You are implementing a data pipeline in Snowpark that reads data from an external stage (e.g., AWS S3) and performs complex transformations, including joins with large Snowflake tables. You notice that the pipeline's performance is significantly slower than expected, despite having sufficient warehouse resources. Which of the following actions would MOST likely improve the performance of the Snowpark data pipeline?

• A. Reduce the number of partitions in the DataFrame representing the data from the external stage using 'df.repartition(l )'.
• B. Persist the DataFrame representing the data from the external stage using 'df.cache()' before performing the joins.
• C. Increase the warehouse size to the largest available option (e.g., X-Large or larger).
• D. Optimize the SQL joins within the Snowpark DataFrame operations by using broadcast joins when appropriate and ensuring correct join key data types.
• E. Ensure that the external stage is properly configured with appropriate data formats (e.g., Parquet) and partitioning schemes that align with the join keys.

A company is using Snowflake's web app interface to manage its data'. A data engineer needs to create a new table, load data into it from a CSV file stored in an internal stage, and then grant SELECT privileges on the table to a specific role using the web app. Which sequence of actions within the Snowflake web app represents the most efficient and secure way to accomplish this task?

• A. 1. Use the SQL worksheet to execute CREATE TABLE statement. 2. Use the Data Load Data wizard to load the CSV file. 3. Use the SQL worksheet to execute GRANT SELECT ON TABLE statement.
• B. 1. Use the SQL worksheet to execute CREATE TABLE statement. 2. Use the Database -> Tables interface, select the table, and use the 'Load Data' option to load the CSV file. 3. Use the Database -> Tables interface, select the table, and use the 'Privileges' tab to grant SELECT privilege to the role.
• C. 1. Use the Database Tables interface to create the new table using the table editor. 2. Upload the CSV file directly to the table using the 'Load Data' option. 3. Use the SQL worksheet to execute GRANT SELECT ON TABLE statement.
• D. 1. Use the Database Tables interface to create the new table using the table editor. 2. Use the Data Load Data wizard to load the CSV file. 3. Use the SQL worksheet to execute GRANT SELECT ON TABLE statement.
• E. 1. Use the Database Tables interface to create the new table using the table editor. 2. Use the Data Load Data wizard to load the CSV file. 3. Use the Database -> Tables interface, select the table, and use the 'Privileges' tab to grant SELECT privilege to the role.

You have a data pipeline that aggregates web server logs hourly. The pipeline loads data into a Snowflake table 'WEB LOGS' which is partitioned by 'event_time'. You notice that queries against this table are slow, especially those that filter on specific time ranges. Analyze the following Snowflake table definition and query pattern and select the options to diagnose and fix the performance issue: Table Definition:

• A. Create a materialized view that pre-aggregates the 'status_code' by hour to speed up the aggregation query.
• B. Increase the warehouse size to improve query performance.
• C. The table is already partitioned by 'event_time' , so there is no need for further optimization.
• D. Add a search optimization strategy to the table on the 'event_time' column.
• E. Change the table to use clustering on 'event_time' instead of partitioning to improve query performance for range filters.

You have a table named 'TRANSACTIONS with the following definition: CREATE TABLE TRANSACTIONS ( TRANSACTION ID NUMBER, TRANSACTION DATE DATE, CUSTOMER_ID NUMBER, AMOUNT PRODUCT_CATEGORY VARCHAR(50) Users frequently query this table using filters on both 'TRANSACTION_DATE and 'PRODUCT CATEGORY. You want to optimize query performance. What is the MOST effective approach?

• A. Create a materialized view joining 'TRANSACTIONS' with a dimension table containing product category information.
• B. Create separate indexes on 'TRANSACTION DATE' and 'PRODUCT CATEGORY.
• C. Cluster the table on ' TRANSACTION_DATE and then create a materialized view filtered by PRODUCT_CATEGORY&.
• D. Partition the table by 'TRANSACTION DATE
• E. Cluster the table using a composite key of '(TRANSACTION_DATE, PRODUCT CATEGORY)'.

Consider the following Python Snowpark stored procedure designed to enrich customer data:

What are potential drawbacks or limitations of this approach, and what improvements can be made to enhance its scalability and maintainability? (Select all that apply)

• A. Hardcoding the SQL query within the stored procedure makes it inflexible. Using Snowpark DataFrame operations instead would improve maintainability.
• B. Fetching data from an external API inside a UDF can introduce performance bottlenecks and potential network issues. Snowflake recommends minimizing external dependencies within UDFs.
• C. The operation retrieves all rows into the client's memory, which can be inefficient for large datasets. Consider using 'write' operation to store results to table.
• D. Defining a UDF within the stored procedure creates unnecessary overhead as the UDF is recompiled every time the stored procedure runs. It's more efficient to define the UDF separately and call it from the procedure.
• E. The code is perfectly fine and doesn't require any improvements, as it effectively enriches customer data using a UDF and Snowpark DataFrame operations.