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Snowflake DEA-C02 Exam Syllabus Topics:

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

1. You have configured a Kafka Connector to load JSON data into a Snowflake table named 'ORDERS. The JSON data contains nested structures. However, Snowflake is only receiving the top- level fields, and the nested fields are being ignored. Which configuration option within the Kafka Connector needs to be adjusted to correctly flatten and load the nested JSON data into Snowflake?

A) Set the 'value.converter.schemas.enable' property to 'true'.
B) Configure the 'snowflake.data.field.name' property to specify the column in the Snowflake table where the entire JSON should be loaded as a VARIANT.
C) Use the 'transforms' configuration with the 'org.apache.kafka.connect.transforms.ExtractField$Value' transformation to extract specific fields.
D) Apply the 'org.apache.kafka.connect.transforms.Flatten' transformation to the 'transforms' configuration.
E) Enable the 'snowflake.ingest.stage' property and set it to a Snowflake internal stage.

2. You have a 'SALES table and a 'PRODUCTS table. The 'SALES table contains daily sales transactions, including 'SALE DATE , 'PRODUCT ID', and 'QUANTITY. The 'PRODUCTS table contains 'PRODUCT and 'CATEGORY. You need to create a materialized view to track the total quantity sold per category daily, optimized for fast query performance. You anticipate frequent updates to the 'SALES table but infrequent changes to the 'PRODUCTS table. Which of the following strategies would provide the MOST efficient materialized view implementation, considering both data freshness and query performance?

A) Create a standard materialized view that joins 'SALES' and 'PRODUCTS' , grouping by 'SALE_DATE and 'CATEGORY, and defining a clustering key on 'SALE DATE' and 'CATEGORY.
B) Create a standard materialized view that joins 'SALES' and 'PRODUCTS' , grouping by 'SALE_DATE and 'CATEGORY, and defining a clustering key on 'SALE DATE.
C) Create two materialized views: one for daily sales by product and another joining the first with 'PRODUCTS' to aggregate by category. Cluster the first view by 'SALE DATE' and the second by 'CATEGORY'.
D) Create a standard materialized view that joins 'SALES' and 'PRODUCTS' , grouping by 'SALE_DATE and 'CATEGORY, and defining a clustering key on ' CATEGORY.
E) Create a standard materialized view that joins 'SALES' and 'PRODUCTS' , grouping by 'SALE DATE and 'CATEGORY without any specific clustering key.

3. You are loading JSON data into a Snowflake table with a 'VARIANT' column. The JSON data contains nested arrays with varying depths. You need to extract specific values from the nested arrays and load them into separate columns in your Snowflake table. Which approach would provide the BEST performance and flexibility?

A) Use a 'COPY' command with a 'TRANSFORM' clause that uses JavaScript UDFs to parse the JSON and extract the values during the load process. Load the extracted values directly into the target columns.
B) Use a stored procedure to parse the JSON data and insert values into the table row by row.
C) Create a view with nested 'FLATTEN' functions to extract the values from the 'VARIANT column. The view serves as the source for further transformations.
D) Load the entire JSON into a 'VARIANT column and then use SQL with nested 'FLATTEN' functions to extract the desired values during query time.
E) Use Snowpipe with auto-ingest, loading directly into the table with the 'VARIANT column. Define data quality checks with pre-load data transformation.

4. A financial services company is implementing Snowflake. They have a table 'CUSTOMER DATA' containing sensitive information like 'CREDIT CARD NUMBER, 'SSN', and 'ADDRESS'. They need to ensure that: 1) Analysts can only see the last four digits of the 'CREDIT CARD NUMBER. 2) Data scientists require full access to the 'ADDRESS' but should not see the 'SSN'. 3) A dedicated compliance role should be able to view all data in its original format for auditing purposes. Which of the following is the MOST efficient and secure approach to implement this using Snowflake's data masking and RBAC?

A) Create masking policies on the 'SSN' , and 'ADDRESS' columns. Use conditional masking expressions based on the CURRENT ROLE() function to determine what data to show to each role (analysts, data scientists, compliance).
B) Replicate the CUSTOMER_DATA table three times, once for each user group (Analysts, Data Scientist and Compliance). Mask sensitive information by altering the data with the respective masking function.
C) Use data encryption for the entire 'CUSTOMER_DATA table and provide decryption keys to specific roles based on their access requirements. Provide the compliance role with the master key.
D) Create dynamic data masking policies on each sensitive column in the 'CUSTOMER_DATA table, associating these policies with specific roles using Snowflake's tag-based masking. Grant roles only the privileges needed to select the columns based on their requirements.
E) Create separate views for analysts and data scientists, applying masking policies within the views, and grant access to these views based on their respective roles. Additionally, grant the compliance role direct access to the base table.

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) 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.
B) Package the Java code and all necessary external libraries into a single JAR file.
C) All of the above.
D) Upload the JAR file to a Snowflake stage.
E) Grant the necessary privileges on the stage and the database to the role executing the stored procedure.

Solutions: