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

1. You are designing a data sharing solution in Snowflake where a provider account shares a view with a consumer account. The view is based on a table that undergoes frequent DML operations (inserts, updates, deletes). The consumer account needs to see a consistent snapshot of the data, even during these DML operations. Which of the following strategies, or combination of strategies, would be MOST effective in ensuring data consistency from the consumer's perspective, and what considerations should be made?

A) Creating a materialized view in the provider account and sharing that materialized view. This adds compute costs to the provider but ensures a consistent snapshot for the consumer account. The materialized view needs to be refreshed periodically, based on the rate of DML changes.
B) Creating a stream on the base table in the provider account and building a view on top of the stream. This way, changes are only reflected when the stream is consumed, allowing for batch processing and controlled updates in the consumer account.
C) Using Snowflake's Time Travel feature by querying the view with a specific 'AT' or 'BEFORE' clause in the consumer account. The provider account needs to inform the consumer account of a specific timestamp that guarantees consistency, adding administrative overhead.
D) A and B
E) Creating a standard view in the provider account and relying on Snowflake's inherent transactional consistency. The consumer account will always see a consistent snapshot of the data as it existed at the beginning of their query execution. No additional configurations are necessary.

2. You are implementing a data pipeline to load data from AWS S3 into Snowflake. The source data consists of CSV files with a header row. Some of the CSV files have inconsistent data types in a specific column (e.g., sometimes an integer, sometimes a string). You want to use the 'COPY' command to load the data and handle these data type inconsistencies gracefully. Which of the following 'COPY' command options, used in conjunction, would BEST address this issue and avoid load failures? Assume the file format is already defined to specify CSV type, header skip, and field delimiter.

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

3. You have a table 'SALES DATA' in your production environment. You want to create a development environment using cloning, but only want to include data up to a specific point in time to minimize storage costs and potential exposure of recent, sensitive data'. You know there were significant changes to the 'SALES DATA' table structure on '2024-01-15'. Your goal is to create a clone that only includes the structure as of '2024-01-14'. Which Snowflake command is MOST appropriate for this scenario?

A)

B)

C)

D)

E)

4. You have a Snowflake table named 'CUSTOMER DATA that contains sensitive Personally Identifiable Information (PII). You need to grant a data analyst access to a subset of the data while masking specific columns containing PII. Which of the following Snowflake features, when used in combination, provides the MOST secure and efficient solution?

A) Grant the analyst the 'SELECT privilege on the 'CUSTOMER_DATA' table and implement row-level security based on the analyst's role.
B) Create a stored procedure that retrieves the data with masking applied and grant the analyst EXECUTE privilege on the stored procedure.
C) Create a secure view on 'CUSTOMER DATA' with column masking policies and grant SELECT privileges on the secure view.
D) Create a view on 'CUSTOMER_DATA' with column masking policies and grant SELECT privileges on the view.
E) Create a new table containing only the non-sensitive columns and grant SELECT privileges on this table.

5. 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 D
B) Option A
C) Option C
D) Option B
E) Option E

Solutions: