20+ practice questions focused on Importing Data — one of the most tested topics on the Databricks Certified Data Analyst Associate exam. Each question includes a detailed explanation so you learn why the right answer is correct.
Start Importing Data PracticeRefer to the exhibit. The analyst finds that the resulting DataFrame has column names like '_c0', '_c1'. What is missing in the configuration?
Explanation: When Spark reads a CSV file and produces column names like '_c0', '_c1', it indicates that Spark did not find a header row. This happens because either the 'header' option was not set to 'true', or the delimiter (sep) in the file does not match the default (comma), causing the entire first line (including the header) to be parsed as a single column or incorrectly split, thus failing to identify the header row.
A data analyst is creating a Databricks SQL dashboard that must ingest a Parquet file stored in cloud object storage. The analyst wants to define the table using pure SQL in the Databricks SQL editor, without using Python or notebooks. Which SQL command should be used to create a table that reads directly from the Parquet file?
Explanation: The correct SQL command to create a table that reads directly from a Parquet file in cloud storage is CREATE TABLE ... USING parquet LOCATION 'path'. This defines an external table in Databricks SQL, allowing the analyst to query the data without moving it. Other options either copy the data, use outdated syntax, or create a view instead of a table.
A data analyst wants to upload a small CSV file from their local machine to the Databricks workspace to analyze it in a notebook. They need a quick, one-time upload without writing code. Which Databricks feature should they use?
Explanation: The Add Data UI is designed for quick, code-free data uploads. It allows analysts to upload local files, preview data, and create tables with a few clicks. Other options require coding, command-line tools, or are less direct for one-time uploads.
A data analyst is ingesting a stream of JSON event files from cloud object storage into a Bronze Delta table using Auto Loader in a Databricks notebook. The source directory receives new, uniquely named files continuously, and the analyst sets the schema location to a Unity Catalog volume path. After several days, the analyst notices that some newly arriving files are being ignored entirely, while others are processed. Which Auto Loader behavior most likely explains why a subset of files is never ingested?
Explanation: Auto Loader maintains a persistent state store (typically backed by RocksDB) under the schema location to track which file paths have already been discovered and processed. When a file with a previously recorded path reappears—common with rename-based ETL that writes to a temp name and renames into the target directory—the stream treats it as already seen and skips it. This produces the pattern of silently ignored files while other, genuinely new paths continue to be ingested.
A data analyst is creating a Delta table from a Parquet file stored in DBFS. They use the following SQL command: CREATE TABLE my_table USING DELTA LOCATION '/mnt/data/parquet_files/'. After running the command, they notice the table contains no data. What is the most likely cause?
Explanation: Delta tables rely on a transaction log to identify data files. A directory of Parquet files without the _delta_log will result in an empty table when created with USING DELTA. The analyst must either convert the Parquet files to Delta or create a Parquet table instead.
+15 more Importing Data questions available
Practice all Importing Data questions1. Baseline your knowledge
Start with 10 questions to gauge your current understanding of Importing Data. This tells you whether you need a concept refresher or just practice.
2. Review every explanation
For each question — right or wrong — read the full explanation. Understanding why an answer is correct is more valuable than knowing the answer itself.
3. Focus on exam traps
Importing Data questions on the Databricks-DA-Assoc frequently use trap wording. Look for subtle differences in answers that test your precision, not just general knowledge.
4. Reach 80% consistently
Do repeated sessions until you score 80%+ three times in a row. Then move to mixed-mode practice to test cross-topic recall under realistic conditions.
The exact number varies per candidate. Importing Data is tested as part of the Databricks Certified Data Analyst Associate blueprint. Practicing with targeted Importing Data questions ensures you can handle any format or difficulty that appears.
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Difficulty is subjective, but Importing Data is a high-priority exam concept tested in multiple ways — direct recall, scenario analysis, and command-output interpretation. Consistent practice is the best way to build confidence.
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