NEW DATABRICKS DATABRICKS-CERTIFIED-DATA-ENGINEER-PROFESSIONAL TEST PRICE | LATEST DATABRICKS-CERTIFIED-DATA-ENGINEER-PROFESSIONAL EXAM PATTERN

New Databricks Databricks-Certified-Data-Engineer-Professional Test Price | Latest Databricks-Certified-Data-Engineer-Professional Exam Pattern

New Databricks Databricks-Certified-Data-Engineer-Professional Test Price | Latest Databricks-Certified-Data-Engineer-Professional Exam Pattern

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Databricks Certified Data Engineer Professional Exam Sample Questions (Q12-Q17):

NEW QUESTION # 12
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. withWatermark("event_time", "10 minutes")
  • C. slidingWindow("event_time", "10 minutes")
  • D. delayWrite("event_time", "10 minutes")
  • E. awaitArrival("event_time", "10 minutes")

Answer: B

Explanation:
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.


NEW QUESTION # 13
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.
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Which statement correctly describes the outcome of executing these command cells in order in an interactive notebook?

  • A. Cmd 1 will succeed and Cmd 2 will fail, countries at will be a Python variable containing a list of strings.
  • B. Both commands will succeed. Executing show tables will show that countries at and sales at have been registered as views.
  • C. Cmd 1 will succeed and Cmd 2 will fail, countries at will be a Python variable representing a PySpark DataFrame.
  • D. Both commands will fail. No new variables, tables, or views will be created.
  • E. Cmd 1 will succeed. Cmd 2 will search all accessible databases for a table or view named countries af: if this entity exists, Cmd 2 will succeed.

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.


NEW QUESTION # 14
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 Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from 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 source query failed to update properly for three consecutive minutes and then restarted
  • C. The recent_sensor_recordingstable was unresponsive for three consecutive runs of the query
  • 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

Answer: E

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.


NEW QUESTION # 15
Which of the following is true of Delta Lake and the Lakehouse?

  • A. Views in the Lakehouse maintain a valid cache of the most recent versions of source tables at all times.
  • B. Primary and foreign key constraints can be leveraged to ensure duplicate values are never entered into a dimension table.
  • C. Delta Lake automatically collects statistics on the first 32 columns of each table which are leveraged in data skipping based on query filters.
  • D. Z-order can only be applied to numeric values stored in Delta Lake tables
  • E. Because Parquet compresses data row by row. strings will only be compressed when a character is repeated multiple times.

Answer: C

Explanation:
Delta Lake automatically collects statistics on the first 32 columns of each table, which are leveraged in data skipping based on query filters. Data skipping is a performance optimization technique that aims to avoid reading irrelevant data from the storage layer. By collecting statistics such as min/max values, null counts, and bloom filters, Delta Lake can efficiently prune unnecessary files or partitions from the query plan. This can significantly improve the query performance and reduce the I/O cost.


NEW QUESTION # 16
A production workload incrementally applies updates from an external Change Data Capture feed to a Delta Lake table as an always-on Structured Stream job. When data was initially migrated for this table, OPTIMIZE was executed and most data files were resized to 1 GB. Auto Optimize and Auto Compaction were both turned on for the streaming production job. Recent review of data files shows that most data files are under 64 MB, although each partition in the table contains at least 1 GB of data and the total table size is over 10 TB.
Which of the following likely explains these smaller file sizes?

  • A. Databricks has autotuned to a smaller target file size based on the overall size of data in the table
  • B. Databricks has autotuned to a smaller target file size to reduce duration of MERGE operations
  • C. Z-order indices calculated on the table are preventing file compaction C Bloom filler indices calculated on the table are preventing file compaction
  • D. Databricks has autotuned to a smaller target file size based on the amount of data in each partition

Answer: B

Explanation:
Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from This is the correct answer because Databricks has a feature called Auto Optimize, which automatically optimizes the layout of Delta Lake tables by coalescing small files into larger ones and sorting data within each file by a specified column. However, Auto Optimize also considers the trade- off between file size and merge performance, and may choose a smaller target file size to reduce the duration of merge operations, especially for streaming workloads that frequently update existing records. Therefore, it is possible that Auto Optimize has autotuned to a smaller target file size based on the characteristics of the streaming production job.


NEW QUESTION # 17
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