A data engineer needs to fetch data from a REST API that returns JSON and land it into a Delta table in ADLS. The API requires an API key in the header. Which approach correctly accomplishes this?

Use `spark.read.format("jdbc")` to read from the REST API endpoint.

Use `dbutils.fs.mount` to mount the API endpoint as a filesystem.

Use `requests.get()` to call the API, parse the JSON, create a Spark DataFrame, and write it with `df.write.format("delta").save("abfss://container@storage.dfs.core.windows.net/path")`.

Use `spark.read.format("json").load("https://api.example.com/data")` directly.

Which DataFrame writer method is used to write data directly into a Unity Catalog table?

df.write.saveAsTable("catalog.schema.table")

df.write.jdbc("catalog.schema.table", properties)

df.write.format("delta").save("catalog.schema.table")

df.write.parquet("catalog.schema.table")

A data engineer needs to connect to an external PostgreSQL database from a Databricks notebook. Which component is required to establish this JDBC connection?

A Databricks SQL warehouse endpoint

A REST API endpoint that exposes the database schema

A JDBC driver JAR that is compatible with the database and accessible to the Spark cluster

A Unity Catalog external location pointing to the database

A data engineer needs to read a table from an external SQL Server database and write it to ADLS as Delta. The table is large, and the engineer wants to minimize the data transferred over the network. Which JDBC option should they use?

Use `partitionColumn` to parallelize the read.

Use `numPartitions` to increase parallelism.

Use `fetchsize` to control the number of rows fetched per round trip.

Use `pushDownPredicate` to filter data in the database.

DatabricksCertified Data Engineer Associate

Domain 2Objective 5

Use JDBC/ODBC or REST Clients in Notebooks to Land Data into Cloud Storage or Directly into UnityCatalog–governed Tables, Usually Orchestrated and Scheduled with Lakeflow Jobs. DATA-ENGINEER-ASSOCIATE Practice Questions (Page 3)

Part of the Data Ingestion and Loading domain, which accounts for 21% of the DATA-ENGINEER-ASSOCIATE exam. Databricks does not publish an official question count, but from its 90-minute exam (~35–60 total, ~7–13 in this domain), expect 1–2 from this objective — we provide 23 practice questions to prepare you well beyond it. (estimate)

23questions here

5free pages

8concepts

21%of the exam

Questions 11–15

11expert · hard
A data engineer is designing a nightly job that reads a large table from an external database and writes it to cloud storage as Delta. The job must be idempotent so that re-runs do not create duplicates. The table has no timestamp column for incremental loads. Which write mode should be used?
Select an answer first
12application · medium
A data engineer needs to fetch data from a REST API that returns JSON and land it into a Delta table in ADLS. The API requires an API key in the header. Which approach correctly accomplishes this?
Select an answer first
13foundation · easy
Which DataFrame writer method is used to write data directly into a Unity Catalog table?
Select an answer first
14foundation · easy
A data engineer needs to connect to an external PostgreSQL database from a Databricks notebook. Which component is required to establish this JDBC connection?
Select an answer first
15application · medium
A data engineer needs to read a table from an external SQL Server database and write it to ADLS as Delta. The table is large, and the engineer wants to minimize the data transferred over the network. Which JDBC option should they use?
Select an answer first

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