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

1. A financial services company, 'Acme Finance', wants to share aggregated, anonymized transaction data with a research firm, 'Data Insights', through a Snowflake Data Clean Room. Acme Finance needs to ensure that Data Insights can only analyze the data using pre- defined aggregate functions and cannot access the raw, underlying transactional details. Acme Finance has already created a secure view to share the aggregated data'. Which of the following steps are necessary to grant Data Insights access to the data securely while enforcing the required restrictions?

A) Create a masking policy that only allows aggregate functions to be executed by Data Insights' role and apply it to the relevant columns in the underlying table. Then, grant SELECT privilege on the secure view directly to the role used by Data Insights' Snowflake account.
B) Create an external function that Data Insights can call to execute pre-approved aggregate functions on the underlying data. Grant USAGE on the function to Data Insights' role and create a secure view that uses that function.
C) Create a row access policy that restricts the rows returned based on the role used by Data Insights. Then, grant SELECT privilege on the secure view directly to the role used by Data Insights' Snowflake account.
D) Create a share object and grant USAGE privilege on the database containing the secure view to the share. Then, grant SELECT privilege on the secure view to the share. Finally, share the share with Data Insights' Snowflake account using their account identifier.
E) Grant SELECT privilege on the secure view directly to the role used by Data Insights' Snowflake account.

2. You are using Snowpark to perform a complex join operation between two large tables: 'ORDERS (1 OOGB) and 'CUSTOMER (50GB). The join is performed on 'ORDERS.CUSTOMER ID = CUSTOMER.ID. The query is running slower than expected. You have already confirmed that the warehouse size is adequate. Which of the following strategies, applied in combination , would most likely improve the join performance within a Snowpark context?

A) Analyze the query profile in Snowflake's web UI to identify the specific bottleneck (e.g., excessive data spilling, high CPU utilization) and address it directly.
B) Use 'session.add_import to add external JAR dependencies. This would enable use of external libraries and improve performance.
C) Ensure both tables are clustered on the join keys CORDERS.CUSTOMER_ID' and 'CUSTOMER.ID').
D) Use Snowpark's 'hint function to force a broadcast join, assuming the 'CUSTOMER table can fit into memory on the worker nodes.
E) Increase the 'AUTO RESIZE' setting on the warehouse to automatically scale up the warehouse size when the load increases.

3. A data engineering team is responsible for an ELT pipeline that loads data into Snowflake. The pipeline has two distinct stages: a high- volume, low-complexity transformation stage using SQL on raw data, and a low-volume, high-complexity transformation stage using Python UDFs that leverages an external service for data enrichment. The team is experiencing significant queueing during peak hours, particularly impacting the high-volume stage. You need to optimize warehouse configuration to minimize queueing. Which combination of actions would be MOST effective?

A) Create a single, X-Small warehouse and rely on Snowflake's query acceleration service to handle the workload.
B) Create two separate warehouses: a Small warehouse configured for auto-suspend after 5 minutes for the high-volume, low-complexity transformations and a Large warehouse configured for auto-suspend after 60 minutes for the low-volume, high-complexity transformations.
C) Create two separate warehouses: a Large, multi-cluster warehouse configured for auto-scale for the high-volume, low-complexity transformations and a Small warehouse for the low-volume, high-complexity transformations.
D) Create a single, large (e.g., X-Large) warehouse and rely on Snowflake's automatic scaling to handle the workload.
E) Create two separate warehouses: a Medium warehouse for the high-volume, low-complexity transformations and an X-Small warehouse for the low-volume, high-complexity transformations.

4. A large e-commerce company uses Snowflake to store website clickstream data in a table named 'WEB EVENTS'. This table is partitioned using the 'EVENT DATE column. The company needs to analyze user behavior across different devices. A common query involves joining 'WEB EVENTS' with a smaller 'USER DEVICES' table (containing user-to-device mappings) to determine the device type for each event. However, the performance of this join operation is poor, especially when filtering 'WEB EVENTS' by a specific date range. The 'USER DEVICES table is small enough to fit in memory. What is the most effective approach to optimize this query for performance?

A) Use a standard 'JOIN' operation between 'WEB_EVENTS' and USER_DEVICES' without any modifications.
B) Create a materialized view that pre-joins 'WEB_EVENTS' and 'USER_DEVICES' tables without filtering
C) Convert the 'WEB EVENTS' table to use a VARIANT data type and query with JSON path expressions.
D) Broadcast the 'USER DEVICES table to all compute nodes before performing the join. (Hint: Consider using 'BROADCAST hint)
E) Use a 'LATERAL FLATTEN' function to process the data in parallel.

5. 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) Use the SYSTEM$CLUSTERING_INFORMATIOW function to get the 'average_overlaps' for the table and 'EVENT_TIMESTAMP' column. A low value indicates good clustering.
B) Execute 'SHOW TABLES' and check the 'clustering_key' column to ensure that the table is indeed clustered on 'EVENT _ TIMESTAMP'.
C) Run ' EXPLAIN' on the slow query and examine the 'partitionsTotal' and 'partitionsScanned' values. A significant difference indicates effective clustering.
D) Run 'SYSTEM$ESTIMATE QUERY COST to estimate the query cost to see if the clustering is impacting the cost.
E) 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.

Solutions: