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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q284-Q289):

NEW QUESTION # 284
A financial analyst is using Snowflake to forecast stock prices based on historical data'. They have a table named 'STOCK PRICES with columns 'TRADE DATE (DATE) and 'CLOSING PRICE (NUMBER). They want to implement a custom moving average calculation using window functions to smooth out short-term fluctuations and identify trends. Specifically, they need to calculate a 7-day weighted moving average, where the most recent day has the highest weight and the weights decrease linearly. Which SQL statement correctly implements this weighted moving average calculation?

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

Answer: D

Explanation:
Option E is the correct answer because it accurately calculates the 7-day weighted moving average with linearly decreasing weights. It assigns weights from 7 (most recent) down to 1 (oldest) within the 7-day window. The weight calculation '(7 - ROW_NUMBER() OVER (ORDER BY TRADE DATE DESC) + 1)' ensures the most recent date has a weight of 7, and the weights decrease linearly to 1. The sum of the weighted closing prices is then divided by the sum of the weights to get the weighted average. Other options are incorrect because they either calculate a simple moving average, apply incorrect weights, or have syntactic errors. Option B and D's row_number() is ordered ascending, resulting in the oldest data point having the highest weight.

NEW QUESTION # 285
You are building a real-time dashboard to monitor website traffic and user behavior for an e-commerce company. The data includes page views, clicks, add-to-carts, and purchases, streamed continuously into Snowflake. You need to visualize the conversion funnel (page views -> clicks -> add-to-carts -> purchases) in real-time and identify drop-off points. Given the following table schema: "'sql CREATE OR REPLACE TABLE website_events ( event_timestamp TIMESTAMP NTZ, event_type VARCHAR(50), user_id VARCHAR(IOO), page_url VARCHAR(255) ); Which approach, including code snippets, would be the MOST efficient and scalable way to achieve this real-time conversion funnel visualization, taking into account the high volume of streaming data?

A. Periodically query the 'website_events' table every 5 minutes, calculate conversion rates for each stage of the funnel using SQL aggregate functions, and update a static chart in a reporting tool.
B. Create a Snowflake Stream on the 'website_events' table. Develop a Snowpipe to continuously ingest data into the table. Utilize a BI tool like Tableau connected directly to the 'website_events' table. Build several dashboards each for event type.
C. Export the 'website_events' data to a message queue (e.g., Kafka) and use a stream processing framework (e.g., Flink) to calculate conversion funnel metrics. Then, load the results into a separate table in Snowflake and visualize it using a BI tool.
D. Create a Snowflake Stream on the 'website_events' table. Create a Snowpipe to ingest data, and build a materialized view that pre-calculates the conversion funnel metrics. Connect a real-time dashboarding tool (e.g., Apache Superset, Grafana) to the materialized view to display the funnel in real-time.
E. Load all 'website_eventS table data into Python Pandas dataframes and use libraries to find the conversion rate in real time.

Answer: D

Explanation:
Option C is the most efficient and scalable. A Snowflake Stream allows you to track changes to the 'website_events' table in real- time. A Snowpipe enables continuous data ingestion. A materialized view pre-calculates the conversion funnel metrics, significantly improving query performance compared to querying the base table directly, especially with high data volumes. Connecting a real-time dashboarding tool to the materialized view provides a real-time view of the funnel. Option A involves periodic querying, which is less real-time and less efficient. Option B suggests direct connection with a BI tool without pre-aggregating the Data, resulting into dashboard performance issue. Option D introduces unnecessary complexity with external message queues and stream processing frameworks. Exporting data to Python dataframe is not scalable for large data volumes.

NEW QUESTION # 286
A telecommunications company wants to identify customers whose service addresses fall within a specific service area polygon defined as a Well-Known Text (WKT) string. The customer addresses are stored in a table 'CUSTOMER ADDRESSES' with a 'ADDRESS POINT column of type GEOGRAPHY. You have the WKT representation of the service area polygon stored in a variable '@service area_wkt'. Which of the following statements will correctly identify the customers within the service area? (Select all that apply)

Answer: A,C

Explanation:
TO_GEOGRAPHY(@service_area_wkt))' correctly identifies points that fall within the service area polygon. converts the WKT string into a GEOGRAPHY object. 'ST_COVERS(TO_GEOGRAPHY(@service_area_wkt), ADDRESS_POINT)' checks if the polygon covers the point.

NEW QUESTION # 287
While reviewing the query profile for a complex data transformation pipeline, you notice a significant amount of time spent in the 'Join' operation between two large tables, 'transactions' and 'customers'. The join is performed on the 'customer _ id' column. Which of the following are potential strategies to optimize the join performance?

A. Broadcasting the smaller table to all compute nodes.
B. Increase the size of the virtual warehouse.
C. Cluster both tables on the 'customer id' column.
D. Ensure that the 'customer_id' column has identical data types in both tables.
E. Rewrite the query to use a 'LATERAL FLATTEN' function instead of a 'JOIN'.

Answer: C,D

Explanation:
Mismatched data types can cause implicit type conversions, which can significantly degrade join performance. Clustering both tables on the join key ('customer_id') will ensure that rows with the same customer ID are located closer together on disk, reducing 1/0 and improving join efficiency. Broadcasting the smaller table is not directly controllable by the user in Snowflake and is handled automatically by the optimizer. 'LATERAL FLATTEN' is not a direct replacement for a join. Increasing the virtual warehouse size might improve overall processing time but doesn't address the specific inefficiency of the join operation.

NEW QUESTION # 288
You are analyzing the query execution plan of a complex data transformation pipeline in Snowflake. The plan shows a 'Remote Join' operation with high execution time. The two tables involved, 'CUSTOMER and 'ORDERS' , reside in different Snowflake accounts, and the join is performed on the 'CUSTOMER ID' column. Which of the following actions would MOST effectively optimize this query and reduce the 'Remote Join' execution time?

A. Replicate the smaller table (either 'CUSTOMER or 'ORDERS, based on size) to the same Snowflake account as the larger table to eliminate the remote join.
B. Create a materialized view in the ORDERS account that pre-aggregates the data needed for the join to reduce the data size sent over the network for remote join.
C. Ensure both 'CUSTOMER and 'ORDERS tables have the same clustering key, prioritizing 'CUSTOMER IDS.
D. Implement data filtering on the 'CUSTOMER table before the 'Remote Join' to reduce the amount of data transferred across accounts. Using temporary table can be used for this task.
E. Increase the warehouse size of the account containing the 'ORDERS' table to improve its processing speed.

Answer: A,D

Explanation:
Options B and C are the most effective. B eliminates the need for a remote join altogether, and C reduces the amount of data transferred during the remote join. Clustering keys (A) don't directly affect remote joins in the same way they affect local joins. Increasing warehouse size (D) can improve performance but doesn't address the fundamental issue of the remote join data transfer. Option E can help if the aggregated data fulfills the query's requirement and reduces significant data transfer, so it might be partially correct, but replicating data or filtering before joining is optimal in most cases.

NEW QUESTION # 289
......

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DAA-C01 ONLINE TEST & DAA-C01 TEST CRAM

DAA-C01 Online Test & DAA-C01 Test Cram