About Snowflake SnowPro Advanced: Data Engineer (DEA-C02) exam torrent

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

1. A financial services company stores sensitive customer data, including credit card numbers, in a Snowflake table called 'CUSTOMER DATA. You need to implement dynamic data masking on the 'CREDIT CARD NUMBER column. You want to ensure that only users with the FINANCE ADMIN' role can view the unmasked credit card numbers. All other users should see a masked version of the data'. Which of the following set of commands is the MOST efficient and secure way to achieve this?

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

2. You are using Snowpark Python to transform a DataFrame 'df_orderS containing order data'. You need to filter the DataFrame to include only orders with a total amount greater than $1000 and placed within the last 30 days. Assume the DataFrame has columns 'order_id', 'order_date' (timestamp), and 'total_amount' (numeric). Which of the following code snippets is the MOST efficient and correct way to achieve this filtering using Snowpark?

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

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

4. You are tasked with implementing row-level filtering on a 'customers' table in Snowflake. You need to restrict access to customer data based on the user's region. The 'customers' table has a 'region' column, and you have a mapping table 'user regions' that associates users with the regions they are allowed to access. The 'user_regions' table has columns 'username' and 'region'. Which of the following SQL statements correctly creates and applies a row access policy to achieve this, minimizing complexity and maximizing performance? Select all that apply:

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

5. You are tasked with creating a Snowpark Java stored procedure to calculate a complex, custom rolling average for a time series dataset. This rolling average requires access to external libraries for statistical calculations. Which of the following steps are necessary to successfully deploy and execute this stored procedure?

A) Grant the necessary privileges on the stage and the database to the role executing the stored procedure.
B) Create a stored procedure in Snowflake, specifying the fully qualified path to the JAR file in the stage, the handler class, and the return type.
C) Package the Java code and all necessary external libraries into a single JAR file.
D) Upload the JAR file to a Snowflake stage.
E) All of the above.

Solutions: