50 MCQ Infpow Document PDF

**Case Study 1: E-commerce Transaction Processing** A company has multiple e-commerce platforms in different regions. They need to integrate transaction data from each platform into a central database for analysis. Each transaction includes details like TransactionID, CustomerID, ProductID, Quantit...

**Case Study 1: E-commerce Transaction Processing** A company has multiple e-commerce platforms in different regions. They need to integrate transaction data from each platform into a central database for analysis. Each transaction includes details like TransactionID, CustomerID, ProductID, Quantity, Price, TransactionDate, and Region. 1. **Which transformation would you use to remove duplicate TransactionID records?** - A\) Sorter Transformation - B\) Aggregator Transformation - C\) Filter Transformation - D\) Expression Transformation 2. **To calculate the total revenue per region, which transformation should be applied?** - A\) Joiner Transformation - B\) Aggregator Transformation - C\) Rank Transformation - D\) Filter Transformation 3. **To filter out transactions from regions outside of the company's operations, which transformation would you use?** - A\) Router Transformation - B\) Filter Transformation - C\) Expression Transformation - D\) Aggregator Transformation 4. **If you need to assign a unique sequential ID to each transaction, which transformation should be utilized?** - A\) Sequence Generator Transformation - B\) Rank Transformation - C\) Joiner Transformation - D\) Lookup Transformation 5. **To retrieve CustomerName based on CustomerID, which transformation would be appropriate?** - A\) Lookup Transformation - B\) Router Transformation - C\) Aggregator Transformation - D\) Sequence Generator Transformation **Case Study 2: Financial Data Integration** A finance company needs to integrate data from multiple financial institutions. The data includes AccountNumber, TransactionAmount, TransactionDate, and AccountType. The goal is to categorize transactions by account type and calculate total amounts per account. 6. **To categorize transactions into Savings, Checking, and Investment accounts, which transformation should be used?** - A\) Router Transformation - B\) Filter Transformation - C\) Expression Transformation - D\) Aggregator Transformation 7. **Which transformation would help calculate the total amount for each account type?** - A\) Aggregator Transformation - B\) Filter Transformation - C\) Router Transformation - D\) Rank Transformation 8. **If you need the top 5 transactions by amount for each account type, which transformation is appropriate?** - A\) Rank Transformation - B\) Sorter Transformation - C\) Aggregator Transformation - D\) Joiner Transformation 9. **To create unique IDs for each transaction, which transformation is recommended?** - A\) Sequence Generator Transformation - B\) Lookup Transformation - C\) Expression Transformation - D\) Update Strategy Transformation 10. **To combine data from two sources based on AccountNumber, which transformation should be used?** - A\) Joiner Transformation - B\) Lookup Transformation - C\) Aggregator Transformation - D\) Router Transformation **Case Study 3: Inventory Management System** A retail company has an inventory management system that tracks stock levels in multiple warehouses. The data includes ProductID, WarehouseID, QuantityOnHand, ReorderLevel, and Location. The company wants to track products below the reorder level. 11. **Which transformation would be suitable to separate products below the reorder level?** - A\) Filter Transformation - B\) Router Transformation - C\) Expression Transformation - D\) Aggregator Transformation 12. **To sum up the QuantityOnHand across warehouses for each product, which transformation should be applied?** - A\) Aggregator Transformation - B\) Joiner Transformation - C\) Sequence Generator Transformation - D\) Lookup Transformation 13. **To combine stock data from two warehouse databases, which transformation is suitable?** - A\) Joiner Transformation - B\) Lookup Transformation - C\) Rank Transformation - D\) Sequence Generator Transformation 14. **To categorize products as Low, Medium, or High stock based on QuantityOnHand, which transformation would you use?** - A\) Expression Transformation - B\) Filter Transformation - C\) Router Transformation - D\) Aggregator Transformation 15. **Which transformation should be used to sort the data by QuantityOnHand in ascending order?** - A\) Sorter Transformation - B\) Rank Transformation - C\) Aggregator Transformation - D\) Sequence Generator Transformation **Case Study 4: Customer Data Cleansing** A CRM system receives customer data from multiple sources with varying formats. The company needs to standardize customer phone numbers, eliminate duplicates, and validate emails. 16. **Which transformation should be used to validate phone numbers using a regular expression?** - A\) Expression Transformation - B\) Router Transformation - C\) Filter Transformation - D\) Sorter Transformation 17. **To remove duplicate records based on CustomerID, which transformation should be applied?** - A\) Aggregator Transformation - B\) Sorter Transformation - C\) Sequence Generator Transformation - D\) Rank Transformation 18. **To filter out records with missing Email or PhoneNumber, which transformation is ideal?** - A\) Filter Transformation - B\) Router Transformation - C\) Lookup Transformation - D\) Joiner Transformation 19. **Which transformation allows creating an audit log of records with invalid data?** - A\) Router Transformation - B\) Aggregator Transformation - C\) Rank Transformation - D\) Sequence Generator Transformation 20. **To check if a CustomerID exists in a reference table, which transformation should be used?** - A\) Lookup Transformation - B\) Joiner Transformation - C\) Filter Transformation - D\) Aggregator Transformation **Case Study 5: Employee Data Integration** A company needs to integrate HR data from different regions. Each dataset includes EmployeeID, FirstName, LastName, Salary, and Department. The goal is to create a unified dataset with unique EmployeeID values and calculate the total salary per department. 21. **Which transformation should you use to remove duplicate EmployeeID values?** - A\) Sorter Transformation - B\) Aggregator Transformation - C\) Sequence Generator Transformation - D\) Rank Transformation 22. **To calculate the total salary per department, which transformation is suitable?** - A\) Aggregator Transformation - B\) Router Transformation - C\) Filter Transformation - D\) Sequence Generator Transformation 23. **To join data from two sources based on EmployeeID, which transformation should be used?** - A\) Joiner Transformation - B\) Lookup Transformation - C\) Rank Transformation - D\) Filter Transformation 24. **To create a unique EmployeeID for each record, which transformation is appropriate?** - A\) Sequence Generator Transformation - B\) Expression Transformation - C\) Aggregator Transformation - D\) Joiner Transformation 25. **To filter employees with salaries above a certain threshold, which transformation is ideal?** - A\) Filter Transformation - B\) Router Transformation - C\) Aggregator Transformation - D\) Rank Transformation **26. Which transformation would you use to generate unique sequential numbers for each row in a dataset, such as creating a primary key for each record?** - A\) Expression Transformation - B\) Sequence Generator Transformation - C\) Rank Transformation - D\) Aggregator Transformation **27. If you need to direct rows to different target tables based on conditions, such as routing customer records from different regions to separate tables, which transformation would you use?** - A\) Filter Transformation - B\) Router Transformation - C\) Aggregator Transformation - D\) Expression Transformation **28. You have a requirement to control how each row is treated when loading data into the target, allowing operations like insert, update, delete, or reject based on specific conditions. Which transformation would enable this functionality?** - A\) Update Strategy Transformation - B\) Expression Transformation - C\) Filter Transformation - D\) Aggregator Transformation **29. Which transformation would you use if you need to order data based on one or more fields, such as sorting employees by salary in descending order?** - A\) Sorter Transformation - B\) Rank Transformation - C\) Joiner Transformation - D\) Aggregator Transformation **30. To calculate the average sales amount for each product category, which transformation would you select to perform the aggregation?** - A\) Lookup Transformation - B\) Aggregator Transformation - C\) Filter Transformation - D\) Rank Transformation **31. You have two separate datasets with the same structure that you need to combine into a single dataset. Which transformation is best suited for this task?** - A\) Joiner Transformation - B\) Lookup Transformation - C\) Union Transformation - D\) Router Transformation **32. You need to assign ranks to a dataset based on a column value, such as ranking employees by performance score. Which transformation should be used for this purpose?** - A\) Rank Transformation - B\) Aggregator Transformation - C\) Filter Transformation - D\) Sorter Transformation **33. You have a requirement to apply multiple conditions and send rows to different targets or streams based on these conditions. Which transformation allows complex filtering into multiple groups?** - A\) Router Transformation - B\) Filter Transformation - C\) Expression Transformation - D\) Sorter Transformation **34. Which transformation would you use to enhance the data flow by looking up additional information from a reference table or database, such as adding customer details based on CustomerID?** - A\) Router Transformation - B\) Lookup Transformation - C\) Aggregator Transformation - D\) Joiner Transformation **35. If you are working with structured XML data and need to import it as a source, which transformation allows you to parse and read XML format data directly?** - A\) XML Source Transformation - B\) File Target Transformation - C\) Lookup Transformation - D\) Expression Transformation **36. If you need to control transaction boundaries in your mapping, allowing you to commit or roll back changes based on a specific condition, which transformation would you use?** - A\) Transaction Control Transformation - B\) Filter Transformation - C\) Router Transformation - D\) Expression Transformation **37. You are working with unstructured flat file data, such as CSV files, and need to map and transform it within your mapping. Which transformation would be used to handle and read flat file sources?** - A\) XML Source Transformation - B\) File Source Transformation - C\) Expression Transformation - D\) Lookup Transformation **38. To merge two datasets from different sources based on a unique identifier, such as merging EmployeeID from two separate HR systems, which transformation would you use?** - A\) Joiner Transformation - B\) Union Transformation - C\) Router Transformation - D\) Rank Transformation **39. You need to apply specific business rules on each row of data, such as calculating a discount based on SalesAmount. Which transformation allows for row-level data manipulation?** - A\) Expression Transformation - B\) Aggregator Transformation - C\) Joiner Transformation - D\) Filter Transformation **40. You need to group records into categories and process them differently, such as processing high-value customers and low-value customers in separate streams. Which transformation is best suited for this?** - A\) Router Transformation - B\) Filter Transformation - C\) Rank Transformation - D\) Aggregator Transformation **41. Which transformation is most appropriate if you need to perform a SQL override to retrieve specific data from a relational table during a lookup?** - A\) Lookup Transformation - B\) Joiner Transformation - C\) Filter Transformation - D\) Aggregator Transformation **42. If you need to filter out records dynamically based on specific conditions, such as selecting only transactions above a certain amount, which transformation would you use?** - A\) Filter Transformation - B\) Router Transformation - C\) Lookup Transformation - D\) Rank Transformation **43. You have a need to map data as key-value pairs for easier retrieval and referencing. Which transformation would you use to set up key-value mappings?** - A\) Lookup Transformation - B\) Key Transformation - C\) Joiner Transformation - D\) Expression Transformation **44. Which transformation would help you identify and remove duplicate records based on a specific field, such as removing duplicate customer records based on CustomerID?** - A\) Sorter Transformation - B\) Aggregator Transformation - C\) Sequence Generator Transformation - D\) Rank Transformation **45. If your mapping requires you to call a stored procedure in the source or target database for data processing or validation, which transformation should you use?** - A\) Stored Procedure Transformation - B\) Lookup Transformation - C\) Filter Transformation - D\) Joiner Transformation **46. When consolidating data from multiple sources in a vertical fashion (adding more rows from other sources), which transformation would be most appropriate?** - A\) Union Transformation - B\) Joiner Transformation - C\) Aggregator Transformation - D\) Router Transformation **47. To retrieve the last record in each group, based on a specific sorting, such as finding the last transaction for each customer, which transformation would you use?** - A\) Rank Transformation - B\) Aggregator Transformation - C\) Filter Transformation - D\) Sorter Transformation **48. If you need to convert data types, such as changing String to Integer or converting date formats, which transformation is ideal?** - A\) Expression Transformation - B\) Filter Transformation - C\) Joiner Transformation - D\) Router Transformation **49. For scenarios where you need to join large data sets from different sources, such as joining CustomerID from two massive datasets, which transformation is optimized for such tasks?** - A\) Joiner Transformation - B\) Aggregator Transformation - C\) Rank Transformation - D\) Filter Transformation **50. If you need to handle individual source system transactions by controlling when to commit or roll back changes based on conditions, which transformation would you select?** - A\) Transaction Control Transformation - B\) Expression Transformation - C\) Aggregator Transformation - D\) Sequence Generator Transformation **Solutions: Case Study 1: E-commerce Transaction Processing** 1. **A) Sorter Transformation**\ *Explanation:* The Sorter Transformation can sort and remove duplicate rows when Distinct is enabled, making it ideal for removing duplicate TransactionID records. 2. **B) Aggregator Transformation**\ *Explanation:* Aggregator Transformation calculates aggregate functions such as SUM, which is required here to calculate the total revenue per region. 3. **B) Filter Transformation**\ *Explanation:* Filter Transformation allows you to include or exclude rows based on conditions, useful for filtering out transactions outside of company operations. 4. **A) Sequence Generator Transformation**\ *Explanation:* Sequence Generator Transformation generates unique sequential numbers, which can serve as IDs for each transaction. 5. **A) Lookup Transformation**\ *Explanation:* Lookup Transformation fetches additional data (like CustomerName) from a reference table using a primary key, CustomerID. **Case Study 2: Financial Data Integration** 6. **A) Router Transformation**\ *Explanation:* Router Transformation splits data into multiple streams based on conditions, which is perfect for categorizing transactions by account type. 7. **A) Aggregator Transformation**\ *Explanation:* To calculate totals by account type, Aggregator Transformation is ideal, as it allows summing TransactionAmount by grouping AccountType. 8. **A) Rank Transformation**\ *Explanation:* Rank Transformation is used to rank or filter rows within a group, here to retrieve the top 5 transactions by amount per account type. 9. **A) Sequence Generator Transformation**\ *Explanation:* This transformation creates unique identifiers, such as sequential IDs, for each transaction. 10. **A) Joiner Transformation**\ *Explanation:* Joiner Transformation can combine data from different sources based on a common key like AccountNumber. **Case Study 3: Inventory Management System** 11. **A) Filter Transformation**\ *Explanation:* Filter Transformation selectively passes records based on conditions, suitable for filtering products below the reorder level. 12. **A) Aggregator Transformation**\ *Explanation:* Aggregator Transformation can calculate sums across groups, here summing QuantityOnHand across warehouses. 13. **A) Joiner Transformation**\ *Explanation:* Joiner Transformation merges data from multiple sources based on a key, useful for combining stock data from different warehouses. 14. **A) Expression Transformation**\ *Explanation:* Expression Transformation allows calculations and conditional expressions, such as categorizing stock levels. 15. **A) Sorter Transformation**\ *Explanation:* Sorter Transformation orders data by one or more fields, here sorting by QuantityOnHand. **Case Study 4: Customer Data Cleansing** 16. **A) Expression Transformation**\ *Explanation:* Expression Transformation can perform data validation checks, like validating phone numbers using regular expressions. 17. **B) Sorter Transformation**\ *Explanation:* Sorter Transformation with the Distinct option can remove duplicate records based on a unique field like CustomerID. 18. **A) Filter Transformation**\ *Explanation:* Filter Transformation filters out records based on specified conditions, here filtering records with missing Email or PhoneNumber. 19. **A) Router Transformation**\ *Explanation:* Router Transformation can direct records to different groups, useful here to log records with invalid data in an audit log. 20. **A) Lookup Transformation**\ *Explanation:* Lookup Transformation allows checking if a CustomerID exists in a reference table, returning data from it if a match is found. **Case Study 5: Employee Data Integration** 21. **A) Sorter Transformation**\ *Explanation:* Sorter Transformation with Distinct can remove duplicate EmployeeID values. 22. **A) Aggregator Transformation**\ *Explanation:* Aggregator Transformation calculates totals or other aggregates, here used for calculating total salary per department. 23. **A) Joiner Transformation**\ *Explanation:* Joiner Transformation merges data from different sources based on a key, such as EmployeeID. 24. **A) Sequence Generator Transformation**\ *Explanation:* Sequence Generator Transformation is ideal for creating unique IDs, such as a unique EmployeeID. 25. **A) Filter Transformation**\ *Explanation:* Filter Transformation allows filtering records based on conditions, useful for selecting employees with salaries above a threshold. **Additional Questions and Answers (26--50)** 26. **B) Sequence Generator Transformation**\ *Explanation:* This transformation generates unique sequential numbers, essential for primary keys or unique IDs. 27. **B) Router Transformation**\ *Explanation:* Router Transformation directs rows into different output groups based on multiple conditions, useful for routing customer data by region. 28. **A) Update Strategy Transformation**\ *Explanation:* Update Strategy Transformation enables you to specify if a row should be inserted, updated, deleted, or rejected. 29. **A) Sorter Transformation**\ *Explanation:* Sorter Transformation sorts data based on specified columns, here useful for ordering by salary. 30. **B) Aggregator Transformation**\ *Explanation:* Aggregator Transformation performs aggregate functions such as calculating averages. 31. **C) Union Transformation**\ *Explanation:* Union Transformation merges data from multiple pipelines or sources into a single pipeline. 32. **A) Rank Transformation**\ *Explanation:* Rank Transformation assigns a rank to rows based on values in a specified column, used for ranking by performance score. 33. **A) Router Transformation**\ *Explanation:* Router Transformation allows complex filtering by dividing data into multiple groups based on conditions. 34. **B) Lookup Transformation**\ *Explanation:* Lookup Transformation performs a lookup in a reference table to enrich data, such as adding customer details. 35. **A) XML Source Transformation**\ *Explanation:* XML Source Transformation reads structured XML data into Informatica mappings. 36. **A) Transaction Control Transformation**\ *Explanation:* Transaction Control Transformation sets commit and rollback points within a mapping, controlling transaction boundaries. 37. **B) File Source Transformation**\ *Explanation:* File Source Transformation reads unstructured or flat file data, mapping it into a structured format. 38. **A) Joiner Transformation**\ *Explanation:* Joiner Transformation combines data from two datasets based on a unique identifier, such as EmployeeID. 39. **A) Expression Transformation**\ *Explanation:* Expression Transformation allows for row-level calculations or business rules, like calculating discounts. 40. **A) Router Transformation**\ *Explanation:* Router Transformation can categorize data into different output groups, suitable for categorizing customers. 41. **A) Lookup Transformation**\ *Explanation:* Lookup Transformation can perform SQL overrides to retrieve specific data from relational tables. 42. **A) Filter Transformation**\ *Explanation:* Filter Transformation removes unwanted rows based on conditions, here filtering transactions by amount. 43. **B) Key Transformation**\ *Explanation:* Key Transformation allows key-value mapping for easy reference and lookup. 44. **A) Sorter Transformation**\ *Explanation:* Sorter Transformation can identify and eliminate duplicate rows based on a specified key. 45. **A) Stored Procedure Transformation**\ *Explanation:* Stored Procedure Transformation calls stored procedures in a database, useful for custom data processing. 46. **A) Union Transformation**\ *Explanation:* Union Transformation merges rows from multiple sources into a single dataset. 47. **A) Rank Transformation**\ *Explanation:* Rank Transformation helps in identifying the last or first records in groups, like last transaction per customer. 48. **A) Expression Transformation**\ *Explanation:* Expression Transformation handles data type conversions, such as String to Integer. 49. **A) Joiner Transformation**\ *Explanation:* Joiner Transformation efficiently handles joins of large datasets on a key, like CustomerID. 50. **A) Transaction Control Transformation**\ *Explanation:* Transaction Control Transformation manages source system transactions, allowing commit or rollback actions based on conditions.