Describe In-Database Analytics and SQL Essentials DATA-SCIENCE-FOUNDATIONS Practice Questions (Page 2)

Questions 6–10

1. 6application · medium

   A retail company stores billions of transaction records in a large data warehouse. Their data science team currently exports subsets of this data to a separate analytics cluster to run Python-based models. The team complains about long export times and network bottlenecks. The company wants to reduce data movement while still allowing the data science team to use their familiar Python tools. What should they do?

   • ASwitch to a different analytics platform that supports faster data transfer protocols.
   • BIncrease the network bandwidth between the warehouse and the analytics cluster to speed up data exports.
   • CCreate smaller summary tables in the warehouse and export only those to the analytics cluster.
   • DMove the Python models to run inside the database using the database's in-database analytics capabilities.
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2. 7application · medium

   A financial services company uses a SQL database for its core transaction data. Their risk analytics team wants to run complex statistical models on this data without moving it out of the database. They also want to use their preferred R environment for model development. What is the most appropriate approach?

   • AUse the database's in-database analytics features that support R integration.
   • BUse an ODBC connection to pull data into R for analysis.
   • CExport the data to a CSV file and load it into R on a separate server.
   • DReplicate the database to a separate analytics database and run R there.
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4. 8application · medium

   A sales team tracks daily sales in a table `daily_sales` with columns `sale_date` and `amount`. They want to calculate a running total of sales over time, ordered by date. Which SQL function should they use?

   • ALAG(amount) OVER (ORDER BY sale_date)
   • BSUM(amount) GROUP BY sale_date
   • CRANK() OVER (ORDER BY amount)
   • DSUM(amount) OVER (ORDER BY sale_date)
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5. 9application · medium

   A company has two tables: `employees` (employee_id, name, department_id) and `departments` (department_id, department_name). They want to list all employees along with their department names, but they also want to include departments that have no employees. Which join should they use?

   • AFULL OUTER JOIN
   • BLEFT JOIN from employees to departments
   • CRIGHT JOIN from employees to departments
   • DINNER JOIN
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6. 10expert · hard

   A company uses a SQL data warehouse for its core operational data. Their data science team wants to use a popular open-source machine learning library that is not available in the database. They are considering using in-database analytics for data preparation and then exporting a smaller dataset for model training. What is the primary benefit of this hybrid approach?

   • AIt reduces data movement by performing heavy data preparation in the database, while still allowing the use of specialized ML libraries.
   • BIt allows the use of SQL for model training.
   • CIt eliminates the need for data export entirely.
   • DIt improves the accuracy of the machine learning models.
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