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

1. Consider a scenario where you're optimizing a data pipeline in Snowflake responsible for aggregating sales data from multiple regions. You've identified that the frequent full refreshes of the target aggregated table are causing significant performance overhead and resource consumption. Which strategies could be employed to optimize these full refreshes without sacrificing data accuracy?

A) Replace the full refresh with a 'TRUNCATE TABLE' followed by an 'INSERT statement. This approach is faster than 'CREATE OR REPLACE TABLE' and reduces locking.
B) Implement incremental data loading using streams and tasks. This allows you to only process and load the changes that have occurred since the last refresh, reducing the amount of data that needs to be processed.
C) Schedule the full refreshes during off-peak hours when the Snowflake warehouse is less utilized. This minimizes the impact on other workloads but does not reduce the actual processing time.
D) Utilize Snowflake's Time Travel feature to clone the previous version of the aggregated table, apply the necessary changes to the clone, and then swap the clone with the original table using 'ALTER TABLE SWAP WITH'. Note that this will impact data availability during the swap operation.
E) Leverage Snowflake's search optimization service on the base tables. While costly, this will dramatically speed up full table scans performed in the aggregation.

2. You are tasked with loading a large dataset (50TB) of JSON files into Snowflake. The JSON files are complex, deeply nested, and irregularly structured. You want to maximize loading performance while minimizing storage costs and ensuring data integrity. You have a dedicated Snowflake virtual warehouse (X-Large).
Which combination of approaches would be MOST effective?

A) Load the JSON data using the COPY INTO command with no pre-processing. Create a VIEW on top of the raw VARIANT column to flatten the data for querying.
B) Load the JSON data using the COPY INTO command with gzip compression. Create a raw VARIANT column alongside projected relational columns for frequently accessed fields, and use materialized views to improve query performance.
C) Use Snowpipe with auto-ingest, create a raw VARIANT column alongside projected relational columns for frequently accessed fields, and use search optimization on those projected columns.
D) Use Snowpipe with auto-ingest, create a single VARIANT column in your target table, and rely solely on Snowflake's automatic schema detection.
E) Pre-process the JSON data using a Python script with Pandas to flatten the structure and convert it into a relational format like CSV. Then, load the CSV files using the COPY INTO command with gzip compression.

3. A financial services company is implementing Snowflake. They have a table 'CUSTOMER DATA' containing sensitive information like 'CREDIT CARD NUMBER, 'SSN', and 'ADDRESS'. They need to ensure that: 1) Analysts can only see the last four digits of the 'CREDIT CARD NUMBER. 2) Data scientists require full access to the 'ADDRESS' but should not see the 'SSN'. 3) A dedicated compliance role should be able to view all data in its original format for auditing purposes. Which of the following is the MOST efficient and secure approach to implement this using Snowflake's data masking and RBAC?

A) Replicate the CUSTOMER_DATA table three times, once for each user group (Analysts, Data Scientist and Compliance). Mask sensitive information by altering the data with the respective masking function.
B) Create masking policies on the 'SSN' , and 'ADDRESS' columns. Use conditional masking expressions based on the CURRENT ROLE() function to determine what data to show to each role (analysts, data scientists, compliance).
C) Create separate views for analysts and data scientists, applying masking policies within the views, and grant access to these views based on their respective roles. Additionally, grant the compliance role direct access to the base table.
D) Use data encryption for the entire 'CUSTOMER_DATA table and provide decryption keys to specific roles based on their access requirements. Provide the compliance role with the master key.
E) Create dynamic data masking policies on each sensitive column in the 'CUSTOMER_DATA table, associating these policies with specific roles using Snowflake's tag-based masking. Grant roles only the privileges needed to select the columns based on their requirements.

4. You are tasked with building a robust data quality monitoring system for a Snowflake data pipeline. The pipeline processes customer order data and loads it into a 'CUSTOMER ORDERS table. You need to implement checks to ensure that certain critical columns (e.g., 'ORDER ID, 'CUSTOMER ID', 'ORDER DATE, meet specific data quality requirements (e.g., not null, valid format, within acceptable range). You want to design a flexible and scalable solution that allows you to easily add, modify, and monitor data quality rules. Select the options to implement that and scale efficiently Assume there is a central Data Quality table for each metrics

A) Implement a Snowpark Python UDF that leverages a data quality library (e.g., Great Expectations) to define and execute data quality rules. The UDF takes a DataFrame representing the data to be checked and returns a DataFrame containing the data quality check results.
B) Develop a parameterized stored procedure that accepts the table name, column name, data quality rule definition, and threshold values as input parameters. This procedure then dynamically constructs and executes the SQL query to check the data quality rule.
C) Utilize Snowflake's native Data Governance features, such as data masking and row-level security, to enforce data quality rules.
D) Build a set of custom Snowflake Native Apps to monitor and report on data quality. Each App will focus on one or more critical tables or data quality checks
E) Create a series of individual SQL scripts, each checking a specific data quality rule for a specific column, and schedule these scripts to run using Snowflake tasks.

5. You are responsible for optimizing query performance on a Snowflake table called 'WEB EVENTS, which contains clickstream data'. The table has the following structure: CREATE TABLE WEB EVENTS ( event_id VARCHAR(36), user_id INT, event_time TIMESTAMP NTZ, event_type VARCHAR(50), page_url VARCHAR(255), device_type VARCHAR(50) Users frequently run queries that filter the 'WEB EVENTS table based on a combination of 'event_type', and a date range derived from 'event_time' You observe that these queries are consistently slow Which of the following strategies would be MOST effective in improving the performance of these frequently executed queries?

A) Create a clustering key with the following order: 'event_type' , 'device_type' , 'event_time' .
B) Add a column to the 'WEB EVENTS' table for the date part of 'event_time' and create a clustering key using the new date column along with and device_type' .
C) Create a materialized view that pre-aggregates data by 'event_type' , 'device_type' , and day (derived from 'event_time').
D) Create a clustering key on 'event_time' .
E) Create a search optimization service on the 'page_url' column.

Solutions: