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?

• A. At some point, the upstream data provider switched everything to multi-line JSON.
• B. The badRecordsPath location is accumulating many small files.
• C. The engineer forgot to set the option " cloudFiles.quarantineMode " = " rescue " .
• D. The source data is valid JSON but does not conform to the defined schema in some way.

A data engineer needs to provide access to a group named manufacturing-team. The team needs privileges to create tables in the quality schema.
Which set of SQL commands will grant a group named manufacturing-team to create tables in a schema named production with the parent catalog named manufacturing with the least privileges?

• A. GRANT CREATE TABLE ON SCHEMA manufacturing.quality TO manufacturing-team; GRANT USE SCHEMA ON SCHEMA manufacturing.quality TO manufacturing-team; GRANT USE CATALOG ON CATALOG manufacturing TO manufacturing-team;
• B. GRANT CREATE TABLE ON SCHEMA manufacturing.quality TO manufacturing-team; GRANT CREATE SCHEMA ON SCHEMA manufacturing.quality TO manufacturing-team; GRANT USE CATALOG ON CATALOG manufacturing TO manufacturing-team;
• C. GRANT CREATE TABLE ON SCHEMA manufacturing.quality TO manufacturing-team; GRANT CREATE SCHEMA ON SCHEMA manufacturing.quality TO manufacturing-team; GRANT CREATE CATALOG ON CATALOG manufacturing TO manufacturing-team;
• D. GRANT USE TABLE ON SCHEMA manufacturing.quality TO manufacturing-team; GRANT USE SCHEMA ON SCHEMA manufacturing.quality TO manufacturing-team; GRANT USE CATALOG ON CATALOG manufacturing TO manufacturing-team;

The security team is exploring whether or not the Databricks secrets module can be leveraged for connecting to an external database.
After testing the code with all Python variables being defined with strings, they upload the password to the secrets module and configure the correct permissions for the currently active user. They then modify their code to the following (leaving all other variables unchanged).

Which statement describes what will happen when the above code is executed?

• A. The connection to the external table will succeed; the string value of password will be printed in plain text.
• B. The connection to the external table will succeed; the string " redacted " will be printed.
• C. An interactive input box will appear in the notebook; if the right password is provided, the connection will succeed and the encoded password will be saved to DBFS.
• D. An interactive input box will appear in the notebook; if the right password is provided, the connection will succeed and the password will be printed in plain text.
• E. The connection to the external table will fail; the string " redacted " will be printed.

Which configuration parameter directly affects the size of a spark-partition upon ingestion of data into Spark?

• A. spark.sql.adaptive.coalescePartitions.minPartitionNum
• B. spark.sql.autoBroadcastJoinThreshold
• C. spark.sql.files.maxPartitionBytes
• D. spark.sql.adaptive.advisoryPartitionSizeInBytes
• E. spark.sql.files.openCostInBytes

A data engineer is developing a Lakeflow Declarative Pipeline (LDP) using a Databricks notebook directly connected to their pipeline. After adding new table definitions and transformation logic in their notebook, they want to check for any syntax errors in the pipeline code without actually processing data or running the pipeline.
How should the data engineer perform this syntax check?

• A. Switch to a workspace file instead of a notebook to access validation and diagnostics tools.
• B. Open the web terminal from the notebook and run a shell command to validate the pipeline code.
• C. Disconnect the notebook from the pipeline and reconnect it to a compute cluster to access code validation features.
• D. Use the "Validate" option in the notebook to check for syntax errors.