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Snowflake DEA-C02 Exam Syllabus Topics:

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

1. You are ingesting data from an AWS S3 bucket into a Snowflake table using a COPY INTO statement. The COPY INTO command fails with an error indicating 'Invalid stage location specified'. You have verified that the stage name is correct and the Snowflake user has the necessary privileges to access the stage. However, the error persists. Which of the following are potential causes and solutions for this issue?

A) The external stage definition in Snowflake includes an incorrect storage integration. Examine and correct the STORAGE INTEGRATION parameter in the CREATE STAGE statement.
B) The S3 bucket is encrypted using KMS and the Snowflake integration lacks the necessary key grant. Check the KMS key policy to ensure the storage integration IAM role has decrypt permission.
C) The network policy configured in Snowflake is blocking access to the AWS S3 endpoint. Check the network policy rules and ensure they allow outbound traffic to the S3 region.
D) The IAM role associated with the Snowflake stage is incorrect or does not have sufficient permissions to access the S3 bucket. Verify the IAM role configuration and permissions.
E) The S3 bucket policy is not correctly configured to allow Snowflake to assume the IAM role. Review the bucket policy to ensure it grants access to the Snowflake IAM role.

2. You are troubleshooting a slowly performing query in Snowflake that aggregates data from a large ORDERS table (10 billion rows) partitioned by ORDER DATE. The query execution plan shows significant 'Remote Spill to Disk'. Which of the following actions would be MOST effective in reducing the spill and improving query performance? Assume all statistics are up-to-date and the data is properly clustered by ORDER_DATE.

A) Optimize the query to leverage data pruning based on ORDER DATE by ensuring the query filters on a specific or limited range of ORDER DATE values.
B) Reduce the number of columns selected in the query, only selecting those that are essential for the aggregation.
C) Increase the value of the parameter. This allows the warehouse to scale up further if needed.
D) Increase the virtual warehouse size. This will provide more memory for the query to execute.
E) Rewrite the query to use window functions instead of aggregate functions.

3. You have configured a Kafka Connector to load JSON data into a Snowflake table named 'ORDERS. The JSON data contains nested structures. However, Snowflake is only receiving the top- level fields, and the nested fields are being ignored. Which configuration option within the Kafka Connector needs to be adjusted to correctly flatten and load the nested JSON data into Snowflake?

A) Configure the 'snowflake.data.field.name' property to specify the column in the Snowflake table where the entire JSON should be loaded as a VARIANT.
B) Set the 'value.converter.schemas.enable' property to 'true'.
C) Apply the 'org.apache.kafka.connect.transforms.Flatten' transformation to the 'transforms' configuration.
D) Use the 'transforms' configuration with the 'org.apache.kafka.connect.transforms.ExtractField$Value' transformation to extract specific fields.
E) Enable the 'snowflake.ingest.stage' property and set it to a Snowflake internal stage.

4. You have configured a replication group to replicate a database 'CUSTOMER DATA' from your primary Snowflake account (AWS us- east-I) to your secondary Snowflake account (Azure eastus). After a recent network outage, the replication process stopped. Upon investigation, you find that some tables in the 'CUSTOMER DATA' database in the primary account have been modified (schema changes). You need to resume replication and ensure data consistency in the secondary account, with minimal impact on users querying the secondary database. Which of the following commands or sequence of commands would be the MOST appropriate to refresh the secondary database while minimizing downtime for query users?

A) Issue a 'ALTER REPLICATION GROUP REFRESH$ command. This will automatically handle schema changes and resume replication.
B) Issue a 'ALTER DATABASE REFRESH FROM command. This command automatically handles schema changes and ensures data consistency with minimal downtime.
C) First, pause all running queries on the secondary account. Then, execute a full refresh using 'ALTER DATABASE REFRESH FROM Finally, resume the queries on the secondary account.
D) Suspend the replication group with 'ALTER REPLICATION GROUP SUSPEND;' Then execute the following: 'ALTER DATABASE ENABLE REPLICATION TO ACCOUNT ALTER REPLICATION GROUP RESUME;'
E) First, suspend the replication group with 'ALTER REPLICATION GROUP SUSPEND;'. Then, drop the secondary database 'DROP DATABASE . Finally, recreate the secondary database as a replica: 'CREATE DATABASE AS REPLICA OF and resume the replication group 'ALTER REPLICATION GROUP RESUME;'.

5. You are tasked with creating a UDTF in Snowflake to perform a complex data transformation that requires external libraries (e.g., for advanced string manipulation or data analysis). The transformation involves cleaning and standardizing addresses from a table containing millions of customer records. Which language and approach would be most appropriate and efficient for this scenario?

A) Java UDTF with necessary JAR files uploaded to Snowflake's internal stage, leveraging external libraries for address parsing and standardization.
B) Python UDTF leveraging Anaconda packages (e.g., 'addressparser' , 'pandas') for advanced address parsing and standardization, utilizing Snowflake's optimized execution environment for Python.
C) SQL UDF with nested CASE statements for address standardization.
D) Scala UDTF leveraging sbt to manage dependencies to achieve address parsing and standardization.
E) JavaScript UDF utilizing regular expressions for simple string replacements.

Solutions: