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Snowflake DEA-C02 Exam Syllabus Topics:

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

1. A financial institution needs to tokenize sensitive customer data (credit card numbers) stored in a Snowflake table named 'CUSTOMER_DATA before it's consumed by a downstream reporting application. The institution uses an external tokenization service accessible via a REST API. Which of the following approaches is the MOST secure and scalable way to implement tokenization during data loading, minimizing exposure of the raw credit card data within Snowflake?

A) Load the raw data directly into the 'CUSTOMER DATA' table. Create a masking policy that utilizes a UDF that calls the external tokenization API directly to tokenize the credit card number values on read.
B) Load the raw data into a staging table, then create a Snowflake Task that executes a stored procedure. The stored procedure calls the external tokenization API using 'SYSTEM$EXTERNAL_FUNCTION_REQUEST' for each row and updates the original table with the tokenized values.
C) Use a Snowflake UDF (User-Defined Function) written in Java that calls the external tokenization API directly. Create a masking policy that utilizes the UDF and applies it to the credit card number column.
D) Use Snowflake's Data Sharing feature to securely share the raw data with the downstream application, instructing them to perform the tokenization within their own environment.
E) Utilize Snowflake's Snowpipe to ingest the data directly. Inside a COPY INTO statement, use an external function to call the tokenization service during the ingestion process to tokenize the data before it's loaded into the target table.

2. A data engineering team is using a Snowflake stream to capture changes made to a source table named 'orders'. They want to only capture 'INSERT and 'UPDATE operations but exclude 'DELETE operations from being captured in the stream. Which of the following configurations will achieve this requirement? Assume the stream has already been created and is named 'orders_stream'.

A) Create a view on top of the base table that filters out deleted rows, and then create a stream on the view.
B) Alter the stream using the 'HIDE_DELETES parameter: 'ALTER STREAM orders_stream SET HIDE_DELETES = TRUE;'
C) Use task and stream combination. In the task, create view using 'select from orders where metadata$isDelete = false' and create stream on that view.
D) It's impossible to configure a stream to exclude specific DML operations. All changes are always tracked.
E) Create a Snowflake task that periodically truncates the stream's metadata table, removing DELETE records.

3. Your company is implementing data governance policies in Snowflake and wants to automatically classify data to track Personally Identifiable Information (PII). You have defined a classification policy with a tag 'PII' and associated tag values like 'Email', 'CreditCard', and 'SSN'. You want to monitor the usage of PII data'. Which of the following approaches is the MOST efficient way to track access and modifications to columns tagged with the 'PII' tag and any of its tag values?

A) Enable Snowflake's data governance features, including object tagging and data classification. Then, leverage the ACCESS HISTORY view, filtering based on POLICY _ TAGS. This will show all accesses to data tagged as PII.
B) Create a scheduled task to query the INFORMATION SCHEMCOLUMNS view and filter based on the TAG DATABASE, TAG SCHEMA, and TAG NAME columns to identify PII columns, then query the QUERY_HISTORY view and filter based on the identified column names.
C) Implement a custom UDF that intercepts all queries. Inside the UDF, check if any of the accessed tables or columns are tagged with 'PII', and log those queries into a separate logging table for monitoring.
D) Use Snowflake's native data governance capabilities in conjunction with Snowflake Horizon to actively monitor data quality and access patterns, specifically focusing on PII-tagged columns.
E) Create a masking policy that redacts all PII data. Monitor access attempts via the ACCESS HISTORY view. Although it hinders data usability, this approach guarantees no PII data leakage and allows easy monitoring.

4. You are implementing a data share between two Snowflake accounts. The provider account wants to grant the consumer account access to a function that returns anonymized customer data based on a complex algorithm. The provider wants to ensure that the consumer cannot see the underlying implementation details of the anonymization algorithm. Which of the following approaches can achieve this goal? (Select TWO)

A) Create a view that calls the secure UDF and share that view with the consumer account.
B) Create a secure UDF in the provider account and grant usage on the secure UDF to the share. Share the share with the consumer account.
C) Share the underlying table and provide the consumer account with the anonymization algorithm separately.
D) Create a standard UDF in the provider account and grant usage on the UDF to the share. Share the share with the consumer account.
E) Create an external function in the provider account and grant usage to the share. Share the share with the consumer account.

5. 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) 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.
B) 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.
C) 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
D) Utilize Snowflake's native Data Governance features, such as data masking and row-level security, to enforce data quality rules.
E) 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.

Solutions: