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

1. You have a Snowflake Task that is designed to transform and load data into a target table. The task relies on a Stream to detect changes in a source table. However, you notice that the task is intermittently failing with a 'Stream STALE' error, even though the data in the source table is continuously updated. What are the most likely root causes and the best combination of solutions to prevent this issue? (Select TWO)

A) The Stream has reached its maximum age (default 14 days) and expired. There is no way to recover data from an expired Stream. You need to recreate the Stream and reload the source table.
B) The Stream is not configured with 'SHOW INITIAL ROWS = TRUE, causing initial changes to be missed and eventually leading to staleness. Recreate the stream with this parameter set to TRUE.
C) The source table is being modified with DDL operations (e.g., ALTER TABLE ADD COLUMN), which are not supported by Streams. Use Table History to track schema changes and manually adjust the Stream's query if needed. Use 'COPY GRANTS' during the DDL.
D) The Task is not running frequently enough, causing the Stream to accumulate too many changes before being consumed, exceeding its retention period. Increase the task's execution frequency or increase the stream's 'DATA RETENTION TIME IN DAYS
E) DML operations (e.g., UPDATE, DELETE) being performed on the source table are affecting rows older than the Stream's retention period. Reduce the stream's 'DATA RETENTION TIME IN DAYS' to match the oldest DML operation on the source table.

2. You are setting up a Kafka connector to load data from a Kafka topic into a Snowflake table. You want to use Snowflake's automatic schema evolution feature to handle potential schema changes in the Kafka topic. Which of the following is the correct approach to enable and configure automatic schema evolution using the Kafka Connector for Snowflake?

A) Set 'snowflake.ingest.file.name' to an existing file in a stage.
B) Set the property to 'true' and the 'snowflake.ingest.stage' to an existing stage.
C) Set the 'value.converter.schemas.enable' to 'true' and provide Avro schemas and also, configure the Snowflake table with appropriate data types for each field. Schema Evolution is not supported by the Kafka Connector for Snowflake.
D) Automatic schema evolution is not directly supported by the Kafka Connector for Snowflake. You must manually manage schema changes in Snowflake.
E) Set the 'snowflake.data.field.name' property to the name of the column in the Snowflake table where the JSON data will be stored as a VARIANT, and set 'snowflake.enable.schematization' to 'true'.

3. A large e-commerce company is experiencing performance issues with its daily sales report queries. These queries aggregate data from a fact table 'SALES FACT (100 billion rows) and several dimension tables, including 'CUSTOMER DIM', 'PRODUCT DIM', and 'DATE DIM'. The queries are run every morning and are essential for business decision-making. The team has identified that the 'SALES FACT table's primary key is 'SALE ID, but the queries frequently filter and join on 'CUSTOMER and 'PRODUCT ID. You want to use query acceleration service for these reports without changing query logic. Which combination of actions will MOST effectively leverage query acceleration service, assuming sufficient credits?

A) Enable clustering on the 'CUSTOMER DIM' and 'PRODUCT DIMS tables.
B) Increase the size of the virtual warehouse used for running the reports and enable query acceleration. Set the parameter to a high value.
C) Create materialized views that pre-aggregate the sales data based on 'CUSTOMER ID', 'PRODUCT ID, and 'DATE ID, then enable query acceleration on the virtual warehouse.
D) Enable Automatic Clustering on the 'SALES FACT table based on 'CUSTOMER ID' and 'PRODUCT ID, then enable query acceleration on the virtual warehouse.
E) Enable search optimization on the columns 'CUSTOMER ID' and 'PRODUCT ID of the 'SALES FACT table, then enable query acceleration on the virtual warehouse. Set the QUERY_ACCELERATION_MAX_SCALE_FACTOR parameter to a reasonable value based on testing.

4. A Snowflake data pipeline ingests data from multiple external sources into a RAW DATA table. A transformation process then moves the data to a ANALYTICS DATA table, applying several complex UDFs written in Java and Python for data cleansing and enrichment. Performance is significantly slower than expected. Which combination of techniques would BEST improve the performance of this transformation pipeline?

A) Reduce the number of UDF calls by consolidating them into a single, more complex UDF. Replace the transformation pipeline with a series of COPY INTO statements.
B) Rewrite the UDFs in SQL or Snowpark Python/Java for better integration with the Snowflake engine and leverage vectorization where possible; cache intermediate results using temporary tables.
C) Use external functions instead of UDFs to offload the processing to an external compute environment and configure auto-scaling for the virtual warehouse.
D) Implement data partitioning in the RAW DATA table based on ingestion time and switch to using stored procedures instead of transformation pipelines.
E) Increase the virtual warehouse size and re-cluster the ANALYTICS DATA table based on the most frequently filtered columns.

5. Consider a scenario where you have a Snowflake table named 'CUSTOMER DATA' containing customer IDs (INTEGER) and encrypted credit card numbers (VARCHAR). You need to create a secure JavaScript UDF to decrypt these credit card numbers using a custom encryption key stored securely within Snowflake's internal stage, and then mask all but the last four digits of the decrypted number for data protection. Which of the following actions are necessary to ensure both functionality and security while adhering to Snowflake's best practices for UDF development and security?

A) Store the encryption key in a separate file on an internal stage accessible only by the UDF's service account and load the key from the file within the UDF at runtime.
B) Encrypt the key using a weaker encryption algorithm before storing it in an internal stage to balance security and performance.
C) Store the encryption key directly within the JavaScript UDF code as a string variable.
D) Use Snowflake's Secure Vault (Secret) feature to store the encryption key and retrieve it securely within the UDF.
E) Pass the encryption key as an argument to the UDF each time it is called.

Solutions: