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

1. You have a VARIANT column named 'raw_data' in a Snowflake table 'eventS , containing nested JSON data'. You need to extract specific fields Cevent_id', 'timestamp' , and 'user.user_id') and load them into a relational table 'structured_events' with columns 'event_id' , 'timestamp' , and 'user_id', respectively. However, some entries may be missing the 'user' object. Which of the following SQL statements will achieve this while handling missing 'user' objects gracefully and ensuring data integrity, and also efficiently handle potentially large JSON payloads?

A) Option C
B) Option E
C) Option A
D) Option D
E) Option B

2. You are tasked with creating a JavaScript stored procedure in Snowflake to perform a complex data masking operation on sensitive data within a table. The masking logic involves applying different masking rules based on the data type and the column name. Which approach would be the MOST secure and maintainable for storing and managing these masking rules? Assume performance is not your primary concern but code reuse and maintainability is the most important thing.

A) Hardcoding the masking rules directly within the JavaScript stored procedure.
B) Defining the masking rules as JSON objects within the stored procedure code.
C) Storing masking logic in Javascript UDFs and calling these UDFs dynamically within the stored procedure based on column names and datatype
D) Using external stages and pulling the masking rules from a configuration file during stored procedure execution.
E) Storing the masking rules in a separate Snowflake table and querying them within the stored procedure.

3. You are designing a data product for the Snowflake Marketplace that provides daily weather forecasts. You need to ensure that consumers of your data receive the latest forecast data every morning automatically with minimal latency. Which of the following strategies offers the MOST efficient and cost-effective solution for updating the shared data?

A) Create a stored procedure that truncates and reloads the shared tables with the latest forecast data from a staging table. Schedule this stored procedure to run every morning at 6 AM using a Snowflake task.
B) Create a scheduled task that executes a full refresh of the shared tables every morning at 6 AM. This task uses CREATE OR REPLACE TABLE AS SELECT to rebuild the tables with the latest forecast data.
C) Implement a continuous data pipeline using Snowflake Streams and Tasks to incrementally update the shared tables as new forecast data becomes available. The stream tracks changes and tasks apply those changes to the shared tables.
D) Share the raw data files stored in an external stage with the consumers. Consumers will then need to create their own pipelines to process and load the data.
E) Manually upload a new CSV file containing the latest forecast data to a Snowflake stage and then load it into the shared tables every morning at 6 AM.

4. You are building a data pipeline to ingest JSON data from an external API using the Snowflake SQL API. The API returns nested JSON structures, and you need to extract specific fields and load them into a Snowflake table with a flattened schem a. You also need to handle potential schema variations and missing fields in the JSON data. Which approach provides the MOST robust and flexible solution for this scenario, maximizing data quality and minimizing manual intervention?

A) Parse the JSON data in your client application (e.g., Python) using a library like 'json' or , transform the data into a tabular format, and then use the Snowflake Connector for Python to load the data into Snowflake.
B) Use a stored procedure with dynamic SQL to parse the JSON, create new tables based on the current schema, and load data. Maintain metadata on table versions.
C) Utilize Snowflake's schema detection feature during the COPY INTO process. This will automatically infer the schema from the JSON data and create the table accordingly.
D) Use the 'JSON TABLE function in a Snowflake SQL query executed via the SQLAPI to flatten the JSON data and extract the required fields. Handle missing fields by using 'DEFAULT values in the table schema.
E) Load the raw JSON data into a VARIANT column in Snowflake. Create a series of views on top of the VARIANT column to extract the required fields and handle schema variations using 'TRY TO ' functions.

5. You are designing a data loading process for a high-volume streaming data source. The data arrives as Avro files in an AWS S3 bucket. You need to load this data into a Snowflake table with minimal latency and operational overhead. Which of the following combinations of Snowflake features and configurations would be MOST suitable for this scenario? (Select TWO)

A) Use the 'COPY INTO' command with a scheduled task that runs every 5 minutes to load new files from the S3 bucket.
B) Implement Snowpipe with auto-ingest configured to listen for S3 event notifications whenever a new Avro file is added to the bucket.
C) Configure an external table pointing to the S3 bucket and query the Avro files directly from Snowflake.
D) Use a Kafka connector to stream data directly from the Kafka topic to Snowflake.
E) Create a custom Spark application that reads Avro files from S3, transforms the data, and then writes it to Snowflake using the Snowflake Spark connector.

Solutions: