Quick Answer
The answer is `spark.read.parquet()`. This method is the correct choice because it is the native Spark DataFrame reader specifically designed to handle Parquet files, a columnar storage format that provides efficient compression and encoding schemes, making it ideal for big data workloads in Azure Databricks. On the Microsoft Azure Data Engineer Associate DP-203 exam, this question tests your understanding of Spark’s structured data APIs and how they integrate with Azure Data Lake Storage Gen2; a common trap is confusing `spark.read.format("parquet").load()` with the shorthand `spark.read.parquet()`, but both are functionally equivalent, though the latter is more concise and directly signals the file format. The exam often pairs this with scenarios involving schema inference or partitioning, so remember that Parquet preserves schema metadata automatically. A useful memory tip: think “Parquet is the default format for Spark, so `spark.read.parquet()` is the shortcut you reach for first.”
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.
Your organization uses Azure Data Lake Storage Gen2 (ADLS Gen2) and wants to transform data using Azure Databricks. The data is stored in Parquet format. You need to read the data into a Spark DataFrame. Which DataFrame reader method should you use?
spark.read.avro()
Why wrong: Avro reader is for Avro format.
spark.read.json()
Why wrong: JSON reader is for JSON files.
spark.read.parquet()
The parquet() method reads Parquet files directly.
spark.read.csv()
Why wrong: CSV reader is for comma-separated values, not Parquet.
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.parquet()
Option C is correct because the data is stored in Parquet format, and the Spark DataFrame reader method `spark.read.parquet()` is specifically designed to read Parquet files, which is a columnar storage format optimized for big data processing in Azure Databricks.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
spark.read.avro()
Why it's wrong here
Avro reader is for Avro format.
spark.read.json()
Why it's wrong here
JSON reader is for JSON files.
spark.read.parquet()
Why this is correct
The parquet() method reads Parquet files directly.
Related concept
Read the scenario before looking for a memorised answer.
spark.read.csv()
Why it's wrong here
CSV reader is for comma-separated values, not Parquet.
Common exam traps
The trap here is that candidates may confuse file format reader methods (e.g., using `spark.read.avro()` for Parquet data) due to assuming all binary formats are interchangeable, but each reader method is strictly tied to its specific file format.
Detailed technical explanation
Parquet stores data in a columnar format using techniques like dictionary encoding and run-length encoding, which significantly reduces I/O and storage costs in ADLS Gen2. When using `spark.read.parquet()`, Spark automatically infers the schema from the Parquet metadata, enabling predicate pushdown and column pruning for faster queries. In real-world scenarios, this is critical for large-scale ETL pipelines where reading only necessary columns can reduce data scanned by 90% or more.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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.
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FAQ
Develop data processing — This question tests Develop data processing — Read the scenario before looking for a memorised answer..
The correct answer is: spark.read.parquet() — Option C is correct because the data is stored in Parquet format, and the Spark DataFrame reader method `spark.read.parquet()` is specifically designed to read Parquet files, which is a columnar storage format optimized for big data processing in Azure Databricks.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 24, 2026
This DP-203 practice question is part of Courseiva's free Microsoft 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 DP-203 exam.
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