You are designing a data ingestion process that involves loading data from an external stage. The data is partitioned into multiple files based on date. The stage is configured to point to the root directory of the partitioned dat a. You want to efficiently load only the data for a specific date (e.g., '2023-01-15') using the 'COPY' command. Assume your stage name is 'my _ stage' , your table is 'my_table', your date column is named 'event_date', and the files in the stage are named in the format 'data YYYY-MM-DD.csv'. Which of the following options allows you to selectively load the data for the specific date? (Select ALL that apply)

• A. Option C
• B. Option B
• C. Option D
• D. Option E
• E. Option A

You are tasked with sharing a subset of a customer table (CUSTOMER DATA') residing in your organization's Snowflake account with a partner organization. You need to mask personally identifiable information (PII) while providing near real-time updates. You decide to use a secure view. Which of the following SQL statements is the MOST efficient and secure way to accomplish this, assuming the partner only needs 'customer id', 'masked_email', 'city', and 'state'? The email should be masked using SHA256.

• A. Option C
• B. Option B
• C. Option D
• D. Option E
• E. Option A

A data provider wants to share a large dataset (several TB) with multiple consumers. The dataset is updated daily. The provider wants to minimize the cost associated with data sharing and ensure that consumers receive consistent data'. Which of the following strategies would be the MOST cost-effective and maintainable?

• A. Create a data share and grant access to all consumers directly on the base tables.
• B. Create a data share with views that point to the base tables. Use time travel to allow consumers to query data from a specific point in time before the daily update.
• C. Create a data share with views that point to the base tables, and clone the base tables daily into a separate 'staging' database before sharing.
• D. Create a data share and create a separate database for each consumer, cloning the data into each consumer's database daily.
• E. Create a data share containing external tables pointing to data stored in cloud storage (e.g., AWS S3), updated daily using a pipeline.

You are tasked with designing a data pipeline that ingests JSON data from an external stage (AWS S3). The JSON files contain records for various product types, each having a different set of attributes. Some product types might have attributes that are not present in other types. You want to create a single Snowflake table that can accommodate all product types without defining a rigid schema upfront and also be queryable efficiently. Which of the following approaches, combining external tables, schema evolution and querying, would be MOST effective? (Choose two)

• A. Create a single external table with a VARIANT column to store the entire JSON record for each product. Use LATERAL FLATTEN to extract specific attributes during querying.
• B. Create a single external table with a VARIANT column and use the 'VALIDATE function to identify and handle schema inconsistencies during data loading.
• C. Create a separate external table for each product type, defining the schema for each table based on the attributes present in the corresponding JSON files.
• D. Create a stored procedure that dynamically infers the schema from the JSON files and creates a new Snowflake table based on the inferred schema.
• E. Load all the data into a raw Snowflake internal table. Use dynamic SQL to infer distinct product types and create views on top of the raw table for each product type.

You are developing a Snowpark Python stored procedure that performs complex data transformations on a large dataset stored in a Snowflake table named 'RAW SALES'. The procedure needs to efficiently handle data skew and leverage Snowflake's distributed processing capabilities. You have the following code snippet:

Which of the following strategies would be MOST effective to optimize the performance of this Snowpark stored procedure, specifically addressing potential data skew in the 'product id' column, assuming 'product_id' is known to cause uneven data distribution across Snowflake's micro-partitions?

• A. Utilize Snowflake's automatic clustering on the 'TRANSFORMED_SALES table by specifying 'CLUSTER BY when creating or altering the table to ensure future data is efficiently accessed.
• B. Implement a custom partitioning strategy using before the transformation logic to redistribute data evenly across the cluster.
• C. Increase the warehouse size significantly to compensate for the data skew and improve overall processing speed without modifying the partitioning strategy.
• D. Use the 'pandas' API within the Snowpark stored procedure to perform the transformation, as 'pandas' automatically optimizes for data skew.
• E. Combine salting with repartitioning by adding a random number to the 'product_id' before repartitioning, then removing the salt after the transformation to break up the skew. Then, enable automatic clustering on the 'TRANSFORMED SALES' table.