answer: C
Explanation:
Explanation
Answer is, SELECT * FROM CSV. 'dbfs:/location/csv_files/'
you can query external files stored on the storage using below syntax
SELECT * FROM format.`/Location`
format - CSV, JSON, PARQUET, TEXT
NEW QUESTION # 60
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