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[General] Reliable DAA-C01 Test Tips - DAA-C01 Latest Test Answers

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【General】 Reliable DAA-C01 Test Tips - DAA-C01 Latest Test Answers

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P.S. Free & New DAA-C01 dumps are available on Google Drive shared by TorrentExam: https://drive.google.com/open?id=1xNzPB2NHNSYlHwqFlTxdEW5VdpMinPGw
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Snowflake SnowPro Advanced: Data Analyst Certification Exam Sample Questions (Q135-Q140):NEW QUESTION # 135
When performing forecasting using statistics and built-in functions, what role do these functions play?
  • A. These functions only apply to limited data types for forecasting.
  • B. They enable the creation of custom forecasting models as needed.
  • C. They solely aid in data visualization without impacting the forecasting process.
  • D. They restrict forecasting to predefined models without flexibility.
Answer: B
Explanation:
Built-in functions in statistics facilitate the creation of custom forecasting models, allowing flexibility in the forecasting process.

NEW QUESTION # 136
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. 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.
  • B. 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.
  • C. 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.
  • 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 # 137
You have a Snowflake table named 'orders' with columns (INT), (DATE), and (VARIANT). The 'order_detailS column contains a JSON object with information about items in the order, including a nested array of items called 'items' , where each item has a (INT) and 'quantity' (INT). You need to write a SQL query to extract all 'product_id' values from the 'items' array within the column for orders placed in the last 30 days. Which query will accomplish this most effectively?
  • A.
  • B.
  • C.
  • D.
  • E.
Answer: D
Explanation:
Option A correctly uses the 'LATERAL FLATTEN' function to unnest the 'items' array within the 'order_details variant column. This creates a row for each item in the array, allowing you to access the 'product_id' for each item. The 'WHERE clause filters the results to only include orders from the last 30 days. Options B, C, D and E do not correctly use the 'FLATTEN' function and would only retrieve the product_id of the first item in the array, if the array exists.

NEW QUESTION # 138
When designing a data collection system, what factors should be considered when assessing how often data needs to be collected? (Select all that apply)
  • A. Volume of data
  • B. Data source availability
  • C. Business requirements
  • D. Data collection tool limitations
Answer: A,C
Explanation:
Assessing data collection frequency involves considering business requirements and the volume of data necessary for analysis.

NEW QUESTION # 139
When utilizing User-Defined Functions (UDFs) in SQL, which statement accurately describes their usage?
  • A. UDFs can't be invoked within Stored Procedures.
  • B. UDFs are solely used for administrative tasks within databases.
  • C. They allow custom-defined operations on data, extending SQL functionalities.
  • D. UDFs are only applicable to numeric data types.
Answer: C
Explanation:
UDFs extend SQL functionalities by enabling custom operations on data, going beyond standard SQL capabilities.

NEW QUESTION # 140
......
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