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Microsoft Implementing Data Engineering Solutions Using Azure Databricks Sample Questions (Q28-Q33):

NEW QUESTION # 28
Note: This section contains one or more sets of questions with the same scenario and problem. Each question presents a unique solution to the problem. You must determine whether the solution meets the stated goals. More than one solution in the set might solve the problem. It is also possible that none of the solutions in the set solve the problem.
After you answer a question in this section, you will NOT be able to return. As a result, these questions do not appear on the Review Screen.
You have an Azure Databricks workspace that is enabled for Unity Catalog and contains a Delta table named Orders.
You load the Orders table into an Apache Spark DataFrame named df.
You need to create a DataFrame that excludes rows where the order amount is null.
Solution: You run the following expression.
df.filter(df.order_amount != None)
Does this meet the goal?

Answer: B

Explanation:
Correct:
* You run the following expression.
df.dropna(subset=["order_amount"])
The expression df.dropna(subset=["order_amount"]) is an appropriate and effective way to exclude rows where order_amount is null.
* You run the following expression.
df.filter(df.order_amount.isNotNull())
To exclude rows where the order amount is null, you can use the isNotNull() method or a SQL expression within the filter() or where() functions.Here are the standard, appropriate expressions:
Option 1: Python/PySpark API (Recommended)
pythondf_clean = df.filter(df["order_amount"].isNotNull())
Incorrect:
* You run the following expression.
df.fillna(0, subset=['order_amount'])
* You run the following expression.
df.filter(df.order_amount != None)
Reference:
https://www.geeksforgeeks.org/python/filter-pyspark-dataframe-columns-with-none-or-null-values/
https://learn.microsoft.com/en-us/azure/databricks/pyspark/reference/classes/dataframe/dropna


NEW QUESTION # 29
You have an Azure Databricks workspace that is enabled for Unity Catalog and contains a catalog named Catalog 1. Catalog 1 contains a table named Transactions. Transactions contains the following columns:
* transaction_id
* customet_name
* email address
* credit_card_number
* transaction_amount
You need to ensure that business analysts can query all the tows in the Transactions table. The solution must meet the following requirements:
* Prevent the analysts from seeing the full values in the email_address and credit_catd_number columns.
* Ensure that the analysts can see only the values after the @ character in each email address.
* Ensure that the analysts can see only the last four digits of each credit card number.
* Enable the analysts to query the table without errors.
* Follow the principle of least privilege.
What should you do?

Answer: A

Explanation:
The correct answer is C. Column masks are the right tool when you need to partially expose sensitive data rather than hide it entirely. A column mask is a SQL function attached to a column that rewrites the returned value based on who is querying. For email_address, the mask returns only the substring after '@'. For credit_card_number, it returns only the last four digits. Business analysts get useful data without seeing anything sensitive, and they can still query the table without errors.
Option A (row-level filters) controls which rows a user sees, not the values within a row - it can't partially redact a column. Option B (grant SELECT only on non-sensitive columns) removes the columns entirely, so analysts can't see even partial email or card values - that doesn't meet the 'can see only partial values' requirement. Option D (column-level encryption) requires key management infrastructure and decryption at query time, which is significantly more complex than column masking for this use case.
Reference: https://learn.microsoft.com/en-us/azure/databricks/data-governance/unity-catalog/row-and-column- filters


NEW QUESTION # 30
You have an Azure Databricks workspace that is attached to a Unity Catalog metastore named metastore1, metastore1 contains a catalog named catalog1.
You need to create a new schema named schema2 that meets the following requirements:
- Is contained in catalog1
- Uses abfss://[email protected]/data as
the managed location
Which SQL statement should you execute?

Answer: A

Explanation:
To create a new schema with a specific managed location in Azure Databricks Unity Catalog, use the CREATE SCHEMA command.
CREATE SCHEMA catalog_name.schema_name
MANAGED LOCATION 'abfss://[email protected]/data'; Catalog Name: Replace catalog_name with your existing catalog.
Schema Name: Replace schema_name with your desired new schema name.Quotes: The storage path must be enclosed in single quotes.
Privileges: You must have CREATE SCHEMA privileges on the parent catalog and ownership (or adequate permissions) on the external location.
Reference:
https://dev.to/encorepartners/creating-your-first-catalog-schema-and-tables-in-databricks-20p3


NEW QUESTION # 31
Which component enforces table-level permissions in Databricks?

Answer: B

Explanation:
Unity Catalog provides fine-grained access control at table, schema, and column levels. It centralizes governance across workspaces. Cluster policies control compute settings. Spark configuration does not manage security. DBFS permissions are not sufficient for enterprise governance.


NEW QUESTION # 32
You have an Azure Databricks workspace that is enabled for Unity Catalog and contains two managed Delta tables named sales.schema1.table1 and sales.schema1.table2.
sales.schema1.table1 contains sales data from the current year.
sales.schema1.table2 contains historical data.
You need to load all the rows from sales.schema1.table1 into sales.schema1.table2. The solution must preserve any existing data in sales.schema1.table2 and minimize processing effort.
Which command should you run?

Answer: B

Explanation:
The correct answer is A - INSERT INTO.
INSERT INTO appends all rows from the source to the target. Existing rows in table2 are untouched - the historical data stays intact - and the current year's rows from table1 are added. That is exactly what the requirement asks: 'preserve any existing data and minimise processing effort.' Option B (CREATE TABLE AS SELECT) would fail if table2 already exists, or if used with CREATE OR REPLACE it wipes table2 entirely before writing - historical data gone. Option C (INSERT OVERWRITE) replaces the entire table content with only the table1 rows, also destroying table2's existing data. Option D (CREATE OR REPLACE TABLE AS SELECT) explicitly drops and recreates the table, eliminating all historical records.
INSERT INTO is deliberately the simplest possible command here - one line, no special options, no risk of accidental data loss.
Reference: https://learn.microsoft.com/en-us/azure/databricks/sql/language-manual/delta-insert-into


NEW QUESTION # 33
......

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