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

1. You are tasked with creating a system to monitor the data quality of a 'SALES DATA" table. The table is updated daily with new sales records, and you need to ensure that the 'SALE AMOUNT column always contains positive values. You decide to use Snowflake tasks and streams for this purpose. Consider the following Snowflake script. What is the most appropriate way to modify the 'SALES DATA' table so the task has a 'WHEN' clause that runs only if the 'SALE AMOUNT has negative values? Assume the stream 'SALES DATA STREAM' is properly configured on 'SALES DATA'.

A)

B)

C)

D)

E)

2. 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) Use a combination of materialized views and query acceleration for best performance.
B) 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.
C) Enable Automatic Clustering on the table to improve data organization based on query patterns.
D) Enable the Materialized View feature to create materialized views over the frequently queried nested JSON attributes.
E) Create a dedicated virtual warehouse specifically for ad-hoc queries, and enable query acceleration on this warehouse.

3. You have implemented external tokenization for a sensitive data column in Snowflake using a UDF that calls an external API. After some time, you discover that the external tokenization service is experiencing intermittent outages, causing queries using the tokenized column to fail. What is the BEST approach to mitigate this issue and maintain data availability while minimizing the risk of exposing the raw data?

A) Replicate the tokenized table to another Snowflake region and switch to the replica during outages of the primary region. The tokenization service is guaranteed to be available in at least one region.
B) Implement a try-catch block within the UDF. In the catch block, return a pre-defined static token value (same value always) instead of attempting to call the external tokenization service. You can't return the raw value.
C) Implement a masking policy on the column that returns the raw data when the tokenization UDF is unavailable, detected by catching exceptions within the policy logic.
D) Implement a try-catch block within the UDF. In the catch block, return a pre-defined, non-sensitive default value instead of attempting to call the external tokenization service. You can't return the raw value.
E) Modify the tokenization UDF to cache tokenization mappings locally within the Snowflake environment. When the external service is unavailable, the UDF can use the cached values.

4. A data engineering team has created a Snowflake Listing to share their company's sales data'. They want to allow consumers to access the Listing programmatically. The consumers need to know when new versions of the Listing are available. What is the MOST efficient method for consumers to be notified about new Listing versions without continuously polling Snowflake?

A) The data consumer implements a custom webhook endpoint that the data provider calls whenever a new Listing version is published. The data provider will need to maintain a list of all consumers.
B) Snowflake automatically notifies consumers via an event notification service (e.g., AWS SNS, Azure Event Grid) integrated with the Data Marketplace when a new version is available.
C) The consumer sets up a scheduled task in Snowflake to periodically query the 'SHOW VERSIONS command for the Listing.
D) The data provider manually sends email notifications to each consumer whenever a new version is created.

5. A data engineer is using the Snowflake Spark connector to read a large table from Snowflake into a Spark DataFrame. The table contains a 'TIMESTAMP NTT column. After loading the data, the engineer observes that the values in the 'TIMESTAMP NTZ' column are not preserved accurately when retrieved from the DataFrame. What are the potential issues and what configurations can be adjusted in Snowflake to improve the result?

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

Solutions: