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

1. You are designing a data recovery strategy for a critical table 'CUSTOMER DATA' in your Snowflake environment. The data in this table is highly sensitive, and regulatory requirements mandate a retention period of at least 90 days for potential audits. You need to configure the Time Travel retention period to meet these requirements. What is the maximum supported Time Travel retention period, and how would you set it at the table level?

A) The maximum retention period is 365 days. You can set it using: ALTER TABLE CUSTOMER DATA SET DATA RETENTION TIME IN DAYS = 365;'
B) The maximum retention period is 7 days. You can set it using: 'ALTER TABLE CUSTOMER_DATA SET = 7;'
C) The maximum retention period depends on your Snowflake edition and can be set at the account level only.
D) The maximum retention period is 90 days. You can set it using: 'ALTER TABLE CUSTOMER_DATA SET = 90;'
E) The maximum retention period is 90 days for Enterprise Edition or higher. You can set it using: 'ALTER TABLE CUSTOMER DATA SET DATA RETENTION TIME IN DAYS = 90;'

2. You are a data engineer responsible for data governance in a Snowflake environment. Your company has implemented data classification using tags to identify sensitive data'. The compliance team has requested a report detailing all tables and columns that contain PII data, specifically including the tag name, tag value, the fully qualified name of the table, and the column name. You have the necessary privileges to access the Snowflake metadata views. Which of the following queries would provide the MOST comprehensive and accurate report, considering performance and ease of understanding?

A)

B)

C)

D)

E)

3. You are tasked with implementing a data governance strategy in Snowflake for a large data warehouse. Your objective is to classify sensitive data columns, such as customer phone numbers and email addresses, using tags. You want to define a flexible tagging system that allows different levels of sensitivity (e.g., 'Confidential', 'Restricted') to be applied to various columns. Furthermore, you need to ensure that any data replicated to different regions maintains these classifications. Which of the following statements accurately describe best practices for implementing and maintaining data classification using tags in Snowflake, especially in a multi-region setup? Choose TWO.

A) Create a scheduled task that automatically identifies sensitive data based on regular expressions and applies the appropriate tags. This automates the classification process.
B) Define tag schemas at the account level and replicate them to all regions. This ensures consistency of tag definitions across the entire organization.
C) When replicating data between regions, the tags are automatically replicated along with the data, provided that replication is configured using database replication or failover groups including the tagging schema.
D) Tags and tag values must be uniquely defined across all schemas to avoid conflicts and ensure accurate data classification; Snowflake enforces uniqueness implicitly.
E) Always grant the ACCOUNTADMIN role to users who need to apply tags. This simplifies the process and ensures they have all necessary privileges.

4. You are developing a Snowpark Python application that processes data from a large table. You want to optimize the performance by leveraging Snowpark's data skipping capabilities. The table 'CUSTOMER ORDERS is partitioned by 'ORDER DATE. Which of the following Snowpark operations will MOST effectively utilize data skipping during data transformation?

A) Applying a filter >= '2023-01-01') & (col('ORDER_DATE') <= '2023-03-31'))' after performing a complex join operation.
B) Applying a filter '2023-01-01') & '2023-03-31'))' before performing any join or aggregation operations.
C) Creating a new DataFrame with only the columns needed using 'ORDER_DATE', 'ORDER_AMOUNT')' before any filtering operations.
D) Executing 'df.collect()' to load the entire table into the client's memory before filtering.
E) Using the 'cache()' method on the DataFrame before filtering by 'ORDER DATE

5. A data engineering team is tasked with optimizing a complex query that joins three tables: 'ORDERS' , 'CUSTOMERS' , and 'PRODUCTS. The 'ORDERS' table contains millions of records and is frequently joined with 'CUSTOMERS' (containing customer demographics) and 'PRODUCTS' (containing product details). The initial query uses standard JOIN syntax, but performance is slow. The query retrieves order details along with customer and product information, filtering by a specific date range in the 'ORDERS' table and a customer segment in the 'CUSTOMERS table. Which optimization strategy would be MOST effective for significantly improving query performance?

A) Replace the standard JOINs with LATERAL FLATTEN operations.
B) Apply clustering keys to the 'ORDERS table based on the date column used in the WHERE clause and clustering keys to the 'CUSTOMERS' table on the customer segment column. Also create appropriate indexes.
C) Increase the virtual warehouse size to X-LARGE without analyzing the query profile.
D) Create materialized views that pre-join the 'ORDERS', 'CUSTOMERS, and 'PRODUCTS tables and filter based on common criteria.
E) Convert the entire dataset into a single VARIANT column and query using JSON path expressions.

Solutions: