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Databricks Certified Professional Data Engineer Exam Sample Questions (Q67-Q72):

NEW QUESTION # 67
A notebook accepts an input parameter that is assigned to a python variable called department and this is an optional parameter to the notebook, you are looking to control the flow of the code using this parameter. you have to check department variable is present then execute the code and if no department value is passed then skip the code execution. How do you achieve this using python?

A. 1.if department is not None:
2. #Execute code
3.then:
4. pass
B. 1.if department is None:
2. #Execute code
3.else:
4. pass
C. 1.if department is not None:
2. #Execute code
3.end:
4. pass
D. 1.if (department is not None)
2. #Execute code
3.else
4. pass
E. 1.if department is not None:
2. #Execute code
3.else:
4. pass
(Correct)

Answer: E

Explanation:
Explanation
The answer is,
1.if department is not None:
2. #Execute code
3.else:
4. pass

NEW QUESTION # 68
Which of the following features of data lakehouse can help you meet the needs of both workloads?

A. Data lakehouse combines compute and storage for simple governance.
B. Data lakehouse fully exists in the cloud.
C. Data lakehouse requires very little data modeling.
D. Data lakehouse can store unstructured data and support ACID transactions.
E. Data lakehouse provides autoscaling for compute clusters.

Answer: D

Explanation:
Explanation
The answer is A data lakehouse stores unstructured data and is ACID-compliant,

NEW QUESTION # 69
The data engineering team is using a bunch of SQL queries to review data quality and monitor the ETL job every day, which of the following approaches can be used to set up a schedule and auto-mate this process?

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

Answer: A

Explanation:
Explanation
Explanation
Individual queries can be refreshed on a schedule basis,
To set the schedule:
1. Click the query info tab.
Graphical user interface, text, application, email Description automatically generated

* Click the link to the right of Refresh Schedule to open a picker with schedule intervals.
Graphical user interface, application Description automatically generated

* Set the schedule.
The picker scrolls and allows you to choose:
* An interval: 1-30 minutes, 1-12 hours, 1 or 30 days, 1 or 2 weeks
* A time. The time selector displays in the picker only when the interval is greater than 1 day and the day selection is greater than 1 week. When you schedule a specific time, Databricks SQL takes input in your computer's timezone and converts it to UTC. If you want a query to run at a certain time in UTC, you must adjust the picker by your local offset. For example, if you want a query to execute at 00:00 UTC each day, but your current timezone is PDT (UTC-7), you should select 17:00 in the picker:
Graphical user interface Description automatically generated

* Click OK.
Your query will run automatically.
If you experience a scheduled query not executing according to its schedule, you should manually trigger the query to make sure it doesn't fail. However, you should be aware of the following:
* If you schedule an interval-for example, "every 15 minutes"-the interval is calculated from the last successful execution. If you manually execute a query, the scheduled query will not be executed until the interval has passed.
* If you schedule a time, Databricks SQL waits for the results to be "outdated". For example, if you have a query set to refresh every Thursday and you manually execute it on Wednesday, by Thursday the results will still be considered "valid", so the query wouldn't be scheduled for a new execution. Thus, for example, when setting a weekly schedule, check the last query execution time and expect the scheduled query to be executed on the selected day after that execution is a week old. Make sure not to manually execute the query during this time.
If a query execution fails, Databricks SQL retries with a back-off algorithm. The more failures the further away the next retry will be (and it might be beyond the refresh interval).
Refer documentation for additional info,
https://docs.microsoft.com/en-us/azure/databricks/sql/user/queries/schedule-query

NEW QUESTION # 70
A junior data engineer has been asked to develop a streaming data pipeline with a grouped aggregation using DataFramedf. The pipeline needs to calculate the average humidity and average temperature for each non-overlapping five-minute interval. Events are recorded once per minute per device.
Streaming DataFramedfhas 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. window("event_time", "10 minutes").alias("time")
B. window("event_time", "5 minutes").alias("time")
C. to_interval("event_time", "5 minutes").alias("time")
D. "event_time"
E. lag("event_time", "10 minutes").alias("time")

Answer: B

Explanation:
This is the correct answer because the window function is used to group streaming data by time intervals. The window function takes two arguments: a time column and a window duration. The window duration specifies how long each window is, and must be a multiple of 1 second. In this case, the window duration is "5 minutes", which means each window will cover a non-overlapping five-minute interval. The window function also returns a struct column with two fields: start and end, which represent the start and end time of each window. The alias function is used to rename the struct column as "time". Verified References: [Databricks Certified Data Engineer Professional], under "Structured Streaming" section; Databricks Documentation, under "WINDOW" section.https://www.databricks.com/blog/2017/05/08/event-time-aggregation-watermarking-apache-sparks-struc

NEW QUESTION # 71
Which of the following Auto loader structured streaming commands successfully performs a hop from the landing area into Bronze?

A. 1.spark
2..readStream
3..format("cloudFiles")
4..option("cloudFiles.format","csv")
5..option("cloudFiles.schemaLocation", checkpoint_directory)
6..load("landing")
7..writeStream.option("checkpointLocation", checkpoint_directory)
8..table(raw)
(Correct)
B. 1.spark
2..readStream
3..format("csv")
4..option("cloudFiles.schemaLocation", checkpoint_directory)
5..load("landing")
6..writeStream.option("checkpointLocation", checkpoint_directory)
7..table(raw)
C. 1.spark
2..read
3..load(rawSalesLocation)
4..writeStream
5..option("checkpointLocation", checkpointPath)
6..outputMode("append")
7..table("uncleanedSales")
D. 1.spark
2..readStream
3..load(rawSalesLocation)
4..writeStream
5..option("checkpointLocation", checkpointPath).outputMode("append")
6..table("uncleanedSales")
E. 1.spark
2..read
3..format("cloudFiles")
4..option("cloudFiles.format","csv")
5..option("cloudFiles.schemaLocation", checkpoint_directory)
6..load("landing")
7..writeStream.option("checkpointLocation", checkpoint_directory)
8..table(raw)

Answer: A

Explanation:
Explanation
The answer is
1.spark
2..readStream
3..format("cloudFiles") # use Auto loader
4..option("cloudFiles.format","csv") # csv format files
5..option("cloudFiles.schemaLocation", checkpoint_directory)
6..load('landing')
7..writeStream.option("checkpointLocation", checkpoint_directory)
8..table(raw)
Note: if you chose the below option which is incorrect because it does not have readStream
1.spark.read.format("cloudFiles")
2..option("cloudFiles.format","csv")
3....
4...
5...
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 # 72
......

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