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Exam DP-750 Topic 1 Question 31 Discussion
Actual exam question for Microsoft's DP-750 exam
Question #: 31
Topic #: 1
Question #: 31
Topic #: 1
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?
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?
Suggested Answer: A Vote an answer
The correct answer is A - Auto Loader.
Auto Loader is exactly the right tool for this scenario: new CSV files land in ADLS Gen2, and they need to be ingested into managed Delta tables in near-real time with minimal custom code. Auto Loader uses file-system notifications or incremental directory listing to detect new arrivals, processes only the newly added files (skipping previously ingested ones), and writes results into Delta tables - all with schema inference and evolution support built in.
Option B (scheduled Spark batch jobs) adds latency tied to the schedule interval and requires custom 'what files have I already processed' tracking. Option C (external table referencing CSV files) exposes the raw files for querying but doesn't load data into managed Delta tables - it also can't provide NRT updates as files change. Option D (Azure Data Factory pipeline) introduces external orchestration overhead and is a heavier solution for something Auto Loader handles natively in a few lines of PySpark.
Reference: https://learn.microsoft.com/en-us/azure/databricks/ingestion/auto-loader/
Auto Loader is exactly the right tool for this scenario: new CSV files land in ADLS Gen2, and they need to be ingested into managed Delta tables in near-real time with minimal custom code. Auto Loader uses file-system notifications or incremental directory listing to detect new arrivals, processes only the newly added files (skipping previously ingested ones), and writes results into Delta tables - all with schema inference and evolution support built in.
Option B (scheduled Spark batch jobs) adds latency tied to the schedule interval and requires custom 'what files have I already processed' tracking. Option C (external table referencing CSV files) exposes the raw files for querying but doesn't load data into managed Delta tables - it also can't provide NRT updates as files change. Option D (Azure Data Factory pipeline) introduces external orchestration overhead and is a heavier solution for something Auto Loader handles natively in a few lines of PySpark.
Reference: https://learn.microsoft.com/en-us/azure/databricks/ingestion/auto-loader/
by Lillian at Jul 16, 2026, 01:17 PM
Here are all the actual test exam dumps for IT exams. Most people prepare for the actual exams with our test dumps to pass their exams. So it's critical to choose and actual test pdf to succeed.
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