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[General] Authentic Databricks Databricks-Certified-Professional-Data-Engineer Exam Questi

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【General】 Authentic Databricks Databricks-Certified-Professional-Data-Engineer Exam Questi

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q90-Q95):NEW QUESTION # 90
A junior data engineer is working to implement logic for a Lakehouse table named silver_device_recordings.
The source data contains 100 unique fields in a highly nested JSON structure.
The silver_device_recordings table will be used downstream for highly selective joins on a number of fields, and will also be leveraged by the machine learning team to filter on a handful of relevant fields, in total, 15 fields have been identified that will often be used for filter and join logic.
The data engineer is trying to determine the best approach for dealing with these nested fields before declaring the table schema.
Which of the following accurately presents information about Delta Lake and Databricks that may Impact their decision-making process?
  • A. Tungsten encoding used by Databricks is optimized for storing string data: newly-added native support for querying JSON strings means that string types are always most efficient.
  • B. Schema inference and evolution on Databricks ensure that inferred types will always accurately match the data types used by downstream systems.
  • C. By default Delta Lake collects statistics on the first 32 columns in a table; these statistics are leveraged for data skipping when executing selective queries.
  • D. Because Delta Lake uses Parquet for data storage, Dremel encoding information for nesting can be directly referenced by the Delta transaction log.
Answer: C
Explanation:
Delta Lake, built on top of Parquet, enhances query performance through data skipping, which is based on the statistics collected for each file in a table. For tables with a large number of columns, Delta Lake by default collects and stores statistics only for the first 32 columns. These statistics include min/max values and null counts, which are used to optimize query execution by skipping irrelevant data files. When dealing with highly nested JSON structures, understanding this behavior is crucial for schema design, especially when determining which fields should be flattened or prioritized in the table structure to leverage data skipping efficiently for performance optimization.References: Databricks documentation on Delta Lake optimization techniques, including data skipping and statistics collection (https://docs.databricks.com/delta/optimizations/index.html).

NEW QUESTION # 91
A Delta Lake table was created with the below query:

Consider the following query:
DROP TABLE prod.sales_by_store -
If this statement is executed by a workspace admin, which result will occur?
  • A. Nothing will occur until a COMMIT command is executed.
  • B. Data will be marked as deleted but still recoverable with Time Travel.
  • C. An error will occur because Delta Lake prevents the deletion of production data.
  • D. The table will be removed from the catalog and the data will be deleted.
  • E. The table will be removed from the catalog but the data will remain in storage.
Answer: D
Explanation:
When a table is dropped in Delta Lake, the table is removed from the catalog and the data is deleted. This is because Delta Lake is a transactional storage layer that provides ACID guarantees. When a table is dropped, the transaction log is updated to reflect the deletion of the table and the data is deleted from the underlying storage. References:
* https://docs.databricks.com/delta/quick-start.html#drop-a-table
* https://docs.databricks.com/delta/delta-batch.html#drop-table

NEW QUESTION # 92
How to determine if a table is a managed table vs external table?
  • A. All external tables are stored in data lake, managed tables are stored in DELTA lake
  • B. Run IS_MANAGED('table_name') function
  • C. Run SQL command DESCRIBE EXTENDED table_name and check type
  • D. Run SQL command SHOW TABLES to see the type of the table
  • E. All managed tables are stored in unity catalog
Answer: C
Explanation:
Explanation
The answer is Run SQL command DESCRIBE EXTENDED table_name and check type Example of External table Graphical user interface, text, application Description automatically generated

Example of managed table
Graphical user interface, text, application, Teams Description automatically generated


NEW QUESTION # 93
Which of the following technologies can be used to identify key areas of text when parsing Spark Driver log4j output?
  • A. C++
  • B. pyspsark.ml.feature
  • C. Regex
  • D. Julia
  • E. Scala Datasets
Answer: C
Explanation:
Explanation
Regex, or regular expressions, are a powerful way of matching patterns in text. They can be used to identify key areas of text when parsing Spark Driver log4j output, such as the log level, the timestamp, the thread name, the class name, the method name, and the message. Regex can be applied in various languages and frameworks, such as Scala, Python, Java, Spark SQL, and Databricks notebooks. References:
https://docs.databricks.com/note ... regular-expressions
https://docs.databricks.com/spar ... expressions-in-udfs
https://docs.databricks.com/spar ... regexp_extract.html
https://docs.databricks.com/spar ... regexp_replace.html

NEW QUESTION # 94
Where in the Spark UI can one diagnose a performance problem induced by not leveraging predicate push-down?
  • A. In the Query Detail screen, by interpreting the Physical Plan
  • B. In the Storage Detail screen, by noting which RDDs are not stored on disk
  • C. In the Delta Lake transaction log. by noting the column statistics
  • D. In the Executor's log file, by grippingfor "predicate push-down"
  • E. In the Stage's Detail screen, in the Completed Stages table, by noting the size of data read from the Input column
Answer: A
Explanation:
Explanation
This is the correct answer because it is where in the Spark UI one can diagnose a performance problem induced by not leveraging predicate push-down. Predicate push-down is an optimization technique that allows filtering data at the source before loading it into memory or processing it further. This can improve performance and reduce I/O costs by avoiding reading unnecessary data. To leverage predicate push-down, one should use supported data sources and formats, such as Delta Lake, Parquet, or JDBC, and use filter expressions that can be pushed down to the source. To diagnose a performance problem induced by not leveraging predicate push-down, one can use the Spark UI to access the Query Detail screen, which showsinformation about a SQL query executed on a Spark cluster. The Query Detail screen includes the Physical Plan, which is the actual plan executed by Spark to perform the query. The Physical Plan shows the physical operators used by Spark, such as Scan, Filter, Project, or Aggregate, and their input and output statistics, such as rows and bytes. By interpreting the Physical Plan, one can see if the filter expressions are pushed down to the source or not, and how much data is read or processed by each operator. Verified References: [Databricks Certified Data Engineer Professional], under "Spark Core" section; Databricks Documentation, under "Predicate pushdown" section; Databricks Documentation, under "Query detail page" section.

NEW QUESTION # 95
......
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