- A
spark.read.option("mergeSchema","true").csv(path)
Why wrong: Incorrect. `mergeSchema` is not a valid option for CSV files; it is used for Delta Lake or Parquet to handle schema evolution.
- B
spark.read.format("delta").load(path)
Why wrong: Incorrect. `format("delta")` reads Delta Lake format, not CSV files.
- C
spark.read.option("inferSchema","true").csv(path)
Correct. `inferSchema` automatically infers the schema from CSV files, but does not handle schema evolution across files with varying schemas.
- D
spark.read.csv(path)
Why wrong: Incorrect. Reading CSV without options does not infer the schema or handle schema evolution.
Question 835 of 851
DP-203 inferSchema Practice Question
This DP-203 practice question tests your understanding of develop data processing. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. A key principle to apply: inferSchema. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
You need to process a large number of CSV files stored in Azure Data Lake Storage Gen2 using Azure Databricks. The files are nested in multiple folders, and the schema varies slightly between files. You want to automatically infer the schema and handle schema evolution. Which read option should you use?
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
✓
spark.read.option("inferSchema","true").csv(path)
Option C is the best choice because `inferSchema` allows Spark to automatically detect the schema from the CSV files. However, it does not handle schema evolution across files with varying schemas; each file's schema is inferred independently, and if schemas differ, you may encounter errors or missing columns. For true schema evolution, you would need to use a custom schema or union DataFrames. Options A and B are invalid: A uses `mergeSchema`, which is not a standard CSV read option—it is used for Delta Lake or Parquet—and B reads Delta format, not CSV. Option D reads without any schema inference or evolution handling.
Key principle: inferSchema
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
spark.read.option("mergeSchema","true").csv(path)
Why it's wrong here
Incorrect. `mergeSchema` is not a valid option for CSV files; it is used for Delta Lake or Parquet to handle schema evolution.
- ✗
spark.read.format("delta").load(path)
Why it's wrong here
Incorrect. `format("delta")` reads Delta Lake format, not CSV files.
- ✓
spark.read.option("inferSchema","true").csv(path)
Why this is correct
Correct. `inferSchema` automatically infers the schema from CSV files, but does not handle schema evolution across files with varying schemas.
Related concept
inferSchema
- ✗
spark.read.csv(path)
Why it's wrong here
Incorrect. Reading CSV without options does not infer the schema or handle schema evolution.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Candidates often assume `mergeSchema` works for CSV files, but it is only supported for Delta Lake and Parquet formats. For CSV, `inferSchema` is the correct way to automatically infer the schema, but it does not merge schemas across files with different structures.
Detailed technical explanation
How to think about this question
Under the hood, `mergeSchema` works by Spark reading all files in the path, computing a unified schema by taking the union of all columns across files, and setting missing columns to null. This is particularly useful in real-world scenarios where data pipelines append new columns over time, such as IoT sensor data where newer CSV files include additional metrics. However, `mergeSchema` can be computationally expensive for large numbers of files because it requires scanning all files to determine the schema before reading the data.
KKey Concepts to Remember
- inferSchema
- mergeSchema
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
inferSchema
Real-world example
How this comes up in practice
A media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
What to study next
Got this wrong? Here's your next step.
Review inferSchema, then practise related DP-203 questions on the same topic to reinforce the concept.
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FAQ
Questions learners often ask
What does this DP-203 question test?
Develop data processing — This question tests Develop data processing — inferSchema.
What is the correct answer to this question?
The correct answer is: spark.read.option("inferSchema","true").csv(path) — Option C is the best choice because `inferSchema` allows Spark to automatically detect the schema from the CSV files. However, it does not handle schema evolution across files with varying schemas; each file's schema is inferred independently, and if schemas differ, you may encounter errors or missing columns. For true schema evolution, you would need to use a custom schema or union DataFrames. Options A and B are invalid: A uses `mergeSchema`, which is not a standard CSV read option—it is used for Delta Lake or Parquet—and B reads Delta format, not CSV. Option D reads without any schema inference or evolution handling.
What should I do if I get this DP-203 question wrong?
Review inferSchema, then practise related DP-203 questions on the same topic to reinforce the concept.
What is the key concept behind this question?
inferSchema
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Last reviewed: Jul 4, 2026
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