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. Events are recorded once per minute per device.
df has the following schema: device_id INT, event_time TIMESTAMP, temp FLOAT, humidity FLOAT Code block:
df.withWatermark("event_time", "10 minutes")
.groupBy(
________,
"device_id"
)
.agg(
avg("temp").alias("avg_temp"),
avg("humidity").alias("avg_humidity")
)
.writeStream
.format("delta")
.saveAsTable("sensor_avg")
Which line of code correctly fills in the blank within the code block to complete this task?

• A.window("event_time", "5 minutes").alias("time")
• B.to_interval("event_time", "5 minutes").alias("time")
• C."event_time"
• D.lag("event_time", "5 minutes").alias("time")

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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?

• A.The total average temperature across all sensors exceeded 120 on three consecutive executions of the query
• B.The recent_sensor_recordingstable was unresponsive for three consecutive runs of the query
• C.The source query failed to update properly for three consecutive minutes and then restarted
• D.The maximum temperature recording for at least one sensor exceeded 120 on three consecutive executions of the query
• E.The average temperature recordings for at least one sensor exceeded 120 on three consecutive executions of the query

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Define an external SQL table by connecting to a local instance of an SQLite database using JDBC

• A.1.CREATE TABLE users_jdbc
  2.USING SQLITE
  3.OPTIONS (
  4. url = "jdbc:/sqmple_db",
  5. dbtable = "users"
  6.)
• B.1.CREATE TABLE users_jdbc
  2.USING SQL
  3.URL = {server:"jdbc:/sqmple_db",dbtable: "users"}
• C.1.CREATE TABLE users_jdbc
  2.USING SQL
  3.OPTIONS (
  4. url = "jdbc:sqlite:/sqmple_db",
  5. dbtable = "users"
  6.)
• D.1.CREATE TABLE users_jdbc
  2.USING org.apache.spark.sql.jdbc.sqlite
  3.OPTIONS (
  4. url = "jdbc:/sqmple_db",
  5. dbtable = "users"
  6.)
• E.1.CREATE TABLE users_jdbc
  2.USING org.apache.spark.sql.jdbc
  3.OPTIONS (
  4. url = "jdbc:sqlite:/sqmple_db",
  5. dbtable = "users"
  6.)

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A particular job seems to be performing slower and slower over time, the team thinks this started to happen when a recent production change was implemented, you were asked to take look at the job history and see if we can identify trends and root cause, where in the workspace UI can you perform this analysis?

• A.Under jobs UI select the job you are interested, under runs we can see current active runs and last 60 days historical run
• B.Under jobs UI select the job cluster, under spark UI select the application job logs, then you can access last 60 day historical runs
• C.Under Workspace logs, select job logs and select the job you want to monitor to view the last 60 day historical runs
• D.Under Compute UI, select Job cluster and select the job cluster to see last 60 day his-torical runs
• E.Historical job runs can only be accessed by REST API

Correct Answer: A

Explanation
The answer is,
Under jobs UI select the job you are interested, under runs we can see current active runs and last 60 days historical run

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A data engineer is testing a collection of mathematical functions, one of which calculates the area under a curve as described by another function.
Which kind of the test does the above line exemplify?

• A.Integration
• B.Unit
• C.Manual
• D.functional

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