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| Section | Weight | Objectives |
|---|---|---|
| Set up and configure an Azure Databricks environment | 15-20% | - Create and configure Azure Databricks workspaces
|
| Prepare and process data | 30-35% | - Ingest and transform data
|
| Secure and govern Unity Catalog objects | 15-20% | - Implement governance and security
|
| Deploy and maintain data pipelines and workloads | 30-35% | - Manage production workloads
|
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NEW QUESTION # 57
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-fillna(0, subset=['order_amount'])
Does this meet the goal?
Answer: B
Explanation:
The correct answer is B - No.
df.fillna(0, subset=['order_amount']) does not remove null rows. It replaces null values in order_amount with the integer 0, leaving those rows in the DataFrame with 0 as their order_amount value. The row count is unchanged - nulls are imputed, not dropped.
The requirement is to exclude rows where order_amount is null, meaning those rows should not appear in the result at all. fillna is a data imputation function - it fills gaps with default values, which is a different operation entirely from filtering. After fillna, a downstream process would see order_amount = 0 and might treat it as a valid zero-value order rather than recognising it was originally null.
The correct approach is df.dropna(subset=['order_amount']) or df.filter(df.order_amount.isNotNull()), both of which physically remove null rows from the resulting DataFrame.
Reference: https://learn.microsoft.com/en-us/azure/databricks/pyspark/basics
NEW QUESTION # 58
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: D
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 # 59
You have an Azure Databricks workspace that is enabled for Unity Catalog and contains a managed Delta table named Payments.
Payments stores transaction data and contains a column named payment_amount of the Decimal data type.
You must enforce the following business rule:
payment_amount must be between 0 and 10,000, inclusive
You need to ensure that records that violate the rule are rejected when data is written to the Payments table.
What should you do?
Answer: A
Explanation:
A CHECK constraint enforces a Boolean condition whenever data is inserted or updated. The constraint can require payment_amount > = 0 AND payment_amount < = 10000, causing a transaction containing an invalid value to fail instead of allowing the record into Payments. This provides storage-level data-quality enforcement regardless of which pipeline, notebook, or SQL statement performs the write. Row-level security controls which existing records users can see; it does not reject invalid writes. SELECT statements filter results only when they are executed and therefore cannot protect the underlying table. Table update triggers are not the standard Delta Lake mechanism for this requirement. Azure Databricks classifies CHECK constraints as enforced constraints and rejects transactions when their conditions are violated. Microsoft Learn
NEW QUESTION # 60
You have an Azure Databricks workspace that uses serverless compute.
You need to ingest data by using Lakeflow Jobs. New records must be processed as soon as they become available.
Which type of job trigger should you use for the ingestion?
Answer: B
Explanation:
The correct answer is D - Continuous trigger.
A Continuous trigger keeps the job running as a perpetual loop. As soon as one micro-batch or iteration completes, the next begins. New records are picked up with the shortest possible latency - as close to real- time as a Lakeflow Jobs pipeline gets.
File Arrival (Option B) is event-driven but introduces per-file trigger overhead and is best suited for file-based ingestion rather than continuous streaming workloads. Scheduled (Option C) runs at fixed clock intervals - if new data arrives between runs, it waits until the next scheduled execution. Manual (Option A) requires a human to start each run.
The question specifies serverless compute, which pairs naturally with Continuous trigger because serverless handles cluster lifecycle automatically - the job stays active without managing a persistent cluster. 'New records must be processed as soon as they become available' is the exact use case the Continuous trigger is designed for.
Reference: https://learn.microsoft.com/en-us/azure/databricks/jobs/triggers
NEW QUESTION # 61
Hotspot Question
You have an Azure Databricks workspace that is enabled for Unity Catalog and contains a Delta table named db1.sales_orders.
db1.sales_orders is updated nightly and has change data feed (CDF) enabled.
You need to ingest all the changes from the db1.sales_orders table, including inserts, updates, and deletes, into a downstream pipeline.
How should you complete the PsySpark code segment? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 62
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