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| Section | Objectives |
|---|---|
| Topic 1: Snowflake Architecture and Data Platform Fundamentals | - Data platform fundamentals
|
| Topic 2: Security, Governance, and Data Sharing | - Access control and security
|
| Topic 3: Data Modeling and Performance Optimization | - Performance tuning
|
| Topic 4: Data Transformation and Analysis | - Analytical workloads
|
| Topic 5: Data Loading and Unloading | - Data ingestion methods
|
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NEW QUESTION # 21
You are creating a Snowsight dashboard to display the results of an A/B test on a website. You have the following tables: (columns: 'USER_ID', 'VARIANT' (VARCHAR, either 'A' or 'B'), 'CONVERSION' (BOOLEAN), 'TIMESTAMP') 'USER DEMOGRAPHICS' (columns: 'USER_ID, 'REGION', 'DEVICE) The stakeholders want to see the following visualizations: 1. Overall conversion rate for each variant. 2. Conversion rate for each variant broken down by region. 3. A table showing the statistical significance (p-value) of the difference in conversion rates between variants for each region, using a Chi-Square test. (Assume you have access to a stored procedure CHI SQUARE TEST(variant_a_conversions INT, variant_a_total INT, variant_b_conversions INT, variant b total INT) that returns the p-value.) Which combination of queries and Snowsight features will achieve the desired outcome with optimal performance and maintainability?
Answer: B
Explanation:
Option B is the most efficient and maintainable. Creating two views allows for clean separation of concerns and reusability. 'VARIANT CONVERSION RATES pre-calculates the conversion rates, making the statistical significance calculation in cleaner and more performant. The resulting Snowsight dashboard is then simple to build using these views. Option A introduces unnecessary complexity by involving external tools (Python) and creating a new table. Option C attempts to do too much within Snowsight's calculated fields, which is not ideal for complex calculations like calling stored procedures. Option D might be performant, but makes the data presentation inflexible and ties the data to this specific dashboard. Option E is good to have the data aggregated, however it depends on option B to create the dashboard with the aggregated data.
NEW QUESTION # 22
Which aspects are important when making predictions based on data for forecasting purposes?
(Select all that apply)
Answer: A,D
Explanation:
Incorporating statistical methods and considering trends and anomalies are crucial for accurate predictions in forecasting.
NEW QUESTION # 23
What challenges might arise when handling JSON data in Snowflake using built-in functions for traversing, flattening, and nesting? (Select all that apply)
Answer: A,C
Explanation:
Challenges include handling nested structures and complexities in parsing and querying nested JSON objects, which might arise when manipulating JSON data in Snowflake.
NEW QUESTION # 24
Consider a 'customer_orders' table with 'customer_id' , 'order_date', and 'order_amount'. You need to identify customers who have placed orders consistently over the last 3 months, specifically, you need to find customers who have placed an order in each of the last 3 months (including the current month). Assume the current date is '2024-01-15'. Which of the following query snippets, when incorporated into a complete query, would be most efficient and accurate for identifying these customers?
Answer: B
Explanation:
Option E is the most precise and efficient. It explicitly checks if a customer has an order in each of the three specific months (November, December, January). It does this by truncating the 'order_date' to the beginning of the month using 'DATE TRUNC('MONTH', order_datey and then comparing against the truncated values for the last three months calculated using 'DATEADD. The 'SUM' will only be equal to 3 if the customer has at least one order in each of those months. Option A calculates the number of distinct months for each customer but doesn't guarantee they are the last 3 months. Option B checks if the customer has placed at least 3 orders in the last 3 months, but it might be that all 3 orders are in a single month. Option C doesn't count distinct months. Option D only returns 1 if the customer has placed an order in the last 3 months. It does not guarantee the customer placed an order in all the past 3 months.
NEW QUESTION # 25
A data analyst is experiencing slow query performance when joining two large tables, 'SALES' (1 billion rows) and 'CUSTOMERS' (10 million rows), on 'CUSTOMER ID. The 'SALES' table is frequently updated. The following query is used: SELECT s. , c. FROM SALES s JOIN CUSTOMERS c ON s.CUSTOMER_lD = c.CUSTOMER_lD WHERE s.SALE_DATE DATEADD(day, -30, CURRENT DATE()); Which of the following strategies would MOST effectively improve the query performance, assuming you have appropriate privileges to alter objects and cost is a concern?
Answer: A
Explanation:
Creating 'SALES LAST 30 DAYS' table and joining is the most effective strategy because it reduces the size of the SALES table significantly before the join operation. Materialized views (A) are generally effective, but the high update frequency of the SALES table could lead to significant overhead in materialized view maintenance. Standard indexes (B) are not supported in Snowflake. Search optimization (C) may not be suitable as the could have high cardinality. Clustering (D) could improve performance, but the benefits might not outweigh the cost of reclustering after frequent data loading, and it does not limit the initial size of the join.
NEW QUESTION # 26
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