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DAA-C01 Latest Test Answers - DAA-C01 Exam Cram QuestionsWith our professional experts¡¯ unremitting efforts on the reform of our DAA-C01 guide materials, we can make sure that you can be focused and well-targeted in the shortest time when you are preparing a test, simplify complex and ambiguous contents. With the assistance of our DAA-C01 Study Guide you will be more distinctive than your fellow workers. For all the above services of our DAA-C01 practice engine can enable your study more time-saving and energy-saving. 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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