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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Configure and manage Azure Databricks environments | 15-20% | - Workspace and compute configuration
|
| Topic 2: Deploy and manage data pipelines and workloads | 30-35% | - Lakehouse architecture operations
|
| Topic 3: Prepare and process data | 30-35% | - Data quality and validation
|
| Topic 4: Secure and govern data using Unity Catalog | 15-20% | - Data governance fundamentals
|
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NEW QUESTION # 22
You have an Azure Databricks workspace that contains a job in Lakeflow Jobs named Job1.
Job! processes raw data files stored in Azure Storage.
New files arrive at unpredictable intervals.
You need to ensure that Job1 starts automatically when new files arrive and does NOT consume compute resources when no data is available.
Which type of job trigger should you use?
Answer: D
Explanation:
The correct answer is C - File Arrival trigger.
File Arrival monitors a specified Azure Storage path and fires a job run each time a new file lands there. This ticks both requirements: Job1 starts automatically in response to new data (no human involvement), and when no files arrive, no job runs - no cluster spins up, no compute cost is incurred.
Option A (scheduled) runs at fixed intervals regardless of whether files are waiting. A quiet weekend still kicks off hourly (or daily) runs, burning compute for nothing. Option B (continuous) keeps the job running perpetually, consuming resources even during long gaps between file arrivals - exactly what 'does NOT consume compute resources when no data is available' rules out. Option D (manual) requires a person to trigger every run, which is unsuitable for unpredictable arrival patterns.
Reference: https://learn.microsoft.com/en-us/azure/databricks/jobs/triggers#file-arrival-trigger
NEW QUESTION # 23
You have an Azure Databricks workspace that is enabled for Unity Catalog.
You plan to ingest data from CSV files stored in Azure Data Lake Storage Gen2. New rows are appended frequently.
You need to implement a data ingestion solution that meets the following requirements:
- New data must be available in near-real-time (NRT).
- The data must be stored in managed Delta tables.
- The solution must minimize custom code and maintenance effort.
What should you include in the solution?
Answer: B
Explanation:
You should use Auto Loader with Delta Live Tables (DLT) or a streaming readStream using the cloudFiles format to load data into Unity Catalog managed tables.
To achieve the absolute lowest maintenance and custom code, Delta Live Tables with Auto Loader is the recommended choice.
Configure Cloud FilesFormat option: Set the source format to cloudFiles in your Spark stream.File detection: Auto Loader automatically tracks new files arriving in Azure Data Lake Storage (ADLS) Gen2.Schema evolution: It infers and adapts to schema changes without code updates.
Reference:
https://docs.databricks.com/aws/en/ingestion/cloud-object-storage/auto-loader/unity-catalog
NEW QUESTION # 24
What happens if incoming data violates Delta table schema?
Answer: B
Explanation:
Delta Lake enforces schema by default. If incoming data does not match schema, the write operation fails unless schema evolution is explicitly enabled. It does not auto-cast or append invalid data. Overwriting would require explicit command.
NEW QUESTION # 25
You have an Azure Databricks workspace that contains a job in Lakeflow Jobs named Job1.
Job! runs every hour.
Occasionally, the job run takes longer than one hour to complete. Overlapping runs must be prevented to avoid data corruption.
You need to configure the job scheduling behavior.
What should you configure? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Two settings address the overlapping-run problem:
Concurrent Runs policy set to ' Skip ' (or ' Allow only one concurrent run ' ). When a new scheduled trigger fires while the previous run is still in progress, the new run is skipped rather than starting alongside the ongoing one. This prevents two runs from writing to the same tables at the same time - which is the data corruption risk the question highlights.
Cron-based schedule for the hourly trigger. A cron expression defines the regular execution cadence.
Combined with the concurrency setting, the job runs hourly but never overlaps.
An alternative to ' Skip ' is ' Wait ' (queue the new run), which ensures every scheduled run eventually executes - but for this scenario where overlapping is the primary concern, skipping the missed run is typically preferable to building up a queue of back-to-back executions.
Reference: https://learn.microsoft.com/en-us/azure/databricks/jobs/configure-jobs#concurrent-runs
NEW QUESTION # 26
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.dropna(subset=["order_amount"])
Does this meet the goal?
Answer: B
Explanation:
The correct answer is A - Yes.
df.dropna(subset=['order_amount']) is the idiomatic PySpark way to remove rows where a specific column contains a null. It inspects only the columns listed in subset and drops any row where those columns are null.
The resulting DataFrame contains only rows where order_amount is not null - exactly what the requirement asks for.
The subset parameter is important: without it, dropna() would drop rows where ANY column is null, which could incorrectly exclude rows that have nulls in other columns but a valid order_amount. By specifying subset=['order_amount'], the filter is applied precisely and only to the column in question.
This method is semantically equivalent to df.filter(df.order_amount.isNotNull()) and to the SQL clause WHERE order_amount IS NOT NULL. Both are correct - dropna with a subset is arguably the more readable Pythonic approach.
Reference: https://learn.microsoft.com/en-us/azure/databricks/pyspark/basics
NEW QUESTION # 27
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