You are working on a marketing team request to identify customers with the same information between two tables CUSTOMERS_2021 and CUSTOMERS_2020 each table contains 25 columns with the same schema, You are looking to identify rows that match between two tables across all columns, which of the following can be used to perform in SQL

• A.1.SELECT * FROM CUSTOMERS_2021
  2. UNION
  3.SELECT * FROM CUSTOMERS_2020
• B.1.SELECT * FROM CUSTOMERS_2021
  2. UNION ALL
  3.SELECT * FROM CUSTOMERS_2020
• C.1.SELECT * FROM CUSTOMERS_2021 C1
  2.INNER JOIN CUSTOMERS_2020 C2
  3.ON C1.CUSTOMER_ID = C2.CUSTOMER_ID
• D.1.SELECT * FROM CUSTOMERS_2021
  2. INTERSECT
  3.SELECT * FROM CUSTOMERS_2020
• E.1.SELECT * FROM CUSTOMERS_2021
  2.EXCEPT
  3.SELECT * FROM CUSTOMERS_2020

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Delete records from the transactions Delta table where transactionDate is greater than current timestamp?

• A.DELETE FROM transactions where transactionDate > current_timestamp()
• B.DELETE FROM transactions FORMAT DELTA where transactionDate > cur-renct_timestmap()
• C.DELETE FROM transactions where transactionDate > current_timestamp() KEEP_HISTORY
• D.DELETE FROM transactions if transctionDate > current_timestamp()
• E.DELET FROM transactions where transactionDate GE current_timestamp()

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Question-3: In machine learning, feature hashing, also known as the hashing trick (by analogy to the kernel
trick), is a fast and space-efficient way of vectorizing features (such as the words in a language), i.e., turning
arbitrary features into indices in a vector or matrix. It works by applying a hash function to the features and
using their hash values modulo the number of features as indices directly, rather than looking the indices up in
an associative array. So what is the primary reason of the hashing trick for building classifiers?

• A.It creates the smaller models
• B.It requires the lesser memory to store the coefficients for the model
• C.It reduces the non-significant features e.g. punctuations
• D.Noisy features are removed

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How do you access or use tables in the unity catalog?

• A.schema_name.table_name
• B.schema_name.catalog_name.table_name
• C.catalog_name.table_name
• D.catalog_name.database_name.schema_name.table_name
• E.catalog_name.schema_name.table_name

Correct Answer: E

Explanation
The answer is catalog_name.schema_name.table_name
Graphical user interface, diagram Description automatically generated

Note: Database and Schema are analogous they are interchangeably used in the Unity catalog.
FYI, A catalog is registered under a metastore, by default every workspace has a default metastore called hive_metastore, with a unity catalog you have the ability to create meatstores and share that across multiple workspaces.

Diagram Description automatically generated

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Data engineering team has provided 10 queries and asked Data Analyst team to build a dashboard and refresh the data every day at 8 AM, identify the best approach to set up data refresh for this dashaboard?

• A.Each query requires a separate task and setup 10 tasks under a single job to run at 8 AM to refresh the dashboard
• B.The entire dashboard with 10 queries can be refreshed at once, single schedule needs to be set up to refresh at 8 AM.
• C.Setup JOB with linear dependency to all load all 10 queries into a table so the dashboard can be refreshed at once.
• D.A dashboard can only refresh one query at a time, 10 schedules to set up the refresh.
• E.Use Incremental refresh to run at 8 AM every day.

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