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

1. You are working on a Snowpark Python application that needs to process a stream of data from Kafka, perform real-time aggregations, and store the results in a Snowflake table. The data stream is highly variable, with occasional spikes in traffic that overwhelm your current Snowpark setup, leading to significant latency in processing. Which of the following strategies, either individually or in combination, would be MOST effective to handle these traffic spikes and ensure near real-time processing?

A) Implement dynamic warehouse scaling. Utilize Snowflake's Resource Monitors and the ability to programmatically resize warehouses through Snowpark. Monitor the queue depth or latency of your Snowpark application, and dynamically scale up the warehouse size when thresholds are exceeded. Then, scale it back down when traffic subsides.
B) Configure the Snowflake warehouse used by your Snowpark application to use auto-suspend and auto-resume with a short auto-suspend time to minimize costs during periods of low traffic.
C) Implement a message queuing system (e.g., RabbitMQ, Kafka) between Kafka and your Snowpark application to buffer incoming data during traffic spikes.
D) Use Snowpark's async actions (e.g., to offload data processing to separate threads or processes, allowing your main Snowpark application to continue receiving data.
E) Use 'CACHE RESULT for all queries in snowpark that use Kafka

2. You are designing a data pipeline in Snowflake that involves several tasks chained together. One of the tasks, 'task B' , depends on the successful completion of 'task A'. 'task_B' occasionally fails due to transient network issues. To ensure the pipeline's robustness, you need to implement a retry mechanism for 'task_B' without using external orchestration tools. What is the MOST efficient way to achieve this using native Snowflake features, while also limiting the number of retries to prevent infinite loops and excessive resource consumption? Assume the task definition for 'task_B' is as follows:

A) Utilize Snowflake's external functions to call a retry service implemented in a cloud function (e.g., AWS Lambda or Azure Function). The external function will handle the retry logic and update the task status in Snowflake.
B) Create a separate task, 'task_C', that is scheduled to run immediately after 'task will check the status of 'task_BS in the TASK HISTORY view. If 'task_B' failed, 'task_c' will re-enable 'task_B' and suspend itself. Use the parameter on 'task_B' to limit the number of retries.
C) Leverage Snowflake's event tables like QUERY_HISTORY and TASK _ HISTORY in the ACCOUNT_USAGE schema joined with custom metadata tags to correlate specific transformation steps to execution times and resource usage. Also set up alerting based on defined performance thresholds.
D) Modify the task definition of 'task_B' to include a SQL statement that checks for the success of 'task_R in the TASK_HISTORY view before executing the main logic. If 'task_A' failed, use ' SYSTEM$WAIT to introduce a delay and then retry the main logic. Implement a counter to limit the number of retries.
E) Embed the retry logic directly within the stored procedure called by 'task_B'. The stored procedure should catch exceptions related to network issues, introduce a delay using 'SYSTEM$WAIT , and retry the main logic. Implement a loop with a maximum retry count.

3. You are working with a very large Snowflake table named 'CUSTOMER TRANSACTIONS which is clustered on 'CUSTOMER ID and 'TRANSACTION DATE. After noticing performance degradation on queries that filter by 'TRANSACTION AMOUNT and 'REGION' , you decide to explore alternative clustering strategies. Which of the following actions, when performed individually, will LEAST likely improve query performance specifically for queries filtering by 'TRANSACTION AMOUNT and 'REGION', assuming you can only have one clustering key?

A) Creating a materialized view that pre-aggregates data by 'TRANSACTION_AMOUNT and 'REGION'.
B) Adding ' TRANSACTION_AMOUNT and 'REGIO!V to the existing clustering key while retaining 'CUSTOMER_ID and 'TRANSACTION_DATE
C) Creating a new table clustered on 'TRANSACTION_AMOUNT and 'REGION', and migrating the data.
D) Dropping the existing clustering key and clustering on 'TRANSACTION_AMOUNT' and 'REGION'.
E) Creating a search optimization on 'TRANSACTION_AMOUNT' and 'REGION' columns.

4. A data engineer observes that a Snowflake query, used for generating a daily sales report, consistently runs slower each day, despite the dataset size remaining relatively stable. The query joins a large sales table (SALES) with a smaller product dimension table (PRODUCT) on PRODUCT ID. You've already confirmed that virtual warehouse sizing is adequate and data clustering is enabled on SALES(SALE DATE). Analyze the following scenarios and identify the MOST likely cause of the performance degradation and potential solution:

A) The Snowflake query optimizer is selecting a suboptimal execution plan because the query is complex. Rewrite the query using temporary tables to break down the logic into smaller steps.
B) The data clustering on SALES(SALE DATE) is ineffective as the query also filters on PRODUCT CATEGORY, which isn't clustered. Re-cluster SALES by both SALE DATE and PRODUCT CATEGORY.
C) The virtual warehouse is being overwhelmed by concurrent queries. Implement workload management rules to prioritize the sales report query.
D) The join between SALES and PRODUCT is causing excessive data spill to local storage due to an inefficient join order. Consider using a JOIN hint, specifically a BROADCAST hint on the PRODUCT table (small table).
E) The statistics on the PRODUCT table are outdated. Run 'ANALYZE TABLE PRODUCT' to refresh them.

5. You are tasked with calculating the daily moving average of sales for each product category in your Snowflake data warehouse using Snowpark Python. You need to handle cases where there might be missing sales data for certain days. You have the following sales data available in a table named with columns: (DATE), (VARCHAR), and 'sales_amount' (NUMBER). Which of the following Snowpark Python code snippets correctly calculates the 7-day moving average of sales amount per product category, filling in missing sales days with 0, and handles potential division by zero?

A) Option A
B) Option D
C) Option E
D) Option C
E) Option B

Solutions: