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

1. A data engineering team is managing a Snowflake warehouse that supports a high volume of ad-hoc queries from data analysts exploring a large, semi-structured JSON dataset containing website clickstream data'. The query performance is frequently slow, and analysts are complaining about long wait times. The warehouse is already sized appropriately. You have identified that many of the queries filter on nested JSON attributes that are not explicitly indexed. Considering only query acceleration service features, what is the MOST effective approach to improve query performance for these ad-hoc queries without modifying the queries themselves or significantly increasing storage costs?

A) Enable Search Optimization Service for the table containing the JSON data. Selectively enable search optimization on the specific columns and nested paths that are frequently used in WHERE clause predicates.
B) Use a combination of materialized views and query acceleration for best performance.
C) Enable the Materialized View feature to create materialized views over the frequently queried nested JSON attributes.
D) Enable Automatic Clustering on the table to improve data organization based on query patterns.
E) Create a dedicated virtual warehouse specifically for ad-hoc queries, and enable query acceleration on this warehouse.

2. You're designing a near real-time data pipeline for clickstream data using Snowpipe Streaming. The data volume is extremely high, with bursts exceeding 1 million events per second. Your team reports intermittent ingestion failures and latency spikes. Considering the constraints of Snowpipe Streaming, which of the following strategies would be MOST effective in mitigating these issues, assuming the data format is optimized and network latency is minimal?

A) Increase the number of Snowflake virtual warehouses to handle the increased load.
B) Switch from Snowpipe Streaming to Classic Snowpipe, as it is more resilient to high data volumes.
C) Implement client-side retry logic with exponential backoff and jitter to handle transient errors and avoid overwhelming the service.
D) Implement a message queue (e.g., Kafka) in front of Snowpipe Streaming to buffer incoming events and smooth out the traffic spikes.
E) Reduce the size of each micro-batch being sent to Snowpipe Streaming to minimize the impact of individual failures.

3. You are tasked with designing a data sharing solution where data from multiple tables residing in different databases within the same Snowflake account needs to be combined into a single view that is then shared with a consumer account. The view must also implement row-level security based on the consumer's role. Which of the following options represent valid approaches for implementing this solution? Select all that apply.

A) Create a standard view with a stored procedure to handle the joins across databases and use EXECUTE AS OWNER to avoid permission issues. This standard view should be shared.
B) Create a secure view that joins tables from different databases using fully qualified names (e.g., 'DATABASEI .SCHEMAI . TABLET) and implement row-level security using a masking policy based on the CURRENT_ROLE() function.
C) Create a secure view that joins tables from different databases and implement row-level security using a row access policy based on the CURRENT ROLE() function. Masking policy cannot provide role based access control so will not work.
D) Create a standard view that joins tables from different databases using aliases and implement row-level security using a UDF that checks the consumer's role and filters the data accordingly.
E) Create a view for each table and then build a final view using 'UNION ALL' to combine data from all the views and implement row-level security with a role based row access policy. Standard views should not be used in data sharing.

4. You have a directory table 'my_directory_table' pointing to a stage containing CSV files with headers. You need to query the directory table to find all files modified in the last 24 hours and load those CSV files using COPY INTO into a target table Assume the target table exists and has appropriate schema'. Which of the following SQL statements, or set of statements, will accomplish this efficiently? Note: Consider efficient file loading.

A)

B)

C)

D)

E)

5. A financial services company stores sensitive customer data, including credit card numbers, in a Snowflake table called 'CUSTOMER DATA. You need to implement dynamic data masking on the 'CREDIT CARD NUMBER column. You want to ensure that only users with the FINANCE ADMIN' role can view the unmasked credit card numbers. All other users should see a masked version of the data'. Which of the following set of commands is the MOST efficient and secure way to achieve this?