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Snowflake DEA-C02 Exam Syllabus Topics:

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

1. You have implemented a masking policy on the 'EMAIL' column of a 'USERS' table. The policy masks the email address for all users except those with the 'SUPPORT' role. You now need to grant the 'SELECT' privilege on this table to a new role, 'ANALYST. You want to ensure that the masking policy continues to work as expected for the 'ANALYST' role. Which of the following SQL statements should you execute?

A)

B)

C)

D)

E)

2. You have a table 'EMPLOYEE with a column 'SALARY'. You need to mask the 'SALARY column for all roles EXCEPT for the PAYROLL ADMIN' role. You create the following masking policy:

You then apply this masking policy to the 'SALARY column: ALTER TABLE EMPLOYEE MODIFY COLUMN SALARY SET MASKING POLICY salary_mask; However, users who are not granted the 'PAYROLL _ADMIN' role are still seeing the actual salary values. What is the MOST likely reason for this and how can you resolve it?

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

3. A financial institution is using Snowflake to store transaction data for millions of customers. The data is stored in a table named 'TRANSACTIONS with columns such as 'TRANSACTION ID, 'CUSTOMER ID', 'TRANSACTION DATE, 'TRANSACTION_AMOUNT, and 'MERCHANT CATEGORY'. Analysts are running complex analytical queries that often involve filtering transactions by 'TRANSACTION_DATE, 'MERCHANT CATEGORY' , and 'TRANSACTION_AMOUNT ranges. These queries are experiencing performance bottlenecks. The data team wants to leverage query acceleration service to improve performance without significantly altering the existing query patterns. Which of the following actions or combination of actions would be MOST beneficial, considering the constraints and the nature of the queries? (Select TWO)

A) Enable Automatic Clustering on the 'TRANSACTIONS' table, ordering the keys as 'TRANSACTION_DATE, 'MERCHANT_CATEGORY', 'CUSTOMER_ID. Then, enable query acceleration on the virtual warehouse.
B) Enable Search Optimization Service for the 'TRANSACTIONS' table, specifically targeting the 'MERCHANT_CATEGORY column. Enable query acceleration on the virtual warehouse.
C) Create separate virtual warehouses dedicated to reporting queries and ad-hoc queries respectively. Enable query acceleration only for the warehouse running reporting queries.
D) Create materialized views pre-aggregating the transaction data by 'MERCHANT_CATEGORY and 'TRANSACTION_DATE, and enable query acceleration on the virtual warehouse.
E) Increase the size of the virtual warehouse used for running the queries and enable query acceleration on the warehouse without further modifications.

4. Which of the following statements are true regarding using Dynamic Data Masking and Column-Level Security in Snowflake? (Select all that apply)

A) Column-Level Security via views provides more fine-grained control over data access compared to Dynamic Data Masking.
B) Dynamic Data Masking is applied at query runtime, while Column-Level Security through views or roles is applied when the object is created.
C) Using both Dynamic Data Masking and Column-Level Security (e.g. views) on the same column is redundant and will result in an error.
D) Dynamic Data Masking can be used to apply different masking rules based on the user's role, IP address, or other contextual factors.
E) Dynamic Data Masking policies can reference external tables directly without requiring special grants.

5. 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) 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.
C) Use Snowpipe with auto-ingest, create a single VARIANT column in your target table, and rely solely on Snowflake's automatic schema detection.
D) 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.
E) 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.

Solutions: