Databricks Databricks-Certified-Professional-Data-Engineer Certification Exam Questions in 3 User-Friendly Formats

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Databricks Databricks-Certified-Professional-Data-Engineer Exam Overview:

Certification Vendor:	Databricks
Exam Name:	Databricks Certified Professional Data Engineer Exam
Exam Number:	Databricks-Certified-Professional-Data-Engineer
Real Exam Qty:	60
Available Languages:	English
Related Certifications:	Databricks Certified Data Analyst Associate Databricks Certified Associate Developer
Exam Price:	$200 USD
Exam Duration:	90 minutes
Certificate Validity Period:	2 years
Passing Score:	70%
Exam Format:	Multiple Select, Multiple Choice
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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Databricks is a leading company in the field of data engineering and machine learning. The company offers a wide range of services and tools to help organizations manage and analyze their data more effectively. One of the key offerings from Databricks is the Databricks Certified Professional Data Engineer (Databricks-Certified-Professional-Data-Engineer) certification exam. Databricks-Certified-Professional-Data-Engineer Exam is designed to test the skills and knowledge of data engineers who work with Databricks.

Databricks Certified Professional Data Engineer Exam Sample Questions (Q114-Q119):

NEW QUESTION # 114
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. await("event_time + '10 minutes'")
B. awaitArrival("event_time", "10 minutes")
C. delayWrite("event_time", "10 minutes")
D. slidingWindow("event_time", "10 minutes")
E. withWatermark("event_time", "10 minutes")

Answer: E

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 # 115
A data team's Structured Streaming job is configured to calculate running aggregates for item sales to update a downstream marketing dashboard. The marketing team has introduced a new field to track the number of times this promotion code is used for each item. A junior data engineer suggests updating the existing query as follows: Note that proposed changes are in bold.

Which step must also be completed to put the proposed query into production?

A. Increase the shuffle partitions to account for additional aggregates
B. Specify a new checkpointlocation
C. Remove .option (mergeSchema', true') from the streaming write
D. Run REFRESH TABLE delta, /item_agg'

Answer: B

Explanation:
When introducing a new aggregation or a change in the logic of a Structured Streaming query, it is generally necessary to specify a new checkpoint location. This is because the checkpoint directory contains metadata about the offsets and the state of the aggregations of a streaming query. If the logic of the query changes, such as including a new aggregation field, the state information saved in the current checkpoint would not be compatible with the new logic, potentially leading to incorrect results or failures. Therefore, to accommodate the new field and ensure the streaming job has the correct starting point and state information for aggregations, a new checkpoint location should be specified.
Reference:
Databricks documentation on Structured Streaming: https://docs.databricks.com/spark/latest/structured-streaming/index.html Databricks documentation on streaming checkpoints: https://docs.databricks.com/spark/latest/structured-streaming/production.html#checkpointing

NEW QUESTION # 116
A data engineer is using a Databricks SQL query to monitor the performance of an ELT job. The ELT job is triggered by a specific number of input records being ready to process. The Databricks SQL query returns the number of minutes since the job's most recent runtime. Which of the following approaches can enable the data engineering team to be notified if the ELT job has not been run in an hour?

A. They can set up an Alert for the query to notify when the ELT job fails.
B. They can set up an Alert for the query to notify them if the returned value is greater than 60.
C. They can set up an Alert for the accompanying dashboard to notify them if the returned value is greater than 60.
D. They can set up an Alert for the accompanying dashboard to notify when it has not re-freshed in 60 minutes.
E. This type of alert is not possible in Databricks

Answer: B

Explanation:
Explanation
The answer is, They can set up an Alert for the query to notify them if the returned value is greater than 60.
The important thing to note here is that alert can only be setup on query not on the dashboard, query can return a value, which is used if alert can be triggered.

NEW QUESTION # 117
Which statement characterizes the general programming model used by Spark Structured Streaming?

A. Structured Streaming models new data arriving in a data stream as new rows appended to an unbounded table.
B. Structured Streaming is implemented as a messaging bus and is derived from Apache Kafka.
C. Structured Streaming relies on a distributed network of nodes that hold incremental state values for cached stages.
D. Structured Streaming uses specialized hardware and I/O streams to achieve sub-second latency for data transfer.
E. Structured Streaming leverages the parallel processing of GPUs to achieve highly parallel data throughput.

Answer: A

Explanation:
Explanation
This is the correct answer because it characterizes the general programming model used by Spark Structured Streaming, which is to treat a live data stream as a table that is being continuously appended. This leads to a new stream processing model that is very similar to a batch processing model, where users can express their streaming computation using the same Dataset/DataFrame API as they would use for static data. The Spark SQL engine will take care of running the streaming query incrementally and continuously and updating the final result as streaming data continues to arrive. Verified References: [Databricks Certified Data Engineer Professional], under "Structured Streaming" section; Databricks Documentation, under "Overview" section.

NEW QUESTION # 118
An engineering manager uses a Databricks SQL query to monitor their team's progress on fixes related to
customer-reported bugs. The manager checks the results of the query every day, but they are manually
rerunning the query each day and waiting for the results.
Which of the following approaches can the manager use to ensure the results of the query are up-dated each
day?

A. They can schedule the query to run every 1 day from the Jobs UI
B. They can schedule the query to refresh every 1 day from the query's page in Databricks SQL
C. They can schedule the query to refresh every 1 day from the SQL endpoint's page in Databricks SQL
D. They can schedule the query to refresh every 12 hours from the SQL endpoint's page in Databricks SQL
E. They can schedule the query to run every 12 hours from the Jobs UI

Answer: B

NEW QUESTION # 119
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