RELIABLE DAA-C01 DUMPS SHEET - DAA-C01 VALID BRAINDUMPS EBOOK

Reliable DAA-C01 Dumps Sheet - DAA-C01 Valid Braindumps Ebook

Reliable DAA-C01 Dumps Sheet - DAA-C01 Valid Braindumps Ebook

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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q182-Q187):

NEW QUESTION # 182
You are tasked with creating a data model for a global e-commerce company in Snowflake. They have data on customers, products, orders, and website events. They need to support complex analytical queries such as 'What are the top 10 products purchased by customers in the US who have visited the website more than 5 times in the last month?' The data volumes are very large, and query performance is critical. Which of the following data modeling techniques and Snowflake features, used in combination, would be MOST effective?

  • A. A star schema with fact and dimension tables, combined with clustering the fact table on a composite key of customer ID and product ID.
  • B. A wide, denormalized table containing all customer, product, order, and event data, combined with Snowflake's zero-copy cloning for data backups.
  • C. A data vault model, combined with Snowflake's search optimization service on the hub tables.
  • D. A star schema with fact and dimension tables, combined with materialized views to pre-aggregate data and clustering on dimension keys in the fact table.
  • E. A fully normalized relational model with primary and foreign key constraints, combined with Snowflake's automatic query optimization.

Answer: A,D

Explanation:
Options B and E are the most effective. A star schema (B and E) is well-suited for analytical workloads. Clustering the fact table on customer and product IDs (B) helps improve query performance when filtering on those dimensions. Materialized views (E) provide pre-aggregated data for common queries, further boosting performance. Normalization (A) can lead to too many joins. Data Vault (C) is complex and may not be necessary. A wide, denormalized table (D) can be difficult to manage and maintain, and zero-copy cloning is for backup, not performance. Clustering on dimension keys in the fact table works best when coupled with a star schema and the keys are frequently used as a filter.


NEW QUESTION # 183
You have a Snowflake table 'order details' with columns 'order id', 'customer id', 'order date', and 'order amount'. You need to calculate the 3-month moving average of 'order_amount' for each customer, but only for those customers who have placed at least 5 orders. Which of the following SQL statements will correctly achieve this? (Assume the current date is '2024-01-01 ')

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

Answer: A

Explanation:
Option E is the correct and most clear solution. It calculates the 3-month moving average, filters customers who have placed at least 5 orders, and leverages the power and clarity of Snowflake syntax. The QUALIFY clause effectively filters for customers with at least 5 orders. The 'RANGE BETWEEN INTERVAL '3 MONTH' PRECEDING AND CURRENT ROW accurately calculates the moving average over a 3- month window based on A, B and C calculate a simple moving average of the last 3 rows regardless of date, while D is syntactically invalid as HAVING cannot be used with window function in this way.


NEW QUESTION # 184
You are analyzing customer order data in Snowflake. The 'orders' table has columns: and 'order_totar. Your task is to identify the top 5 customers who have consistently placed high-value orders over time. You need to rank customers based on their average order total, but only consider customers who have placed at least 10 orders. Furthermore, you want to account for the recency of orders by applying a weighted average where more recent orders contribute more to the average. Which of the following approaches will efficiently achieve this goal in Snowflake?

  • A. Create a stored procedure to iterate through each customer, calculate the weighted average order total, and then rank them in the application layer.
  • B. Calculate the average order total using AVG(), filter customers with COUNT( ) >= 10 using HAVING, then rank them using RANK() OVER (ORDER BY average_order_total DESC).
  • C. Calculate the average order total and order count for each customer using a subquery, then join the results with a generated series of dates to calculate the weighted average in the outer query, finally ranking the customers using DENSE_RANK().
  • D. Use a QUALIFY clause in conjunction with window functions to filter customers with at least 10 orders and calculate both average and weighted average. Then use the ranking function over the weighted average.
  • E. Use a weighted average calculation involving a date-based weighting factor (e.g., days since the order date), calculate the average order total with this weighting, filter with COUNT( ) >= 10 using HAVING, and then rank using RANK() OVER (ORDER BY weighted_average_order_total DESC).

Answer: D,E

Explanation:
Both options B and E correctly address the problem. B calculates a weighted average, filters based on the minimum order count, and then ranks customers based on the weighted average. E achieves the same result in using QUALIFY which is an important technique to filter. Option A doesn't account for weighting. C is inefficient and does not leverage Snowflake's processing power. D is unnecessarily complex with join, date series and subquery for simple operation that can be achieved using window functions.


NEW QUESTION # 185
You are designing a data ingestion pipeline for a financial institution. The pipeline loads transaction data from various sources into a Snowflake table named 'TRANSACTIONS. The 'TRANSACTIONS table includes columns such as TRANSACTION , 'ACCOUNT ID', 'TRANSACTION DATE, 'TRANSACTION AMOUNT, and 'TRANSACTION TYPE. The data is loaded in micro- batches using Snowpipe. Due to potential source system errors and network issues, duplicate records with the same 'TRANSACTION ID' are occasionally ingested. You need to ensure data integrity by preventing duplicate 'TRANSACTION_ID' values in the 'TRANSACTIONS' table while minimizing the impact on ingestion performance. Which of the following approaches is the MOST efficient and reliable way to handle this deduplication requirement in Snowflake, considering data integrity and performance?

  • A. Create a stream on the 'TRANSACTIONS' table and use it to identify newly inserted rows. Then, use a merge statement to insert new, distinct transactions into a separate staging table. Finally, periodically truncate the original 'TRANSACTIONS table and load the deduped data from the staging table.
  • B. Create a scheduled task that runs every hour to identify and delete duplicate records based on 'TRANSACTION ID. The task will use a SQL query to find duplicate ' TRANSACTION ID values and remove the older entries.
  • C. Define as the primary key on the 'TRANSACTIONS' table. Snowflake will automatically reject any duplicate inserts during Snowpipe ingestion.
  • D. Create a staging table with the same schema as 'TRANSACTIONS'. Use a 'MERGE' statement within the Snowpipe load process to insert new records from the incoming data into the 'TRANSACTIONS' table, only if the 'TRANSACTION ID does not already exist. Define 'TRANSACTION ID' as the primary key in the staging table. Use clustering on 'TRANSACTION_ID on the target 'TRANSACTIONS' table.
  • E. Use a materialized view built on top of the TRANSACTIONS table that selects distinct transaction ids. This ensures that querying through the materialized view returns no duplicates.

Answer: D

Explanation:
Option E provides the most performant and robust solution. Although Snowflake doesn't enforce primary key constraints, defining them on the staging table and leveraging a 'MERGE' statement during the Snowpipe load process allows for efficient deduplication. Clustering on TRANSACTION_I[Y on the target table also helps with performance. A regular task would be less efficient and introduce latency. Snowflake does not automatically reject duplicate inserts based on defined primary keys (option A). Materialized views don't prevent duplicate data from entering the base table. Option C is possible but more complex to implement than a MERGE statement.


NEW QUESTION # 186
Consider the following SQL query:

Analyzing the Query Profile, you observe that the 'WHERE' clause is not effectively filtering the data'. Which of the following actions could improve performance, assuming the 'order date' column is NOT currently clustered or indexed?

  • A. Create a materialized view with the 'WHERE clause condition and relevant columns.
  • B. Increase the virtual warehouse size.
  • C. Partition the 'orders' table by 'order_date' .
  • D. Cluster the 'orders' table on the 'order date' column.
  • E. Create a secondary index on the 'order_date' column.

Answer: A,D

Explanation:
Clustering on 'order_date' will physically organize the data on disk according to the date, allowing Snowflake to efficiently retrieve the relevant data based on the 'WHERE clause. Creating a materialized view pre-computes the result set based on the filter, so the query would only need to retrieve the top 10 from that smaller dataset. Secondary indexes are not supported in Snowflake. Partitioning is not a feature in Snowflake. Increasing the warehouse size might help with processing power, but it won't directly improve data access efficiency.


NEW QUESTION # 187
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