You are tasked with implementing a Row Access Policy (RAP) on a table 'customer_data' that contains Personally Identifiable Information (PII). The policy must meet the following requirements: 1. Data analysts with the 'ANALYST role should only see anonymized customer data (e.g., masked email addresses, hashed names). 2. Data engineers with the 'ENGINEER role should see the full, unmasked customer data for data processing purposes. 3. No other roles should have access to the data'. You create the following UDFs: 'MASK EMAIL(email address VARCHAR)': Returns an anonymized version of the email address. 'HASH NAME(name VARCHAR): Returns a hash of the customer name. Which of the following is the most efficient and secure way to implement this RAP, assuming minimal performance impact is desired?

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

You are responsible for monitoring data quality in a Snowflake data warehouse. Your team has identified a critical table, 'CUSTOMER DATA, where the 'EMAIL' column is frequently missing or contains invalid entries. You need to implement a solution that automatically detects and flags these anomalies. Which of the following approaches, or combination of approaches, would be MOST effective in proactively monitoring the data quality of the 'EMAIL' column?

• A. Create a Snowflake Task that executes a SQL query to count NULL 'EMAIL' values and invalid 'EMAIL' formats (using regular expressions). The task logs the results to a separate monitoring table and alerts the team if the count exceeds a predefined threshold.
• B. Schedule a daily full refresh of the 'CUSTOMER DATA' table from the source system, overwriting any potentially corrupted data.
• C. Use Snowflake's Data Quality features (if available) to define data quality rules for the 'EMAILS column, specifying acceptable formats and thresholds for missing values. Configure alerts to be triggered when these rules are violated.
• D. Utilize an external data quality tool (e.g., Great Expectations, Deequ) to define and run data quality checks on the 'CUSTOMER DATA' table, integrating the results back into Snowflake for reporting and alerting.
• E. Implement a Streamlit application connected to Snowflake that visualizes the percentage of NULL and invalid 'EMAIL' values over time, allowing the team to manually monitor trends.

You are implementing a data share between two Snowflake accounts. The provider account wants to grant the consumer account access to a function that returns anonymized customer data based on a complex algorithm. The provider wants to ensure that the consumer cannot see the underlying implementation details of the anonymization algorithm. Which of the following approaches can achieve this goal? (Select TWO)

• A. Create an external function in the provider account and grant usage to the share. Share the share with the consumer account.
• B. Create a standard UDF in the provider account and grant usage on the UDF to the share. Share the share with the consumer account.
• C. Create a view that calls the secure UDF and share that view with the consumer account.
• D. Create a secure UDF in the provider account and grant usage on the secure UDF to the share. Share the share with the consumer account.
• E. Share the underlying table and provide the consumer account with the anonymization algorithm separately.

A Snowflake table 'ORDERS' contains billions of records and is frequently queried for reporting purposes. The reporting queries often filter on 'ORDER DATE and 'CUSTOMER ID'. The data engineering team is considering creating a clustering key to improve query performance. They are evaluating two options: (1) clustering on 'ORDER DATE' alone and (2) clustering on '(ORDER DATE, CUSTOMER ID)'. Which of the following statements best describes the trade-offs between these two options in the context of query performance and data maintenance?

• A. Clustering on ORDER DATE alone is preferable because it eliminates the risk of data skewness associated with 'CUSTOMER ID, leading to more balanced micro-partitions and consistent query performance.
• B. Clustering on '(ORDER DATE, CUSTOMER IDY is always the best option because it allows for more granular filtering and reduces the need to scan unnecessary micro-partitions, regardless of data distribution.
• C. Clustering on 'ORDER_DATE alone will result in better overall query performance because it's a single dimension, simplifying the clustering process and reducing the need for Snowflake to perform complex data scans.
• D. Clustering on ' (ORDER DATE, CUSTOMER_ID)' will provide better performance for queries filtering on both columns, but may lead to increased reclustering costs if distribution is skewed within "ORDER_DATE' partitions.
• E. Neither clustering option will significantly improve performance, and the team should focus on optimizing the queries themselves through techniques like query rewriting and the use of appropriate indexes.