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[General] Try Before Buy Our Updated Databricks Databricks-Certified-Professional-Data-Eng

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【General】 Try Before Buy Our Updated Databricks Databricks-Certified-Professional-Data-Eng

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q15-Q20):NEW QUESTION # 15
A nightly job ingests data into a Delta Lake table using the following code:

The next step in the pipeline requires a function that returns an object that can be used to manipulate new records that have not yet been processed to the next table in the pipeline.
Which code snippet completes this function definition?
def new_records():
  • A. return spark.readStream.table("bronze")
  • B. return spark.readStream.load("bronze")
  • C. return spark.read.option("readChangeFeed", "true").table ("bronze")
  • D.
Answer: D
Explanation:
Explanation
This is the correct answer because it completes the function definition that returns an object that can be used to manipulate new records that have not yet been processed to the next table in the pipeline. The object returned by this function is a DataFrame that contains all change events from a Delta Lake table that has enabled change data feed. The readChangeFeed option is set to true to indicate that the DataFrame should read changes from the table, and the table argument specifies the name of the table to read changes from. The DataFrame will have a schema that includes four columns: operation, partition, value, and timestamp. The operation column indicates the type of change event, such as insert, update, or delete. The partition column indicates the partition where the change event occurred. The value column contains the actual data of the change event as a struct type. The timestamp column indicates the time when the change event was committed. Verified References: [Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "Read changes in batch queries" section.

NEW QUESTION # 16
An upstream system has been configured to pass the date for a given batch of data to the Databricks Jobs API as a parameter. The notebook to be scheduled will use this parameter to load data with the following code:
df = spark.read.format("parquet").load(f"/mnt/source/(date)")
Which code block should be used to create the date Python variable used in the above code block?
  • A. date = dbutils.notebooks.getParam("date")
  • B. input_dict = input()
    date= input_dict["date"]
  • C. import sys
    date = sys.argv[1]
  • D. date = spark.conf.get("date")
  • E. dbutils.widgets.text("date", "null")
    date = dbutils.widgets.get("date")
Answer: E
Explanation:
The code block that should be used to create the date Python variable used in the above code block is:
dbutils.widgets.text("date", "null") date = dbutils.widgets.get("date") This code block uses the dbutils.widgets API to create and get a text widget named "date" that can accept a string value as a parameter1. The default value of the widget is "null", which means that if no parameter is passed, the date variable will be "null". However, if a parameter is passed through the Databricks Jobs API, the date variable will be assigned the value of the parameter. For example, if the parameter is "2021-11-01", the date variable will be "2021-11-01". This way, the notebook can use the date variable to load data from the specified path.
The other options are not correct, because:
* Option A is incorrect because spark.conf.get("date") is not a valid way to get a parameter passed through the Databricks Jobs API. The spark.conf API is used to get or set Spark configuration properties, not notebook parameters2.
* Option B is incorrect because input() is not a valid way to get a parameter passed through the Databricks Jobs API. The input() function is used to get user input from the standard input stream, not from the API request3.
* Option C is incorrect because sys.argv1 is not a valid way to get a parameter passed through the Databricks Jobs API. The sys.argv list is used to get the command-line arguments passed to a Python script, not to a notebook4.
* Option D is incorrect because dbutils.notebooks.getParam("date") is not a valid way to get a parameter passed through the Databricks Jobs API. The dbutils.notebooks API is used to get or set notebook parameters when running a notebook as a job or as a subnotebook, not when passing parameters through the API5.
References: Widgets, Spark Configuration, input(), sys.argv, Notebooks

NEW QUESTION # 17
A data engineer has ingested data from an external source into a PySpark DataFrame raw_df. They need to
briefly make this data available in SQL for a data analyst to perform a quality assurance check on the data.
Which of the following commands should the data engineer run to make this data available in SQL for only
the remainder of the Spark session?
  • A. raw_df.saveAsTable("raw_df")
  • B. raw_df.createTable("raw_df")
  • C. raw_df.createOrReplaceTempView("raw_df")
  • D. raw_df.write.save("raw_df")
  • E. There is no way to share data between PySpark and SQL
Answer: C

NEW QUESTION # 18
A data engineer is designing a pipeline in Databricks that processes records from a Kafka stream where late-arriving data is common.
Which approach should the data engineer use?
  • A. Use batch processing and overwrite the entire output table each time to ensure late data is incorporated correctly.
  • B. Implement a custom solution using Databricks Jobs to periodically reprocess all historical data.
  • C. Use an Auto CDC pipeline with batch tables to simplify late data handling.
  • D. Use a watermark to specify the allowed lateness to accommodate records that arrive after their expected window, ensuring correct aggregation and state management.
Answer: D
Explanation:
Comprehensive and Detailed Explanation From Exact Extract of Databricks Data Engineer Documents:
In Structured Streaming, event-time watermarks control how long the engine waits for late-arriving data before finalizing aggregations. By setting an appropriate watermark, Databricks can handle late data gracefully - incorporating records that arrive within the defined window while discarding excessively delayed events.
This approach ensures accurate aggregations, minimizes state size, and prevents memory leaks.
Manual reprocessing (A) or overwriting entire datasets (B) is inefficient and costly, while Auto CDC (C) is used for change tracking in Delta tables, not for streaming event lateness.
Thus, using watermarking is the recommended and official approach for managing late data in streaming pipelines.

NEW QUESTION # 19
A DLT pipeline includes the following streaming tables:
Raw_lot ingest raw device measurement data from a heart rate tracking device.
Bgm_stats incrementally computes user statistics based on BPM measurements from raw_lot.
How can the data engineer configure this pipeline to be able to retain manually deleted or updated records in the raw_iot table while recomputing the downstream table when a pipeline update is run?
  • A. Set the skipChangeCommits flag to true on bpm_stats
  • B. Set the pipelines, reset, allowed property to false on bpm_stats
  • C. Set the pipelines, reset, allowed property to false on raw_iot
  • D. Set the SkipChangeCommits flag to true raw_lot
Answer: C
Explanation:
In Databricks Lakehouse, to retain manually deleted or updated records in the raw_iot table while recomputing downstream tables when a pipeline update is run, the property pipelines.reset.allowed should be set to false. This property prevents the system from resetting the state of the table, which includes the removal of the history of changes, during a pipeline update. By keeping this property as false, any changes to the raw_iot table, including manual deletes or updates, are retained, and recomputation of downstream tables, such as bpm_stats, can occur with the full history of data changes intact.
References:
* Databricks documentation on DLT pipelines:
https://docs.databricks.com/data ... ables-overview.html

NEW QUESTION # 20
......
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