Databricks-DE-Assoc Data Ingestion and Loading Practice Question
A data engineer is using Auto Loader to stream data from Kafka into a Delta table. The Kafka topic receives messages in Avro format, and the schema is stored in a Confluent Schema Registry. The engineer wants Auto Loader to automatically fetch the schema from the registry and evolve it as new versions are registered. Which configuration should the engineer use?
⚠ Common exam trap
The trap here is assuming that Auto Loader can handle any streaming source, when it is limited to file-based sources in cloud storage.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Auto Loader cannot ingest from Kafka; use Structured Streaming with the Kafka source and Schema Registry integration.
Auto Loader is a file ingestion tool and does not support Kafka as a source. For Kafka ingestion with Avro and Schema Registry, the appropriate method is to use Spark Structured Streaming's Kafka source, which can integrate with Schema Registry to fetch and evolve schemas. The engineer should not attempt to use Auto Loader for this purpose. Therefore, the correct answer is to use Structured Streaming with the Kafka source and the necessary Schema Registry configurations.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use the Kafka source with the option kafka.schema.registry.url and set the value format to 'avro'.
Why it's wrong here
While the Kafka source in Spark Structured Streaming supports Avro with Schema Registry through the kafka.schema.registry.url option and value format settings, Auto Loader is not used in this context. The question specifies Auto Loader, which does not integrate with Kafka directly. Therefore, this option, although technically valid for Kafka ingestion with Structured Streaming, does not align with the use of Auto Loader. The engineer would not be using Auto Loader in this scenario.
- ✗
Set the option cloudFiles.format to 'avro' and provide the schema registry URL via cloudFiles.schemaRegistryUrl.
Why it's wrong here
Auto Loader does not natively support reading from Kafka topics; it is designed for file-based sources in cloud storage. While you can use Auto Loader with Kafka by first landing data into files, directly streaming from Kafka requires Structured Streaming with the Kafka source. The cloudFiles options are specific to file ingestion. Therefore, this configuration is not applicable to Kafka ingestion and would not work as described.
- ✗
Set the option cloudFiles.useNotifications to true and configure Kafka as a notification source.
Why it's wrong here
The cloudFiles.useNotifications option is used to enable file notification services (e.g., AWS SNS, Azure Event Grid) for Auto Loader to detect new files in cloud storage. It has nothing to do with Kafka. Kafka is not a file notification service, and this option would not enable Kafka ingestion. Thus, this configuration is incorrect and would not achieve the desired outcome.
- ✓
Auto Loader cannot ingest from Kafka; use Structured Streaming with the Kafka source and Schema Registry integration.
Why this is correct
Auto Loader is specifically designed for ingesting files from cloud storage into Delta Lake. It does not support Kafka as a source. To ingest from Kafka with Avro and Schema Registry, the engineer should use Spark Structured Streaming's Kafka source, which provides built-in support for Schema Registry via options like kafka.schema.registry.url and value.deserializer. This allows schema fetching and evolution. Therefore, the correct approach is to use Structured Streaming, not Auto Loader.
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Databricks exam blueprint
This Databricks-DE-Assoc practice question is part of Courseiva's free Databricks certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the Databricks-DE-Assoc exam.