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Snowflake DEA-C02 Exam Syllabus Topics:

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

1. You are tasked with optimizing a data pipeline that loads data from an external cloud storage location into Snowflake, transforms it, and then loads it into reporting tables. The pipeline is experiencing intermittent performance issues. You want to proactively identify and address these issues. Which of the following monitoring techniques and Snowflake features would be MOST effective for continuous monitoring and performance optimization?

A) Focus exclusively on optimizing SQL queries and data transformations. Monitoring is unnecessary since Snowflake automatically handles performance optimization.
B) Enable Snowflake's Auto-Suspend and Auto-Resume features on the warehouse. This is the most efficient way to manage resources and optimize costs, indirectly addressing performance concerns.
C) Rely solely on Snowflake's default query history and resource monitors. These automatically track performance and usage, providing sufficient insight without additional configuration.
D) Implement custom logging and monitoring using Snowflake Scripting and User-Defined Functions (UDFs) to capture granular performance metrics at each stage of the pipeline and push notifications via external functions to a monitoring service.
E) Utilize Snowflake's System Functions to periodically query performance views (e.g., 'QUERY_HISTORY, ' and write aggregated metrics to a dedicated monitoring table. Configure a scheduled task to generate alerts based on predefined thresholds.

2. You are designing a data pipeline to ingest streaming data from Kafka into Snowflake. The data contains nested JSON structures representing customer orders. You need to transform this data and load it into a flattened Snowflake table named 'ORDERS FLAT'. Given the complexities of real-time data processing and the need for custom logic to handle certain edge cases within the JSON payload, which approach provides the MOST efficient and maintainable solution for transforming and loading this streaming data into Snowflake?

A) Create a Python UDF that calls 'json.loads()' to parse the JSON within Snowflake and then use SQL commands with 'LATERAL FLATTEN' to navigate and extract the desired fields into a staging table. Afterward, use a separate SQL script to insert from staging to the final table 'ORDERS FLAT
B) Use Snowflake's built-in JSON parsing functions within a Snowpipe COPY INTO statement, combined with a 'CREATE VIEW' statement on top of the loaded data. The view will use 'LATERAL FLATTEN' to present the data in the desired flattened structure without physically transforming the underlying data.
C) Implement a custom external function (UDF) written in Java to parse and transform the JSON data before loading it into Snowflake. Configure Snowpipe to call this UDF during the data ingestion process. This UDF will flatten the JSON structure and return a tabular format directly insertable into 'ORDERS FLAT.
D) Use Snowflake's Snowpipe with a COPY INTO statement that utilizes the 'STRIP OUTER ARRAY option to handle the JSON array, combined with a series of SQL queries with 'LATERAL FLATTEN' functions to extract the nested data after loading into a VARIANT column.
E) Utilize a third-party ETL tool (like Apache Spark) to consume the data from Kafka, perform the JSON flattening and transformation logic, and then use the Snowflake connector to load the data into the 'ORDERS FLAT' table in batch mode.

3. A data engineer is using the Snowflake Spark connector to write data to a Snowflake table. The write operation fails consistently with the error 'net.snowflake.client.jdbc.SnowflakeSQLException: SQL execution error: String '. ' is too long (maximum is 16777216)'. Which of the following is the most likely cause and how can it be resolved using Spark Connector?

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

4. You are designing a data pipeline using Snowpipe to ingest data from multiple S3 buckets into a single Snowflake table. Each S3 bucket represents a different data source and contains files in JSON format. You want to use Snowpipe's auto-ingest feature and a single Snowpipe object for all buckets to simplify management and reduce overhead. However, each data source has a different JSON schem a. How can you best achieve this goal while ensuring data is loaded correctly and efficiently into the target table?

A) Use a single Snowpipe with a generic FILE FORMAT that can handle all possible JSON schemas. Implement a VIEW on top of the target table to transform and restructure the data based on the source bucket.
B) Use a single Snowpipe and leverage Snowflake's ability to call a user-defined function (UDF) within the 'COPY INTO' statement to transform the data based on the S3 bucket path. The UDF can parse the bucket path and apply the appropriate JSON schema transformation.
C) Use a single Snowpipe and leverage Snowflake's VARIANT data type to store the raw JSON data. Create separate external tables, each pointing to a specific S3 bucket, and use SQL queries to transform and load the data into the target table.
D) Since Snowpipe cannot handle multiple schemas with a single pipe, pre-process the data in S3 using an AWS Lambda function to transform all files into a common schema before they are ingested by the Snowpipe.
E) Create a separate Snowpipe for each S3 bucket. Although this creates more Snowpipe objects, it allows you to specify a different FILE FORMAT and transformation logic for each data source.

5. You are tasked with implementing row-level security (RLS) on a 'SALES' table to restrict access based on the 'REGION' column. Users with the 'NORTH REGION ROLE should only see data where 'REGION = 'NORTH". You've created a row access policy named north_region_policy'. After applying the policy to the 'SALES table, users with the 'NORTH REGION ROLE are still seeing all rows.
Which of the following is the MOST likely reason for this and how can it be corrected?

A) The policy needs to be explicitly refreshed. Execute 'REFRESH ROW ACCESS POLICY north_region_policy ON SALES;'
B) The user has not logged out and back in since the role was granted to them. Force the user to re-authenticate.
C) The is not enabled. Execute 'ALTER ROW ACCESS POLICY ON SALES SET ENABLED = TRUE;'
D) The policy function within is not using the correct context function to determine the user's role. It should use 'CURRENT ROLE()' instead of 'CURRENT_USER()'
E) The ' does not have the USAGE privilege on the database and schema containing the 'SALES' table. Grant the USAGE privilege to the role.

Solutions: