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

1. You are designing a CI/CD pipeline for your Snowflake data transformations. One stage involves testing a new stored procedure that modifies several tables in your data warehouse. To ensure data integrity and proper rollback capabilities during testing in your development environment, you want to use a combination of cloning and Tme Travel. Select the option that represents the most robust strategy for testing with the ability to revert to the original state in case of failures. Choose all that apply.

A) Create a new database by cloning the existing database before the transformations. Use the cloned database for all development and testing.
B) Immediately after executing the stored procedure, execute 'UNDROP TABLE' on all the affected tables. If there were errors, it would restore data from the point of the procedure execution.
C) Before executing the stored procedure in development, create clones of all affected tables and store the clone names in a configuration file for rollback if needed.
D) Create a clone of the schema containing all the tables affected by the stored procedure before executing the procedure in the development environment. This will create point in time backup to rollback at schema level.
E) Leverage Snowflake's Time Travel feature to create a named snapshot of each affected table immediately before running the stored procedure. Use the 'AT(TIMESTAMP ...)' or 'BEFORE(STATEMENT => ...)' syntax within your testing framework, for easy reversion.

2. You have a Snowflake table 'orders_raw' with a VARIANT column named 'order detailS that contains an array of order items represented as JSON objects. Each object has 'item id', 'quantity' , and 'price'. You need to calculate the total revenue for each order. Which SQL statement efficiently flattens the array and calculates the total revenue using LATERAL FLATTEN and appropriate casting?

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

3. You are building a data pipeline that utilizes a Snowflake stage to store intermediate results. You need to ensure data security and compliance. Which of the following methods offer the BEST approach for securing data stored in a Snowflake stage?

A) Encrypt the data client-side before uploading it to the stage and decrypt it after loading it into Snowflake. This provides an additional layer of security.
B) Utilize network policies to restrict access to the stage based on IP address or network identifier. Only authorized IP addresses should be able to interact with the stage.
C) Configure the stage to use temporary storage, which automatically deletes the data after a specified retention period.
D) Apply masking policies to the columns in the tables that are loaded from the stage. This ensures sensitive data is masked before it reaches the target tables.
E) Encrypt the data at rest on the storage layer using Snowflake's built-in encryption features. Snowflake automatically encrypts all data at rest.

4. You are building a data pipeline to ingest JSON data from an external API using the Snowflake SQL API. The API returns nested JSON structures, and you need to extract specific fields and load them into a Snowflake table with a flattened schem a. You also need to handle potential schema variations and missing fields in the JSON data. Which approach provides the MOST robust and flexible solution for this scenario, maximizing data quality and minimizing manual intervention?

A) Load the raw JSON data into a VARIANT column in Snowflake. Create a series of views on top of the VARIANT column to extract the required fields and handle schema variations using 'TRY TO ' functions.
B) Utilize Snowflake's schema detection feature during the COPY INTO process. This will automatically infer the schema from the JSON data and create the table accordingly.
C) Use the 'JSON TABLE function in a Snowflake SQL query executed via the SQLAPI to flatten the JSON data and extract the required fields. Handle missing fields by using 'DEFAULT values in the table schema.
D) Parse the JSON data in your client application (e.g., Python) using a library like 'json' or , transform the data into a tabular format, and then use the Snowflake Connector for Python to load the data into Snowflake.
E) Use a stored procedure with dynamic SQL to parse the JSON, create new tables based on the current schema, and load data. Maintain metadata on table versions.

5. 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) Enable the 'snowflake.ingest.stage' property and set it to a Snowflake internal stage.
B) Use the 'transforms' configuration with the 'org.apache.kafka.connect.transforms.ExtractField$Value' transformation to extract specific fields.
C) Set the 'value.converter.schemas.enable' property to 'true'.
D) 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.
E) Apply the 'org.apache.kafka.connect.transforms.Flatten' transformation to the 'transforms' configuration.

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