How does using Snowsight's data loading capabilities impact the overall data preparation process?

• A.Doesn't support batch data loading
• B.Streamlines data loading, reducing preparation time
• C.Increases data transformation complexities
• D.Limits data loading to specific file formats

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A marketing team wants to understand the impact of their campaigns on website traffic and conversions. You have the following tables in Snowflake: sCAMPAlGN PERFORMANCE: 'CAMPAIGN ONT), (DATE), 'CLICKS' (INT), 'IMPRESSIONS' (INT), 'COST (NUMBER) 'DATE' (DATE), 'PAGE_VIEWS' ONT), (INT) 'CONVERSIONS' 'DATE (DATE), ONT), (NUMBER) Which SQL query and visualization technique would be most suitable for identifying the correlation between campaign spend and website conversions over time, allowing the team to quickly identify campaigns with a high return on investment (ROI)?

• A.A simple bar chart showing total clicks per campaign ID, generated from a query that joins 'CAMPAIGN PERFORMANCE with 'WEBSITE _ TRAFFIC' on the DATE column.
• B.A scatter plot visualizing the relationship between 'COST' from 'CAMPAIGN PERFORMANCE and 'CONVERSION COUNT from 'CONVERSIONS', aggregated by 'DATE and calculated ROI, generated from a query using window functions to compute cumulative sums and moving averages.
• C.Three separate pie charts, one showing the percentage of clicks per campaign, one showing the percentage of impressions per campaign, and one showing the percentage of conversions per campaign.
• D.Aline chart displaying 'COST from 'CAMPAIGN PERFORMANCE, from 'WEBSITE TRAFFIC, and 'CONVERSION COUNT from 'CONVERSIONS' over time ( ' DATE), joined on the 'DATE column, with a calculated ROI metric displayed as a separate line on the same chart. The query uses a common table expression (CTE) to first calculate daily ROI.
• E.An excel Pivot table from exported data from all these tables which uses the data points required.

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What option would allow a Data Analyst to efficiently estimate cardinality on a data set that contains trillions of rows?

• A.Count(Distinct *)
• B.HLL(*)
• C.SYSTEM$ESTIMATE
• D.Count(Distinct *)/Count(*)

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You have identified corrupted data in a production table 'CUSTOMER DATA. Before attempting to clean the data directly in the production table, you want to create a safe environment to test your data cleaning scripts. You are also concerned about the impact of your data cleaning efforts on downstream reporting. Which of the following approaches using Snowflake clones is the MOST appropriate for this scenario?

• A.Create a zero-copy clone of 'CUSTOMER_DATA' named for testing. Clean the data in 'CUSTOMER_DATA_DEV'. Once satisfied, update the 'CUSTOMER_DATR table directly with the cleaning logic.
• B.Create a zero-copy clone of named for testing. Create another zero-copy clone of 'CUSTOMER DATA DEV' named 'CUSTOMER DATA REPORTING'. Clean the data in 'CUSTOMER DATA DENT. Point downstream reporting to 'CUSTOMER DATA REPORTING'.
• C.Create a full copy of 'CUSTOMER DATA' named 'CUSTOMER DATA DEV' for testing. Clean the data in 'CUSTOMER DATA DE-VS. Use a 'MERGE statement to update with the cleaned data from
• D.Create a zero-copy clone of 'CUSTOMER DATA' named 'CUSTOMER DATA DEV' for testing. Clean the data in 'CUSTOMER DATA DEV'. Create a zero- copy clone of 'CUSTOMER_DATX named Update 'CUSTOMER_DATX with the cleaning logic. Point the downstream reporting to 'CUSTOMER DATA REPORTING'.
• E.Create a zero-copy clone of named for testing. Clean the data in Create a separate table named 'CLEANED CUSTOMER DATA'. Insert the cleaned data from 'CUSTOMER DATA DEV' into the new 'CLEANED CUSTOMER DATA' table. Update with the cleaning logic.

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How do stored procedures contribute to the efficiency of data analysis using SQL?

• A.Stored procedures can't be used in conjunction with user-defined functions (UDFs).
• B.Stored procedures enable the execution of repetitive tasks, enhancing efficiency.
• C.They solely facilitate basic arithmetic operations.
• D.They limit data accessibility, hindering analysis.

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