Quick Answer
The correct answer is to use the `mergeSchema` option set to `true` when reading the CSV files. This option, applied as `spark.read.option("mergeSchema","true").csv(path)`, automatically infers the schema from each CSV file and then merges all distinct columns across the files into a single unified schema, handling schema evolution seamlessly. On the Microsoft Azure Data Engineer Associate DP-203 exam, this question tests your understanding of how Azure Databricks processes semi-structured data with varying schemas, a common real-world scenario when ingesting CSV files from Azure Data Lake Storage Gen2. A frequent trap is confusing `mergeSchema` with `inferSchema`; while `inferSchema` detects types per file, it does not merge columns across files, so files with extra columns would be silently dropped or cause errors. Remember the mnemonic: "Merge merges, infer infers alone" — always pair `mergeSchema` with `true` when your CSV files have different column sets.
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. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. 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?
spark.read.option("mergeSchema","true").csv(path)
Infers and merges schemas.
spark.read.format("delta").load(path)
Why wrong: Delta format is not CSV.
spark.read.option("inferSchema","true").csv(path)
Why wrong: Infers schema but does not merge across files.
spark.read.csv(path)
Why wrong: Does not handle schema evolution.
Answer choices
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("mergeSchema","true").csv(path)
Option C (spark.read.format("csv").option("mergeSchema","true").load(path)) is correct because mergeSchema enables automatic schema inference and merging across files with different schemas. Option A loads without schema evolution. Option B infers schema but does not merge. Option D is for Delta Lake, not CSV.
Key principle: NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
Answer analysis
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 this is correct
Infers and merges schemas.
Related concept
Static NAT maps one inside address to one outside address.
spark.read.format("delta").load(path)
Why it's wrong here
Delta format is not CSV.
spark.read.option("inferSchema","true").csv(path)
Why it's wrong here
Infers schema but does not merge across files.
spark.read.csv(path)
Why it's wrong here
Does not handle schema evolution.
Common exam traps
NAT is not only about the public address. The inside/outside interface roles and the ACL or rule that matches traffic are just as important.
Detailed technical explanation
NAT questions usually test address translation, overload/PAT behaviour, static mappings and whether the right traffic is being translated. Read the interface direction and address terms carefully.
Key takeaway
NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
Real-world example
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
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related DP-203 NAT questions on configuration and troubleshooting.
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FAQ
Develop data processing — This question tests Develop data processing — Static NAT maps one inside address to one outside address..
The correct answer is: spark.read.option("mergeSchema","true").csv(path) — Option C (spark.read.format("csv").option("mergeSchema","true").load(path)) is correct because mergeSchema enables automatic schema inference and merging across files with different schemas. Option A loads without schema evolution. Option B infers schema but does not merge. Option D is for Delta Lake, not CSV.
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related DP-203 NAT questions on configuration and troubleshooting.
Static NAT maps one inside address to one outside address.
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Last reviewed: Jun 21, 2026
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