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

1. Given the following scenario: You have an external table 'EXT SALES in Snowflake pointing to a data lake in Azure Blob Storage. The storage account network rules are configured to only allow specific IP addresses and virtual network subnets, enhancing security. You are getting intermittent errors when querying 'EXT SALES. Which of the following could be the cause(s) and the corresponding solution(s)? Select all that apply.

A) The table function cache is stale, causing access to non-existent files. Solution: Run 'ALTER EXTERNAL TABLE EXT_SALES REFRESH'.
B) The Snowflake service principal does not have the correct permissions on the Azure Blob Storage account. Solution: Ensure the Snowflake service principal has the 'Storage Blob Data Reader' role assigned to it.
C) The network connectivity between Snowflake and Azure Blob Storage is unstable. Solution: Implement retry logic in your queries to handle transient network errors.
D) The file format specified in the external table definition does not match the actual format of the files in Azure Blob Storage. Solution: Update the 'FILE_FORMAT parameter in the external table definition to match the correct file format.
E) The Snowflake IP addresses used to access the Azure Blob Storage are not whitelisted in the storage account's firewall settings. Solution: Obtain the Snowflake IP address ranges for your region and add them to the storage account's allowed IP addresses.

2. Consider a table 'EVENT DATA' that stores events from various applications. The table has columns like 'EVENT ID, 'EVENT TIMESTAMP, 'APPLICATION ID', 'USER ID', and 'EVENT _ TYPE. A significant portion of queries filter on 'EVENT TIMESTAMP ranges AND 'APPLICATION ID. The data volume is substantial, and query performance is crucial. You observe high clustering depth after initial loading. Which combination of actions will provide the MOST effective performance optimization, addressing both clustering depth and query performance?

A) Cluster the table on 'USER ICY and rely solely on Snowflake's automatic reclustering feature, without running 'OPTIMIZE TABLES manually.
B) Create multiple materialized views: one filtering on common 'EVENT TIMESTAMP' ranges, and another filtering on common 'APPLICATION ID' values.
C) Create separate tables for each ' , each clustered on 'EVENT_TIMESTAMP'. Then, create a view that UNION ALLs these tables.
D) Cluster the table on 'EVENT TIMESTAMP' and periodically run 'OPTIMIZE TABLE EVENT DATA' using a small warehouse. Also, create a separate table clustered on 'APPLICATION
E) Cluster the table on '(EVENT TIMESTAMP, APPLICATION IDY and periodically run 'OPTIMIZE TABLE EVENT DATA' using a warehouse sized appropriately for the table size. Then, monitor clustering depth regularly.

3. You have a large Snowflake table 'WEB EVENTS that stores website event data'. This table is clustered on the 'EVENT TIMESTAMP column. You've noticed that certain queries filtering on a specific 'USER ID' are slow, even though 'EVENT TIMESTAMP clustering should be helping. You decide to investigate further Which of the following actions would be MOST effective in diagnosing whether the clustering on 'EVENT TIMESTAMP is actually benefiting these slow queries?

A) Execute 'SHOW TABLES' and check the 'clustering_key' column to ensure that the table is indeed clustered on 'EVENT _ TIMESTAMP'.
B) Query the 'QUERY_HISTORY view to see the execution time of the slow query and compare it to the average execution time of similar queries without a 'USER filter.
C) Run ' EXPLAIN' on the slow query and examine the 'partitionsTotal' and 'partitionsScanned' values. A significant difference indicates effective clustering.
D) Use the SYSTEM$CLUSTERING_INFORMATIOW function to get the 'average_overlaps' for the table and 'EVENT_TIMESTAMP' column. A low value indicates good clustering.
E) Run 'SYSTEM$ESTIMATE QUERY COST to estimate the query cost to see if the clustering is impacting the cost.

4. A healthcare provider stores patient data in Snowflake, including 'PATIENT ID', 'NAME, 'MEDICAL HISTORY , and 'INSURANCE ID. They need to comply with HIPAA regulations. As a data engineer, you need to ensure that PHI (Protected Health Information) is masked appropriately based on user roles. Which of the following steps are NECESSARY to achieve this using Snowflake's data masking features and RBAC? (Select all that apply)

A) Enforce multi-factor authentication (MFA) for all users accessing the Snowflake environment to enhance security and prevent unauthorized access to sensitive data.
B) Apply the created masking policies to the corresponding columns in the patient data tables, ensuring that the masking policies are designed to reveal only the necessary information based on the user's role (e.g., doctors see full medical history, nurses see limited medical history, admins see de-identified data).
C) Identify the columns containing PHI and create appropriate masking policies for each column (e.g., masking 'NAME, 'MEDICAL HISTORY, INSURANCE_ID).
D) Grant the 'OWNERSHIP privilege on the 'PATIENT table to the 'ACCOUNTADMIN' role, ensuring complete control and management of the data by the administrator.
E) Create custom roles representing different user groups within the organization (e.g., 'DOCTOR, 'NURSE, 'ADMIN') and grant them the necessary privileges to access the data, including 'SELECT on the tables and views containing patient data.

5. You are designing a Snowpark Python application to process streaming data from a Kafka topic and land it into a Snowflake table 'STREAMED DATA. Due to the nature of streaming data, you want to achieve the following: 1. Minimize latency between data arrival and data availability in Snowflake. 2. Ensure exactly-once processing semantics to prevent data duplication. 3. Handle potential schema evolution in the Kafka topic without breaking the pipeline. Which combination of Snowpark and Snowflake features, applied with the correct configuration, would BEST satisfy these requirements? Select all that apply.

A) Use Snowpipe with auto-ingest and configure it to trigger on Kafka topic events. Define a VARIANT column in 'STREAMED_DATX to handle schema evolution.
B) Use Snowflake Connector for Kafka to load data into a staging table. Then, use Snowpark Python to transform and load the data into 'STREAMED_DATR within a single transaction. Implement schema evolution logic in the Snowpark code to handle changes in the Kafka topic schema.
C) Use Snowflake's native Kafka connector to load data into a staging table. Then, use a Task and Stream combination, using a Snowpark Python UDF, to transform and load the data into 'STREAMED DATA' within a single transaction, handling schema evolution by casting columns to their new types or dropping missing column data.
D) Implement a Snowpark Python UDF that consumes data directly from the Kafka topic using a Kafka client library. Write data into 'STREAMED_DATX within a single transaction. Use a structured data type for the 'STREAMED DATA'.
E) Utilize Snowflake Streams on in conjunction with Snowpark to transform and cleanse the data after it has been ingested by Snowpipe. Apply a merge statement to update an external table of parquet files.

Solutions: