You are tasked with implementing a data loading process for a table 'CUSTOMER DATA' in Snowflake. The source data is in Parquet format on Azure Blob Storage and contains personally identifiable information (PII). You must ensure that the data is loaded securely, masked during the loading process, and that only authorized users can access the unmasked data after the load. Assume you have already created a stage pointing to the Azure Blob Storage. Which of the following steps should you take to achieve this?

• A. Use a 'COPY command with the 'TRANSFORM' clause and JavaScript UDFs to mask the PII data during the load process. Implement masking policies on the 'CUSTOMER DATA' table to restrict access to the unmasked data.
• B. Use a 'COPY command with the 'ENCRYPTION = (TYPE = 'AZURE CSE', KEY = option to encrypt the data during load. Implement role-based access control to restrict access to the table.
• C. Load the data directly into a 'VARIANT column. Use a SQL transformation with 'FLATTEN' and masking policies on the extracted columns.
• D. Use a 'COPY command with 'ON ERROR = SKIP FILE'. Use a Task to monitor load failures and trigger alerts.
• E. Load the data without masking. Implement dynamic data masking policies on the table's PII columns using Snowflake's Enterprise edition features. Use a 'COPY' command with ERROR = CONTINUE

You have a Snowflake table, 'raw_data', which contains a column 'data url' storing URLs pointing to CSV files with varying schemas. Each CSV file represents sales data, but the column names and data types can differ. You need to create a process to automatically discover the schema of each CSV file, load the data into Snowflake, and standardize the column names to 'order id', 'product id', 'quantity', and 'price'. Which of the following approaches best addresses this requirement, considering scalability and minimal manual intervention?

• A. Create a Snowflake external table that points to the external stage. Define a single file format to be used by external table. Define a pipe that uses 'COPY INTO' to ingest data into external table from the files found at the file URLs.
• B. Create a Python-based external function that downloads the CSV file from the URL using a library like 'pandas', infers the schema using 'pandas.read_csv' , maps the discovered column names to the standardized names, and returns the data as a JSON string. Then, create a Snowflake table with a VARIANT column, call the external function for each URL, and load the returned JSON data into the table. Create a view on top of it.
• C. Leverage a combination of Snowflake Scripting and External functions: create external function that infer the schema of the CSV, create temporary table based on identified schema, fetch the CSV data using SYSTEM$URL GET using snowflake scripting, copy the data into the temporary table, tranform the data into required structure, ingest into target table and finally drop the temporary table
• D. Create a stored procedure that iterates through each URL in 'raw_data' , downloads the CSV file using 'SYSTEM$URL_GET , parses the CSV header to determine the column names, manually maps the discovered column names to the standardized names, creates a temporary table with the discovered schema, loads the data into the temporary table, transforms the data to use the standardized column names, and then inserts the transformed data into a final target table. Drop the temporary table after successful insertion.
• E. Use Snowpipe with auto-ingest to continuously load the CSV files into a VARIANT column in a staging table. Create a series of views on top of the staging table, each view attempting to extract data based on different potential schema variations. Union all the views together to create a single consolidated view.

A data engineer is tasked with optimizing query performance on a Snowflake table named 'SALES DATA, which currently has no clustering key defined. The table contains 'SALE (unique identifier), 'SALE DATE, 'PRODUCT CATEGORY, and 'SALE AMOUNT. The business analysts frequently run queries filtering on 'SALE DATE and then aggregating by 'PRODUCT CATEGORY'. Choosing the right clustering keys for the SALES DATA table is crucial for minimizing disk 1/0 and enhancing query speed. Which of the following clustering key strategies would be MOST effective for the specified query patterns, considering both performance and the potential impact on data loading and DML operations?

• A. Clustering on 'SALE DATE followed by 'PRODUCT CATEGORY.
• B. Clustering only on PRODUCT_CATEGORY.
• C. Creating separate tables for each 'PRODUCT CATEGORY.
• D. Clustering on followed by 'SALE_DATE'.
• E. Clustering only on 'SALE DATE

You have a Snowpark Python application that performs complex calculations on a large dataset stored in Snowflake. The application is currently running slowly. After profiling, you've identified that the UDFs you're using are the bottleneck. These UDFs perform custom data transformations using a third-party Python library which has a significant initialization overhead. Which of the following strategies would be MOST effective to optimize performance, minimizing both runtime and resource consumption?

• A. Implement UDF caching at the Snowflake level by setting the 'VOLATILE property to 'IMMUTABLE or 'STABLE' (if appropriate), and leverage the Snowflake query result cache.
• B. Convert the Snowpark Python application to a Snowpark Java application as Java generally offers better performance than Python.
• C. Rewrite the UDFs in SQL using Snowflake's built-in functions to avoid the overhead of Python execution. If the library's functions aren't available, consider creating external functions using a cloud provider's serverless compute service.
• D. Use Snowpark's 'pandas_udf with 'vectorized=True' and pre-initialize the third-party library within the UDF's execution context using a closure or similar technique for reuse across batches.
• E. Increase the size of the Snowflake warehouse being used for the Snowpark workload. This will provide more CPU and memory resources.

You are tasked with implementing column-level security on the 'EMPLOYEE table to restrict access to the 'SALARY column. Only users with the 'HR ROLE' should be able to view the actual salary. All other users should see NULL. You create a masking policy as follows:

What additional steps are necessary to enforce this policy?

• A. Grant the SELECT privilege on the EMPLOYEE table to the HR ROLE.
• B. Apply the masking policy to the EMPLOYEE table: ALTER TABLE EMPLOYEE SET MASKING POLICY salary_mask;
• C. Apply the masking policy to the SALARY column: ALTER TABLE EMPLOYEE MODIFY COLUMN SALARY SET MASKING POLICY salary_mask;
• D. Grant OWNERSHIP on the masking policy to the HR ROL
• E. Grant the APPLY MASKING POLICY privilege to the HR_ROLE.