A data analyst needs to enrich customer data in a Snowflake database with demographic information obtained from the Snowflake Marketplace. The purchased listing provides data as a secure view Which of the following SQL commands is the MOST efficient and secure way to create a new table in the data analyst's database that combines customer data with the demographic information from the Marketplace listing, while ensuring that only necessary columns from the Marketplace data are included?
Correct Answer: D
Option D is the most efficient and secure because: it creates a new table or replaces if one exists, it explicitly selects only the necessary columns (age, income) from the Marketplace view, preventing unnecessary data exposure. It also explicitly uses JOIN condition. A new table has to created instead of view for persistence and to prevent recomputing of the data on frequent requests.
Question 122
You are analyzing website traffic data in Snowflake and notice a sudden drop in page views from a specific country (Country A) starting last month. You have access to the 'WEBSITE TRAFFIC' table with columns: 'date' , 'country', 'page_viewS, 'device_type'. Which of the following queries and techniques would be MOST effective in identifying the potential cause of this anomaly?
Correct Answer: B,C,D
Options B, C, and D are the most effective. B uses statistical methods to identify the anomaly, C investigates potential external factors (marketing campaigns), and D explores internal segments (device types). Option A is a basic check but doesn't identify causes. Option E is not scalable and inefficient for large datasets. Using a combination of statistical analysis, external data integration, and segmentation provides a comprehensive diagnostic approach.
Question 123
In Snowflake, how does leveraging Time Travel contribute to handling data anomalies and ensuring data integrity?
Correct Answer: C
Time Travel in Snowflake allows querying data as of specific timestamps, aiding in addressing data anomalies and ensuring data integrity by accessing historical data versions.
Question 124
You are tasked with building a data ingestion pipeline to retrieve data from a transactional database using Change Data Capture (CDC).The source database is a MySQL instance. Which of the following approaches is MOST suitable for efficiently retrieving and loading the changed data into Snowflake while minimizing latency?
Correct Answer: B,C
Options B and C are the most efficient and reliable for CDC. Option B leverages the MySQL binary logs directly. Configuring replication to cloud storage and using Snowpipe provides a continuous and near real-time ingestion pipeline. Option C, using a dedicated CDC tool, offers a managed solution that handles the complexities of binary log parsing, change tracking, and data transformation. Option A is less efficient due to the need for periodic polling and potential data loss if changes occur between polls. Option D involves significant coding complexity and potential performance issues. Option E is generally not recommended for transactional data due to performance limitations.
Question 125
You are tasked with creating a dashboard to visualize customer churn. You have a Snowflake table named 'CUSTOMER DATA' with columns 'CUSTOMER ID, 'JOIN DATE, 'LAST ACTIVE DATE, 'REVENUE', and CHURNED' (BOOLEAN). You want to present a cohort analysis showing the retention rate of customers over time, grouped by their join month. Which of the following approaches using SQL and visualization techniques is the MOST effective for creating this cohort analysis visualization for a dashboard using only snowflake?
Correct Answer: C
Option C is the most efficient and scalable. Using a sequence and conditional aggregation allows calculating the retention rate for each cohort in a single SQL query. This avoids the overhead of stored procedures (B) or multiple queries (D). A line chart or heatmap provides an effective visualization of cohort retention over time. Option A can become complex to manage, especially when handling larger datasets. Finally, exporting the data for external visualization defeats the purpose of Snowflake analysis. Sequences and conditionals are preferred as Snowflake doesn't natively support loops like stored procedures for cohort analysis.
