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

1. You are designing a data pipeline to load JSON data from an AWS S3 bucket into a Snowflake table. The JSON files have varying schemas, and you want to use schema evolution to handle changes. You are using a named external stage with 'AUTO REFRESH = TRUE. You notice that some files are not being ingested, and the COPY HISTORY shows 'Invalid JSON' errors. Which of the following actions would BEST address this issue while minimizing manual intervention?

A) Re-create the stage with the 'AUTO REFRESH = FALSE parameter and manually refresh the stage metadata after each file is uploaded. This gives more control over which files are processed.
B) Modify the COPY INTO statement to include 'ON ERROR = SKIP FILE' to ignore files with invalid JSON and continue loading other files. This ensures the pipeline continues without interruption.
C) Adjust the file format definition associated with the stage to be more permissive, allowing for variations in the JSON structure. For example, use 'STRIP OUTER ARRAY = TRUE and configure error handling within the file format.
D) Create a separate landing stage for potentially invalid JSON files and use a task to validate the files before moving them to the main stage for ingestion into Snowflake.
E) Implement a pre-processing step using a Snowpark Python UDF to cleanse the JSON files in the stage before the COPY INTO command is executed. This UDF should handle schema variations and correct any invalid JSON structures.

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

3. You are tasked with creating a development environment from a production database named 'PROD DB'. This database contains sensitive data, and you need to mask the data in the development environment. You decide to use cloning and a transformation function during the cloning process. What is the MOST efficient approach to clone 'PROD DB' into a development database 'DEV DB' and mask sensitive data in the process?

A) Create a clone of 'PROD named 'DEV DB'. Create a warehouse for running masking policies. Then apply masking policies to the tables in 'DEV DB' Cloning masks the underlying data directly.
B) Create a clone of 'PROD named 'DEV DB'. Create stored procedures on 'DEV DB' which apply masking at the query level. Cloning databases does not preserve masking policies from the Source database
C) Create a clone of 'PROD named 'DEV DB', then create views on 'DEV DB' using masking policies. Cloning the Views from 'PROD will automatically copy the masking policies.
D) Clone 'PROD to ' DEV DB'. Export the data from 'DEV DB', transform it using a scripting language (e.g., Python), and then load the transformed data back into replacing the original data.
E) Create a clone of 'PROD named 'DEV DB'. Define masking policies on the columns in 'PROD DB' before cloning. These policies will be automatically applied to the cloned tables in "DEV_DB' ensuring all data is masked during query time in the DEV environment.

4. You are working with a Snowflake table 'customer_data' which contains customer information stored in a VARIANT column named raw_info'. The 'raw_info' JSON structure includes nested addresses, and preferences. Your task is to extract the city from the first address in the 'addresses' array, and the customer's preferred communication method from the 'preferences' object. Some customers might not have addresses or preferences defined. Select the two SQL snippets that correctly and efficiently extract this data, handling missing fields gracefully and providing appropriate type casting. Address array is in the format 'addresses: [ { 'city': '...', 'state': ' '},

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

5. You are tasked with designing a data sharing solution where data from multiple tables residing in different databases within the same Snowflake account needs to be combined into a single view that is then shared with a consumer account. The view must also implement row-level security based on the consumer's role. Which of the following options represent valid approaches for implementing this solution? Select all that apply.

A) Create a secure view that joins tables from different databases using fully qualified names (e.g., 'DATABASEI .SCHEMAI . TABLET) and implement row-level security using a masking policy based on the CURRENT_ROLE() function.
B) Create a standard view with a stored procedure to handle the joins across databases and use EXECUTE AS OWNER to avoid permission issues. This standard view should be shared.
C) Create a secure view that joins tables from different databases and implement row-level security using a row access policy based on the CURRENT ROLE() function. Masking policy cannot provide role based access control so will not work.
D) Create a standard view that joins tables from different databases using aliases and implement row-level security using a UDF that checks the consumer's role and filters the data accordingly.
E) Create a view for each table and then build a final view using 'UNION ALL' to combine data from all the views and implement row-level security with a role based row access policy. Standard views should not be used in data sharing.

Solutions: