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Snowflake SnowPro Advanced: Data Engineer (DEA-C02) Sample Questions:

1. You are developing a Snowpark Python application that needs to process data from a Kafka topic. The data is structured as Avro records. You want to leverage Snowpipe for ingestion and Snowpark DataFrames for transformation. What is the MOST efficient and scalable approach to integrate these components?

A) Configure Snowpipe to ingest the raw Avro data into a VARIANT column in a staging table. Utilize a Snowpark DataFrame with Snowflake's get_object field function on the variant to get an object by name, and create columns based on each field.
B) Use Snowpipe to ingest the Avro data to a raw table stored as binary. Then, use a Snowpark Python UDF with an Avro deserialization library to convert the binary data to a Snowpark DataFrame.
C) Convert Avro data to JSON using a Kafka Streams application before ingestion. Use Snowpipe to ingest the JSON data to a VARIANT column and then process it using Snowpark DataFrames.
D) Create a Kafka connector that directly writes Avro data to a Snowflake table. Then, use Snowpark DataFrames to read and transform the data from that table.
E) Create external functions to pull the Avro data into a Snowflake stage and then read the data with Snowpark DataFrames for transformation.

2. You have a Snowflake stage pointing to an external cloud storage location containing numerous Parquet files. A directory table is created on top of it. Over time, some files are deleted or moved from the external location. You notice discrepancies between the directory table's metadata and the actual files present in the storage location. Choose the option that best describes how Snowflake handles these discrepancies and the actions you should take.

A) Snowflake automatically updates the directory table in real-time, reflecting the changes immediately. No action is needed.
B) Snowflake automatically detects deleted files and marks them as 'invalid' in the directory table. Queries will automatically exclude these invalid files.
C) Snowflake does not track file deletions. If a file is deleted from cloud storage after being added to a directory table, Snowflake continues to reference the deleted file, potentially causing errors during data loading. Run 'VALIDATE on the directory table.
D) Snowflake does not automatically detect these changes. You must manually refresh the directory table using 'ALTER DIRECTORY TABLE ... REFRESH' to synchronize the metadata. Snowflake does not provide an automated cleanup of metadata associated with removed files.
E) Snowflake requires you to drop and recreate the directory table periodically to synchronize the metadata with the external storage. Using 'ALTER DIRECTORY TABLE REFRESH' will not remove deleted files from the directory table's metadata. However, these invalid files wont be shown in select unless explicitly used.

3. You have a table named 'EMPLOYEES with a retention period of 1 day. You accidentally deleted several important rows from this table, but you need to recover the data'. You know the deletion occurred 25 hours ago. What actions should be taken to attempt to recover the deleted data, and what outcome can you expect? Assume you are working in an Enterprise edition of Snowflake account.

A) Attempt to clone the table using Time Travel to a point in time before the deletion, then extract the deleted rows. Expect the recovery to be successful as long as the deletion occurred within the data retention period.
B) Attempt to use Time Travel or cloning to recover the data. Expect the recovery to fail because the deletion occurred outside the I-day data retention period.
C) Since its Enterprise edition of Snowflake account, the Time travel and cloning will work with 7 days retention period, hence attemtp clone table using Time Travel and recover data successfully
D) Attempt to use UNDROP TABLE command if the table was dropped. Expect the recovery to be successful as long as the deletion occurred within the data retention period.
E) Attempt to use Time Travel to query the table before the deletion and re-insert the deleted rows. Expect the recovery to be successful as long as the deletion occurred within the data retention period.

4. You have a Snowflake table 'CUSTOMER DATA' with the following schema:

Your downstream application requires a new column 'PHONE NUMBER VARCHAR(20)' in the 'CUSTOMER DATA' table. You need to add this column without causing any downtime or disruption to the application. What is the MOST appropriate approach?

A) Execute 'ALTER TABLE CUSTOMER DATAADD COLUMN PHONE NUMBER VARCHAR(20);'. This will add the column with a NULL value for existing rows.
B) Create a new table 'CUSTOMER DATA NEW' with the new column, copy data from 'CUSTOMER DATA' to 'CUSTOMER DATA NEW, drop "CUSTOMER DATA', and rename 'CUSTOMER DATA NEW' to 'CUSTOMER DATA'.
C) Create a view 'CUSTOMER on top of 'CUSTOMER DATA' that includes the new 'PHONE NUMBER column with a default value. Update the application to use the view instead of the table.
D) Create a new stream on the table 'CUSTOMER DATA'. Then, create a new table 'CUSTOMER DATA NEW' with the extra PHONE NUMBER column as part of the DDL. Use the stream and table to update data from the old to new table, Then, drop "CUSTOMER_DATA' , and rename to 'CUSTOMER DATA'.
E) create a temporary table which is a copy of CUSTOMER_DATA but with the additional column. swap the CUSTOMER_DATA and CUSTOMER DATA TEMP tables.

5. You are managing a Snowflake environment where data retention is set to the default 1 day for all databases and tables. You need to clone a production table, 'CUSTOMER DATA, to a development environment to test some complex transformations. However, after cloning, you realize that the original 'CUSTOMER DATA' table in production was accidentally dropped 2 days ago. Which of the following statements accurately describe the situation and what can be done, if anything?

A) While the source 'CUSTOMER DATA' is dropped and outside the retention period, the clone created will remain available and fully functional for querying. It is an independent copy. The clone is usable indefinitely, within the limits of your Snowflake account storage.
B) The cloned 'CUSTOMER DATA' table in the development environment will no longer be accessible because the source table has been dropped and the data retention period has expired. The clone is effectively useless.
C) The cloned 'CUSTOMER DATA' table in the development environment will inherit the data retention period from the original 'CUSTOMER DATA' table, so it will also be inaccessible after 1 day if dropped in development.
D) The cloned 'CUSTOMER DATA' table in the development environment will remain accessible because the clone operation created a point-in-time snapshot.
E) The cloned 'CUSTOMER DATA' table will only remain accessible if the source database was also cloned at the same time. If only the table was cloned, the cloned table is unusable after 1 day.

Solutions: