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

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

1. 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) Use Snowflake's Secure Vault (Secret) feature to store the encryption key and retrieve it securely within the UDF.
B) Pass the encryption key as an argument to the UDF each time it is called.
C) Encrypt the key using a weaker encryption algorithm before storing it in an internal stage to balance security and performance.
D) Store the encryption key directly within the JavaScript UDF code as a string variable.
E) 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.

2. A data engineer is responsible for maintaining a Snowflake data warehouse. They notice a significant slowdown in the performance of a specific query that aggregates data from a table called 'SALES DATA', which contains billions of rows. The query is used for generating daily sales reports. The engineer suspects that the issue might be related to clustering. How would you diagnose the effectiveness of the clustering on the 'SALES DATA' table and identify potential improvements?

A) Examine the query profile in the Snowflake web interface to identify stages that are scanning large amounts of data. Check if these stages are benefiting from clustering.
B) Use the 'DESCRIBE TABLE SALES_DATA' command and check the 'clustering_key' property, then run 'SELECT SYSTEM$MEASURE CLUSTERING DEPTH('SALES to check the average depth of the table. Compare the clustering depth to the number of micro- partitions to assess clustering effectiveness. A depth closer to zero is best.
C) Use the 'VALIDATE table command. This command detects fragmentation in the data due to poor clustering.
D) Use the SYSTEM$CLUSTERING_INFORMATION' function to analyze the clustering depth of the table. A high clustering depth indicates poor clustering.
E) Use the 'SHOW TABLES command to view the clustering key defined on the table. Verify that the clustering key is appropriate for the query workload.

3. Which of the following statements are true regarding using Dynamic Data Masking and Column-Level Security in Snowflake? (Select all that apply)

A) Dynamic Data Masking policies can reference external tables directly without requiring special grants.
B) Column-Level Security via views provides more fine-grained control over data access compared to Dynamic Data Masking.
C) Using both Dynamic Data Masking and Column-Level Security (e.g. views) on the same column is redundant and will result in an error.
D) Dynamic Data Masking can be used to apply different masking rules based on the user's role, IP address, or other contextual factors.
E) Dynamic Data Masking is applied at query runtime, while Column-Level Security through views or roles is applied when the object is created.

4. You have created a Snowflake Iceberg table that points to data in an AWS S3 bucket. After some initial data ingestion, you realize that the schema in the Iceberg table does not perfectly match the schema of the underlying Parquet files in S3. Specifically, one of the columns in the Iceberg table is defined as 'VARCHAR , while the corresponding column in the Parquet files is stored as 'INT. What will be the most likely behavior when you query this Iceberg table in Snowflake?

A) The query will fail with an error indicating a data type mismatch between the Iceberg table schema and the underlying Parquet file schema.
B) Snowflake will attempt to cast the data, and if a cast fails (e.g., 'INT' value is too large to fit in 'VARCHAR), the query will return an error only for those specific rows. Other rows will be processed correctly.
C) Snowflake will automatically cast the SINT' data in the Parquet files to 'VARCHAR during query execution, and the query will succeed without any errors or warnings.
D) The query will succeed, but the 'VARCHAR column will contain 'NULL' values for all rows where the underlying Parquet files contain 'INT' values.
E) The query will succeed, but the result will be unpredictable and may vary depending on the specific data values in the Parquet files.

5. You are designing a data warehouse for an e-commerce company. One of the requirements is to provide fast analytics on order fulfillment times by region. You have two tables: 'ORDERS: Contains order information, including ID, 'ORDER DATE, 'REGION ID, and 'FULFILLMENT DATE. 'REGIONS': Contains region information, including 'REGION ID' and Due to the large size of the 'ORDERS' table and the complexity of calculating fulfillment times, you decide to use materialized views.
Which of the following combinations of materialized view definition and Snowflake features would BEST optimize query performance and minimize data staleness for this scenario? Choose two options.

A) Use Snowflake's search optimization service on the 'ORDERS' table instead of creating a materialized view.
B) Partition the 'ORDERS' table by 'ORDER_DATE and create a materialized view that calculates 'FULFILLMENT_TIME grouped by REGION_NAME , clustering by 'ORDER DATE'
C) create a materialized view that joins 'ORDERS' and 'REGIONS', calculates 'FULFILLMENT_TIME' grouped by 'REGION_NAME, and cluster by 'REGION NAM Configure incremental data refreshes.
D) Create a materialized view that joins 'ORDERS and 'REGIONS', calculates 'FULFILLMENT TIME', and groups by 'REGION NAME'. Do not specify a clustering key.
E) Create a materialized view that joins 'ORDERS and 'REGIONS', calculates the difference between 'FULFILLMENT DATE' and 'ORDER DATE as , and groups by REGION_NAME. Cluster the view by ' REGION_NAME.

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