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| Section | Weight | Objectives |
|---|---|---|
| Secure and govern Unity Catalog objects | 15–20% | - Manage data sharing and permissions
|
| Prepare and process data | 30–35% | - Ingest and transform data
|
| Deploy and maintain data pipelines and workloads | 30–35% | - Monitor, troubleshoot, and maintain workloads
|
| Set up and configure an Azure Databricks environment | 15–20% | - Integrate with Azure services
|
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NEW QUESTION # 82
You have an Azure Databricks workspace that is enabled for Unity Catalog and contains two Delta tables named Table1 and Table2 of the same data type.
Table1 contains a column named Columnl. Table2 contains a column named Column2. You run the following query.
SELECT Column1
FROM Table1
GROUP BY Column1
HAVING COUNT(") > 1
INTERSECT
SELECT C0lumn2
FROM Table2
GROUP BY Column2
HAVING COUNT(') > 1;
What occurs when you run the query?
Answer: A
Explanation:
The correct answer is B - values appear in both tables more than once.
Reading the query from the inside out: each subquery identifies values that appear more than once within their own table. The first subquery returns Column1 values that are duplicated in Table1. The second returns Column2 values that are duplicated in Table2. INTERSECT then returns only the values that appear in both result sets - meaning values that are duplicated in Table1 AND also duplicated in Table2.
Option A describes a UNION result (values in either table), not INTERSECT. Option C only considers duplicates in Table1, ignoring the Table2 condition - that would be the first subquery in isolation. Option D describes values exclusive to Table2 (EXCEPT or MINUS), the opposite of INTERSECT's requirement that values appear in both sets.
INTERSECT always requires membership in both operands - that's its fundamental definition in standard SQL.
Reference: https://learn.microsoft.com/en-us/azure/databricks/sql/language-manual/sql-ref-syntax-qry-select- setops
NEW QUESTION # 83
What improves join performance for small lookup tables?
Answer: A
Explanation:
Broadcast joins send the small table to all worker nodes, avoiding expensive shuffling. This significantly improves performance. Shuffle and sort merge joins are heavier. Cartesian joins are inefficient and generally avoided.
NEW QUESTION # 84
You have an Azure Databricks workspace that is enabled for Unity Catalog and contains a managed Delta table named Sales. Sales stores transaction data and contains the following columns:
* transactionjd (string)
* transaction date (date)
* amount (decimal)
You need to implement the following data quality requirements by using table-level data quality enforcement:
* amount must be greater than 0.
* transaction id must never be null.
* Invalid records must be rejected when data is written to the Sales table.
What should you do?
Answer: C
Explanation:
The correct answer is D - a NOT NULL constraint on transaction_id and a CHECK constraint on amount.
Delta Lake table constraints are enforced at write time by the Delta engine itself. A NOT NULL constraint rejects any INSERT or UPDATE that would place a null in transaction_id. A CHECK constraint with amount
> 0 rejects any row where amount is zero or negative. Combined, they implement exactly the stated quality rules: bad rows are rejected when data is written, not filtered away at read time.
Options A and C (SELECT with WHERE / views) are read-time constructs - they don't prevent invalid data from entering the table. A clever pipeline bypass could write directly to the table and skip the view entirely.
Option B (row-level security with WHERE conditions) is an access-control feature for restricting which rows users see, not for enforcing data quality on writes. Table constraints are the only mechanism that genuinely blocks bad data at the storage layer.
Reference: https://learn.microsoft.com/en-us/azure/databricks/delta/delta-constraints
NEW QUESTION # 85
You have an Azure Databricks workspace that is enabled for Unity Catalog and contains two Delta tables named Table1 and Table2 of the same data type.
Table1 contains a column named Column1. Table2 contains a column named Column2.
You run the following query.
SELECT Colum1
FROM Table1
GROUP BY Column1
HAVING COUNT(*) > 1
INTERSECT
SELECT Column2
FROM Table2
GROUP BY Column2
HAVING COUNT(*) > 1;
What occurs when you run the query?
Answer: A
Explanation:
The query will output a single column containing only the values that are duplicated within Table1 and also duplicated within Table2.
Duplicates Isolated First: The GROUP BY and HAVING COUNT(*) > 1 clauses filter each table independently to find only their duplicate rows.
Matching Values Found: The INTERSECT operator compares the filtered results of both tables.
Shared Values Returned: Only values that exist in both filtered datasets are included in the final output.
Automatic Distinct Filtering: INTERSECT automatically removes any duplicate values from the final result set, ensuring each matching value appears exactly once.
Column Naming: The output column will inherit its name from the first SELECT statement (Column1).
Reference:
https://quizlet.com/study-guides/inf3707-chapter-4-data-retrieval-from-2-or-more-tables-
6ec17b26-76a8-4c33-8fa7-18d64154512d
NEW QUESTION # 86
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.isNotNull())
Does this meet the goal?
Answer: B
Explanation:
The correct answer is A - Yes.
df.filter(df.order_amount.isNotNull()) is the correct PySpark pattern for excluding null rows. The isNotNull() method is a Column method that returns True for every row where order_amount has a value and False for rows where it is null. Spark's filter keeps only the rows where the condition evaluates to True, producing a DataFrame with all null order_amount rows removed.
This works correctly because isNotNull() is explicitly null-aware - unlike the != None comparison in Q52, it doesn't rely on Python equality semantics. Under the hood it maps to the SQL expression order_amount IS NOT NULL, which is unambiguous in both SQL and Spark.
Both df.filter(df.order_amount.isNotNull()) and df.dropna(subset=['order_amount']) produce identical results.
The choice between them is stylistic - isNotNull() reads more explicitly as a filter condition, while dropna is more compact when handling multiple columns.
Reference: https://learn.microsoft.com/en-us/azure/databricks/pyspark/basics
NEW QUESTION # 87
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