I am trying to follow along with a training course, but I am consistently running into an error loading a CSV with Spark from DBFS.  Specifically, I keep getting an "Invalid format detected error".  Has anyone else encountered this and found a solution?  Code an error message below:

Code

file_path = f"dbfs:/mnt/dbacademy-datasets/scalable-machine-learning-with-apache-spark/v02/airbnb/sf-listings/sf-listings-2019-03-06.csv"

raw_df = spark.read.csv(file_path, header="true", inferSchema="true", multiLine="true", escape='"')

display(raw_df)

 Error

AnalysisException: Incompatible format detected.

A transaction log for Delta was found at `dbfs://_delta_log`,
but you are trying to read from `dbfs:/mnt/dbacademy-datasets/scalable-machine-learning-with-apache-spark/v02/airbnb/sf-listings/sf-listings-2019-03-06.csv` using format("csv"). You must use
'format("delta")' when reading and writing to a delta table.

To disable this check, SET spark.databricks.delta.formatCheck.enabled=false
To learn more about Delta, see https://docs.databricks.com/delta/index.html
---------------------------------------------------------------------------
AnalysisException                         Traceback (most recent call last)
File <command-3615789235235519>:3
      1 file_path = f"dbfs:/mnt/dbacademy-datasets/scalable-machine-learning-with-apache-spark/v02/airbnb/sf-listings/sf-listings-2019-03-06.csv"
----> 3 raw_df = spark.read.csv(file_path, header="true", inferSchema="true", multiLine="true", escape='"')
      5 display(raw_df)

File /databricks/spark/python/pyspark/instrumentation_utils.py:48, in _wrap_function.<locals>.wrapper(*args, **kwargs)
     46 start = time.perf_counter()
     47 try:
---> 48     res = func(*args, **kwargs)
     49     logger.log_success(
     50         module_name, class_name, function_name, time.perf_counter() - start, signature
     51     )
     52     return res

File /databricks/spark/python/pyspark/sql/readwriter.py:729, in DataFrameReader.csv(self, path, schema, sep, encoding, quote, escape, comment, header, inferSchema, ignoreLeadingWhiteSpace, ignoreTrailingWhiteSpace, nullValue, nanValue, positiveInf, negativeInf, dateFormat, timestampFormat, maxColumns, maxCharsPerColumn, maxMalformedLogPerPartition, mode, columnNameOfCorruptRecord, multiLine, charToEscapeQuoteEscaping, samplingRatio, enforceSchema, emptyValue, locale, lineSep, pathGlobFilter, recursiveFileLookup, modifiedBefore, modifiedAfter, unescapedQuoteHandling)
    727 if type(path) == list:
    728     assert self._spark._sc._jvm is not None
--> 729     return self._df(self._jreader.csv(self._spark._sc._jvm.PythonUtils.toSeq(path)))
    730 elif isinstance(path, RDD):
    732     def func(iterator):

File /databricks/spark/python/lib/py4j-0.10.9.5-src.zip/py4j/java_gateway.py:1321, in JavaMember.__call__(self, *args)
   1315 command = proto.CALL_COMMAND_NAME +\
   1316     self.command_header +\
   1317     args_command +\
   1318     proto.END_COMMAND_PART
   1320 answer = self.gateway_client.send_command(command)
-> 1321 return_value = get_return_value(
   1322     answer, self.gateway_client, self.target_id, self.name)
   1324 for temp_arg in temp_args:
   1325     temp_arg._detach()

File /databricks/spark/python/pyspark/errors/exceptions.py:234, in capture_sql_exception.<locals>.deco(*a, **kw)
    230 converted = convert_exception(e.java_exception)
    231 if not isinstance(converted, UnknownException):
    232     # Hide where the exception came from that shows a non-Pythonic
    233     # JVM exception message.
--> 234     raise converted from None
    235 else:
    236     raise

AnalysisException: Incompatible format detected.

A transaction log for Delta was found at `dbfs://_delta_log`,
but you are trying to read from `dbfs:/mnt/dbacademy-datasets/scalable-machine-learning-with-apache-spark/v02/airbnb/sf-listings/sf-listings-2019-03-06.csv` using format("csv"). You must use
'format("delta")' when reading and writing to a delta table.

To disable this check, SET spark.databricks.delta.formatCheck.enabled=false
To learn more about Delta, see https://docs.databricks.com/delta/index.html