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[General] 100% Pass Quiz High Hit-Rate Snowflake - DAA-C01 - SnowPro Advanced: Data Analys

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【General】 100% Pass Quiz High Hit-Rate Snowflake - DAA-C01 - SnowPro Advanced: Data Analys

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q33-Q38):NEW QUESTION # 33
You are tasked with improving the performance of queries against a very large table named 'SALES DATA, which contains sales transactions from multiple regions. The table is frequently queried based on 'REGION' , 'SALE DATE, and 'PRODUCT CATEGORY. The data in 'SALE DATE' ranges from 2020 to the present. You want to optimize query performance by leveraging Snowflake's clustering capabilities, but you also want to minimize the impact of data ingestion and maintenance. Considering the characteristics of the data and query patterns, which of the following strategies would be the most effective and cost-efficient approach for clustering 'SALES DATA'?
  • A. Create a materialized view pre-aggregating the data by 'REGION' , 'SALE_DATE, and 'PRODUCT_CATEGORY'. No explicit clustering is required on the base table.
  • B. Cluster by ' REGION' and only. Snowflake will automatically optimize for 'PRODUCT_CATEGORY due to its internal statistics and query optimization.
  • C. Partition the table by 'SALE_DATE and cluster within each partition by ' REGIO!V and PRODUCT_CATEGORY.
  • D. Cluster by 'REGION', 'SALE DATE , and 'PRODUCT CATEGORY in that order. Monitor clustering depth frequently and rebuild the table if clustering depth exceeds a threshold.
  • E. Cluster by 'SALE DATE only. Snowflake's automatic clustering will handle the other dimensions over time, and this minimizes upfront clustering costs.
Answer: D
Explanation:
Option A, clustering by 'REGION', 'SALE DATE', and 'PRODUCT CATEGORY' in that order is the most effective approach. Given the frequent filtering on these three columns, clustering on them ensures that Snowflake can efficiently prune the data and minimize the amount of data scanned during query execution. By monitoring clustering depth and rebuilding when it exceeds a threshold, you can maintain optimal performance over time as new data is ingested. Option B is not ideal because SALE_DATE alone might not be sufficient for all query patterns. Option C is not ideal because Snowflake does not automatically optimize 'PRODUCT_CATEGORY only because of stats. Option D, Materialized view might not be good because they are usually pre aggregated and in this scenario are required raw data. E is invalid because partition does not exist in snowflake.

NEW QUESTION # 34
You are working with a dataset containing customer orders and their associated products. The data is stored in two tables: 'ORDERS' (order_id, customer_id, order_date) and 'ORDER ITEMS' (order_id, product_id, quantity, price). The Bl team needs to analyze the top- selling products and the average order value per customer. However, due to data quality issues, some 'order _ id' values in the 'ORDER ITEMS table do not exist in the 'ORDERS' table, leading to data inconsistencies. What is the most robust and efficient way to handle this data inconsistency issue while ensuring accurate reporting?
  • A. Use an INNER JOIN between 'ORDERS' and 'ORDER ITEMS'. Before joining, use a subquery or CTE to filter 'ORDER ITEMS' to include only 'order_id' values that exist in 'ORDERS'. After joining apply a filter for valid prices(Price > 0). This ensures correct analysis and avoids invalid orders. Select all the rows with valid prices (Price > 0).
  • B. Use a UNION ALL statement to combine the two tables, creating a single table with all order and item information. Handle inconsistencies during data extraction and loading (ETL) processes.
  • C. Create a flattened view that combines 'ORDERS' and 'ORDER_ITEMS' using a FULL OUTER JOIN. Handle missing values in the reporting layer.
  • D. Use a LEFT JOIN from 'ORDERS' to 'ORDER ITEMS to include all orders, even if they don't have corresponding items. Filter out rows with NULL 'product_id' in the final result.
  • E. Use a RIGHT JOIN from 'ORDERS' to 'ORDER ITEMS' and filter out rows where order details are missing.
Answer: A
Explanation:
Option B provides the most robust and efficient solution. By using an INNER JOIN after filtering the 'ORDER_ITEMS' table to include only valid 'order_id' values (those present in the 'ORDERS' table), you ensure that the analysis is based on consistent and reliable data. The subquery/CTE effectively cleans the data before joining, preventing inaccurate calculations. Filtering for valid prices ensures data quality. Option A would include orders without item details, which is not desired for this analysis. Options C and D might result in inaccurate results due to the incomplete relationship between orders and products. Option E is not suitable since it mixes the distinct column sets, resulting in inaccurate analyses. Always ensure referential integrity where business logic dictates.

NEW QUESTION # 35
You are building a data pipeline to ingest customer data into Snowflake. You have identified a need to dynamically determine the data load timestamp during the ingestion process itself, without relying on external systems or pre-defined variables. Which system function(s) would be the MOST appropriate and efficient choice to accomplish this?
  • A. CURRENT _ TIMESTAMP()
  • B. NOW()
  • C. SYSDATE()
  • D. GETDATE()
  • E.
Answer: A
Explanation:
The function returns the current timestamp at the start of the statement. 'SYSDATE()' and 'GETDATE()' functions does not exists in Snowflake. is a synonym for CURRENT TIMESTAMP(). However, is not a standard documented function.

NEW QUESTION # 36
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?
  • A. Create three separate charts using raw SQL queries for each visualizatiom Calculate the p-value outside of Snowflake using Python and load it into a new table. Use this new table to display a table chart with p-values.
  • B. Create a single view 'COMBINED_DATX that joins and ' USER_DEMOGRAPHICS'. Use this view in Snowsight to create all three charts. Use calculated fields in Snowsight to determine conversion rates and call the stored procedure for p-value within the calculated field.
  • C. Create a stored procedure that takes 'start_date' and 'end_date' as parameters, performs all calculations (conversion rates and Chi-Square tests), and returns three result sets for the visualizations. Use the stored procedure as the data source for the Snowsight dashboard.
  • D. Create two views: 'VARIANT CONVERSION RATES': Calculates the overall and regional conversion rates. RESULTS': Executes for each region based on the view. Create three charts in Snowsight, using the views for the conversion rates and statistical significance table.
  • E. Create a task to aggregate the number of conversions and total views from the ab test data daily. Create views using the aggregated table to build the requested charts in Snowsight.
Answer: D
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 # 37
What role do secure views play in data analysis practices?
  • A. They prevent the creation of materialized views.
  • B. Secure views limit access to data, hindering analysis.
  • C. They don't impact data security but significantly enhance query performance.
  • D. Secure views offer enhanced data security while allowing selective data access.
Answer: D
Explanation:
Secure views enhance data security by allowing selective data access while benefiting analysis.

NEW QUESTION # 38
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