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A data engineer is using Auto Loader to read incoming JSON data as it arrives. They have configured Auto Loader to quarantine invalid JSON records but notice that over time, some records are being quarantined even though they are well-formed JSON. The code snippet is: df = (spark.readStream .format("cloudFiles") .option("cloudFiles.format", "json") .option("badRecordsPath", "/tmp/somewhere/badRecordsPath") .schema("a int, b int") .load("/Volumes/catalog/schema/raw_data/")) What is the cause of the missing data?
Correct Answer: C
Comprehensive and Detailed Explanation From Exact Extract of Databricks Data Engineer Documents: Databricks Auto Loader quarantines records that fail schema validation, even if they are syntactically valid JSON. The documentation explains that "records that do not match the defined schema are considered corrupt and written to the bad records path." In this case, the data likely contains valid JSON fields whose structure or data types differ from the declared schema (a int, b int). For example, if a field expected as an integer arrives as a string, Auto Loader will quarantine it. The presence of badRecordsPath confirms this behavior. The cloudFiles.quarantineMode option controls quarantine behavior but is unrelated to valid data that fails schema validation. Thus, the correct cause is C.