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

1. You need to create a UDF in Snowflake to perform complex data validation. This UDF must access an external API to retrieve validation rules based on the input data'. You want to ensure that sensitive API keys are not exposed within the UDF's code and that the external API call is made securely. Which of the following approaches is the MOST secure and appropriate for this scenario?

A) Store the API key as an environment variable within the UDF's JavaScript code. Snowflake automatically encrypts environment variables for security.
B) Hardcode the API key directly into the UDF's JavaScript code, obfuscating it with base64 encoding.
C) Use a Snowflake Secret to securely store the API key. Retrieve the secret within the UDF using the 'SYSTEM$GET_SECRET function, and use 'SECURITY INVOKER with caution or define the UDF as 'SECURITY DEFINER with appropriate role based access controls .
D) Store the API key in a Snowflake table with strict access controls, and retrieve it within the UDF using a SELECT statement. Use 'SECURITY INVOKER to ensure the UDF uses the caller's privileges when accessing the table.
E) Pass the API key as an argument to the UDF when it is called. Rely on the caller to provide the correct key and keep it secure.

2. A data engineering team observes that queries against a large fact table ('SALES FACT') are slow, even after clustering and partitioning. The table contains columns like 'SALE ID', 'PRODUCT ID, 'CUSTOMER D', 'SALE DATE', 'QUANTITY', and 'PRICE' Queries commonly filter on 'PRODUCT ID' and 'SALE DATE. After implementing search optimization on these two columns, performance only marginally improves. You suspect the data distribution for 'PRODUCT ID' might be skewed. What steps can you take to further investigate and improve query performance?

A) Use to estimate the cost of search optimization on the 'SALES_FACT table and consider disabling it if the cost is too high.
B) Experiment with different clustering keys, possibly including 'PRODUCT_ID and "SALE_DATE in the clustering key.
C) Create separate tables for each "PRODUCT_ID' to improve query performance.
D) Analyze the cardinality and data distribution of the 'PRODUCT_ID column using 'APPROX COUNT_DISTINCT and histograms to confirm the skewness.
E) Drop and recreate the 'SALES FACT table, as the metadata might be corrupted.

3. A retail company wants to store product data in a Snowflake VARIANT column. The product data is currently in a relational table called 'PRODUCTS' with columns 'PRODUCT ID', 'PRODUCT NAME, 'CATEGORY, 'PRICE, and 'DISCOUNT. They want to create a JSON structure where each product is represented as a JSON object, and the entire result set is a JSON array. Which of the following SQL statements will achieve this transformation most efficiently?

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

4. You have a Snowflake Stream named 'PRODUCT CHANGES' created on a table 'PRODUCTS'. A downstream task attempts to consume records from the stream, but occasionally fails with a 'Table PRODUCTS has been altered' error. The 'PRODUCTS' table undergoes DDL changes (e.g., adding/dropping columns) infrequently, but these changes are necessary for evolving business requirements. How can you design a more resilient data pipeline that minimizes disruptions caused by DDL changes to the 'PRODUCTS' table while still leveraging the 'PRODUCT CHANGES' stream?

A) Implement error handling in the downstream task to automatically retry consuming records from the 'PRODUCT CHANGES' stream after a delay, assuming the DDL changes will be completed quickly.
B) Use a materialized view instead of a standard view as the source for the stream. Materialized views are less susceptible to issues when the underlying base table changes
C) Before executing any DDL changes on the 'PRODUCTS' table, drop and recreate the 'PRODUCT CHANGES' stream. This ensures the stream definition is always in sync with the table structure.
D) Create a new Stream on the 'PRODUCTS' table after each DDL change. The downstream task should dynamically switch to consuming from the new stream when the old stream encounters an error.
E) Create a task that monitors the 'PRODUCTS' table for DDL changes using 'INFORMATION SCHEMA. TABLES'. When a change is detected, pause the downstream task, execute the DDL change, and then resume the downstream task after a short delay.

5. You have a Snowflake table 'CUSTOMER DATA' with the following schema:

Your downstream application requires a new column 'PHONE NUMBER VARCHAR(20)' in the 'CUSTOMER DATA' table. You need to add this column without causing any downtime or disruption to the application. What is the MOST appropriate approach?

A) Create a view 'CUSTOMER on top of 'CUSTOMER DATA' that includes the new 'PHONE NUMBER column with a default value. Update the application to use the view instead of the table.
B) Create a new table 'CUSTOMER DATA NEW' with the new column, copy data from 'CUSTOMER DATA' to 'CUSTOMER DATA NEW, drop "CUSTOMER DATA', and rename 'CUSTOMER DATA NEW' to 'CUSTOMER DATA'.
C) Execute 'ALTER TABLE CUSTOMER DATAADD COLUMN PHONE NUMBER VARCHAR(20);'. This will add the column with a NULL value for existing rows.
D) create a temporary table which is a copy of CUSTOMER_DATA but with the additional column. swap the CUSTOMER_DATA and CUSTOMER DATA TEMP tables.
E) Create a new stream on the table 'CUSTOMER DATA'. Then, create a new table 'CUSTOMER DATA NEW' with the extra PHONE NUMBER column as part of the DDL. Use the stream and table to update data from the old to new table, Then, drop "CUSTOMER_DATA' , and rename to 'CUSTOMER DATA'.

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