DE-ASSOC practice questions 101–110

1. Question 101 · 170 in bank

   A data engineer has realized that the data files associated with a Delta table are incredibly small. They want to compact the small files to form larger files to improve performance. Which of the following keywords can be used to compact the small files?

   • AREDUCE
   • BOPTIMIZE
   • CCOMPACTION
   • DREPARTITION
   • EVACUUM

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   Answer: B

   Explanation

   OPTIMIZE is the correct answer. Compacting small files using the OPTIMIZE command improves table performance such as by combining multiple small files into larger ones.

   Source: Adapted from community discussion — UGOTCOOKIES

   References

   • Databricks documentation

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2. Question 102 · 170 in bank

   In which of the following file formats is data from Delta Lake tables primarily stored?

   • ADelta
   • BCSV
   • CParquet
   • DJSON
   • EA proprietary, optimized format specific to Databricks

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   Answer: C

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3. Question 103 · 170 in bank

   Which of the following is stored in the Databricks customer's cloud account?

   • ADatabricks web application
   • BCluster management metadata
   • CRepos
   • DData
   • ENotebooks

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   Answer: D

   Explanation

   Data stored in Delta Lake or other formats on cloud storage is managed within the customer's own cloud account, while other components like the Databricks web application and cluster management metadata are managed by Databricks itself.

   Source: Adapted from community discussion — 80370eb

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4. Question 104 · 170 in bank

   Which of the following can be used to simplify and unify siloed data architectures that are specialized for specific use cases?

   • ANone of these
   • BData lake
   • CData warehouse
   • DAll of these
   • EData lakehouse

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   Answer: E

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5. Question 105 · 170 in bank

   A data engineer has a Python notebook in Databricks, but they need to use SQL to accomplish a specific task within a cell. They still want all of the other cells to use Python without making any changes to those cells. Which of the following describes how the data engineer can use SQL within a cell of their Python notebook?

   • AIt is not possible to use SQL in a Python notebook
   • BThey can attach the cell to a SQL endpoint rather than a Databricks cluster
   • CThey can simply write SQL syntax in the cell
   • DThey can add %sql to the first line of the cell
   • EThey can change the default language of the notebook to SQL

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   Answer: D

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6. Question 106 · 170 in bank

   Which of the following SQL keywords can be used to convert a table from a long format to a wide format?

   • ATRANSFORM
   • BPIVOT
   • CSUM
   • DCONVERT
   • EWHERE

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   Answer: B

   Explanation

   https://docs.databricks.com/en/sql/language-manual/sql-ref-syntax-qry-select-pivot.html “Pivot” transforms the rows of the table_reference by rotating unique values of a specified column list into separate columns. SYNTAX : table_reference PIVOT ( { aggregate_expression [ [ AS ] agg_column_alias ] } [, ...] FOR column_list IN ( expression_list ) ) column_list { column_name | ( column_name [, ...] ) } expression_list { expression [ AS ] [ column_alias ] | { ( expression [, ...] ) [ AS ] [ column_alias] } [, ...] ) }

   Source: Adapted from community discussion — AndreFR

   References

   • Databricks documentation

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7. Question 107 · 170 in bank

   Which of the following describes a benefit of creating an external table from Parquet rather than CSV when using a CREATE TABLE AS SELECT statement?

   • AParquet files can be partitioned
   • BCREATE TABLE AS SELECT statements cannot be used on files
   • CParquet files have a well-defined schema
   • DParquet files have the ability to be optimized
   • EParquet files will become Delta tables

   Show answer

   Answer: C

   Explanation

   CTAS - CTAS automatically infer schema information from query results and do not support manual schema declaration.This means that CTAS statements are useful for external data ingestion from sources with well-defined schema, such as Parquet files and tables.CTAS statements also do not support specifying additional file options.

   Source: Adapted from community discussion — FastEddie

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8. Question 108 · 170 in bank

   Which of the following code blocks will remove the rows where the value in column age is greater than 25 from the existing Delta table my_table and save the updated table?

   • ASELECT * FROM my_table WHERE age > 25;
   • BUPDATE my_table WHERE age > 25;
   • CDELETE FROM my_table WHERE age > 25;
   • DUPDATE my_table WHERE age <= 25;
   • EDELETE FROM my_table WHERE age <= 25;

   Show answer

   Answer: C

   Explanation

   C is the correct answer as the SELECT statement allows to query a table, the UPDATE statement allows to modify values in columns. If you want to remove rows that don't match a specific condition you must use DELETE

   Source: Adapted from community discussion — DavidRou

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9. Question 109 · 170 in bank

   A data engineer has a Python variable table_name that they would like to use in a SQL query. They want to construct a Python code block that will run the query using table_name. They have the following incomplete code block: ____(f"SELECT customer_id, spend FROM {table_name}") Which of the following can be used to fill in the blank to successfully complete the task?

   • Aspark.delta.sql
   • Bspark.delta.table
   • Cspark.table
   • Ddbutils.sql
   • Espark.sql

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   Answer: E

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10. Question 110 · 170 in bank

    A data engineer has created a new database using the following command: CREATE DATABASE IF NOT EXISTS customer360; In which of the following locations will the customer360 database be located?

    • Adbfs:/user/hive/database/customer360
    • Bdbfs:/user/hive/warehouse
    • Cdbfs:/user/hive/customer360
    • DMore information is needed to determine the correct response
    • Edbfs:/user/hive/database

    Show answer

    Answer: B

    Explanation

    B. dbfs:/user/hive/warehouse Explanation: In Databricks, the default location for databases created in the Hive Metastore is often under the warehouse directory. The CREATE DATABASE command usually creates the metadata entry for the database in the Hive Metastore, but it doesn’t directly create the physical database directory within DBFS (Databricks File System). The exact path structure may differ based on configuration or settings in the Databricks environment, but generally, the warehouse directory is where Hive databases' metadata resides. The physical data within the database will be stored in DBFS, but the metadata for the customer360 database should be within the warehouse directory in Hive Metastore.

    Source: Adapted from community discussion — Garyn

    References

    • Databricks documentation

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DE-ASSOC practice, question 101. 170 questions available.