Interview scenarios paper 1

 Informatica Scenarios in IICS (Informatica Intelligent Cloud Services):

1. Convert Single Row from Source to Three Rows in Target

• Source: Single row with multiple columns
• Target: Three rows, each with a subset of columns

Solution:

1. Use an Expression Transformation:

   • Create three output ports (e.g., Target1, Target2, Target3).
   • For each target port, write expressions to map the desired columns from the source.

2. Example: Source has columns A, B, C. You want Target1 to have A, Target2 to have B, and Target3 to have C.

   • Target1 expression: [Source.A]
   • Target2 expression: [Source.B]
   • Target3 expression: [Source.C]

2. Split Non-Key Columns to Separate Tables with Key Column in Both

• Source: Table with key and non-key columns
• Target: Two tables: one with key and all non-key columns, another with just the key

Solution:

1. Use a Router Transformation:
   • Define the key column(s) as the routing condition.
   • Configure one output port for each target table.

3. Separating Duplicate and Non-Duplicate Rows to Separate Tables

• Source: Table with potential duplicates
• Target: Two tables: one for duplicates, another for non-duplicates

Solution:

1. Use a Sorter Transformation:
   • Sort by the columns that might contain duplicates.
2. Use an Expression Transformation:
   • Create a flag variable to identify duplicates (e.g., IsDuplicate).
   • Set IsDuplicate to true for the first occurrence and false for subsequent occurrences within a group (use previous value ports).
3. Use a Router Transformation:
   • Route rows with IsDuplicate = true to the "Duplicates" target and others to the "Non-Duplicates" target.

4. Retrieving First and Last Record from a Table/File

• Source: Table or file
• Target: Two tables: one for the first record, another for the last

Solution:

1. Use a Sorter Transformation:
   • Sort by a relevant column (e.g., chronological order for a file).
2. Use a Filter Transformation:
   • Filter for the first record (row number = 1) and last record (row number = total rows).
3. Use a Router Transformation:
   • Route the first record to the "First Record" target and the last record to the "Last Record" target.

5. Remove Footer from Your File

• Source: File with footer lines
• Target: File without footer lines

Solution:

1. Use a Filter Transformation:
   • Use an expression to filter out rows based on footer criteria (e.g., pattern matching).

6. Remove Header from Your File

• Source: File with header line(s)
• Target: File without header lines

Solution: (Similar to Scenario 5)

1. Use a Filter Transformation:
   • Use an expression to filter out rows based on header criteria (e.g., pattern matching).

7. Sending First Half Records to Target

• Source: Table or file
• Target: Table or file with the first half of the data

Solution: (Assuming you know the total number of records)

1. Use an Expression Transformation:
   • Create a variable to store the total number of records (TotalRecords).
   • Divide TotalRecords by 2 to get the number of records in the first half (HalfCount).
   • Create another variable (RowNumber) using a Sequence Generator Transformation to keep track of the current row.
   • Filter the rows where RowNumber is less than or equal to HalfCount.

8. Sending Second Half Records to Target

• Source: Table or file
• Target: Table or file with the second half of the data

Solution: (Similar

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Continuing Informatica IICS Scenario Solutions:

Here's the continuation of the Informatica IICS scenarios with solutions, incorporating best practices and addressing potential issues:

9. Sending Alternate Records to Target

• Source: Table or file
• Target: Table or file with alternate records

Solution:

1. Use an Expression Transformation:
   • Create a variable (IsAlternate) to track whether the current record is alternate (true) or not (false).
   • Set IsAlternate to true for the first record and toggle it (true to false and vice versa) for each subsequent record.
2. Use a Filter Transformation:
   • Filter for rows where IsAlternate is true.

10. Separate the Original Records in Target

• Source: Table with potentially modified data
• Target: Table with original data from the source

Solution:

1. This scenario depends on how the data was modified. If you have a versioning system or log of changes, you might be able to extract the original data from there. Informatica IICS itself doesn't have a built-in mechanism to differentiate original records from modified ones unless those changes are explicitly marked.

11. Separate Rows on Group Basis

• Source: Table with groups identified by a column
• Target: Separate tables for each group

Solution:

1. Use a Router Transformation:
   • Define the group column as the routing condition.
   • Configure a separate output port for each target table (you can dynamically generate port names based on the group value using expressions).

12. Get Top 5 Records to Target without Using Rank

• Source: Table
• Target: Table with the top 5 records

Solution:

1. Use a Sorter Transformation:
   • Sort by the column you want to use for ranking (e.g., descending for highest values).
2. Use a Filter Transformation:
   • Limit the

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flag

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Continuing Informatica IICS Scenario Solutions:

15. Sending Records to Target Tables in Cyclic Order

• Source: Table or file
• Target: Multiple target tables (cyclic distribution)

Solution:

1. Use a Router Transformation with a custom expression for cyclic routing:

   Code snippet

   (iif(mod([RowNumber], num_targets) = 0, num_targets, mod([RowNumber], num_targets)))
   

   Use code with caution.
   • Replace num_targets with the actual number of target tables. This expression calculates the target table index based on the current row number (RowNumber) and the total number of target tables.

16. Concatenation of Duplicate Values by Comma Separation

• Source: Table with potential duplicates
• Target: Table with concatenated duplicate values separated by commas

Solution:

1. Use a Sorter Transformation:
   • Sort by the column(s) that might contain duplicates.
2. Use an Aggregator Transformation:
   • Group by the non-duplicate columns.
   • Use a CONCAT aggregation function (with a comma separator) on the duplicate column.

17. Target Table Rows, with Each Row as Sum of All Previous Rows from Source Table

• Source: Table with numeric values
• Target: Table with each row containing the sum of all previous rows in the source

Solution:

1. Use an Expression Transformation:
   • Create a variable (RunningTotal) with an initial value of 0.
   • In the expression for each target column, add the source value to RunningTotal and assign the result to the target column.
   • Update RunningTotal with the new sum for the next row.

18. Produce Files as Target with Dynamic Names

• Source: Table or file
• Target: Files with dynamic names based on source data

Solution:

1. Use an Expression Transformation:
   • Create an expression to generate the desired file name based on source data (e.g., concatenating columns or using a timestamp).
2. Use a Set Target File Properties Transformation:
   • Set the "Filename" property dynamically using the expression from step 1.

19. Validating All Mappings in Repository

• Functionality: Validate mappings for errors or inconsistencies

Solution:

Informatica IICS doesn't have a single "validate all mappings" functionality. However, you can achieve partial validation by:

• Using the lineage view to identify potential issues (e.g., missing source or target connections).
• Running individual mappings in test mode to identify technical errors during data transformation.

20. Using Mapping Parameter and Variable in Mapping

• Functionality: Define and use parameters and variables within a mapping

Solution:

1. Parameters:
   • Define parameters in the mapping properties for reusable values.
   • Use parameter references within expressions (e.g., [myParam]).
2. Variables:
   • Create variables within the Expression Transformation for temporary calculations.
   • Use variable names in expressions after assigning values.

**21. Removing '${}^{\prime }SymbolfromSalaryColumn\ast \ast -\ast \ast Source:\ast \ast Tablewitha"Salary"columncontainin{g}^{\prime }$' symbols

• Target: Table with the '${}^{\prime }symbolremovedfromthe"Salary"column\ast \ast Solution:\ast \ast 1.UseanExpressionTransformation:-Createanewtargetcolumnforthesalarywithoutthesymbol.-UsetheSUBSTRorREPLACEfunctiontoremoveth{e}^{\prime }$' symbol from the source salary value.

22. Currency Converter

• Source: Table with currency values
• Target: Table with converted currency values

Solution:

1. Use an Expression Transformation with a custom currency conversion expression or a built-in IICS function (if available). You might need additional information like source and target currencies and exchange rates.

23. Sending Data One After Another to Three Tables in Cyclic Order

• Source: Table or file
• Target: Three target tables with data in cyclic order

Solution: (Similar to Scenario 15)

1. Use a Router Transformation with a cyclic routing expression for three targets.

**24. Converting '${}^{\prime }Symbolt{o}^{\prime }RS{.}^{\prime }inSalaryColumn\ast \ast -\ast \ast Source:\ast \ast Tablewitha"Salary"columncontainin{g}^{\prime }$' symbols

• Target: Table with the '${}^{\prime }symbolreplacedb{y}^{\prime }RS{.}^{\prime }inthe"Salary"column\ast \ast Solution:\ast \ast (SimilartoScenario21)1.UseanExpressionTransformationwiththeREPLACEfunctiontosubstituteth{e}^{\prime }$' with 'RS.'.

25. Insert and Reject Records Using Update Strategy

• Functionality: Insert successful records and handle errors for rejected records

**Solution

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Interview question set 2

Level-1 (Mappings):

1. Difference between join and lookup transformation:

   • Join Transformation: Joins data from different sources based on a condition and combines the columns horizontally.
   • Lookup Transformation: Retrieves data from a relational table based on a lookup condition and can be either connected or unconnected.

2. What are the different types of lookup in CDI:

   • Connected Lookup: Connected directly to the pipeline and receives input from other transformations.
   • Unconnected Lookup: Not connected directly to the pipeline and called within an expression or another transformation.

3. Difference between Connected and Unconnected lookup:

   • Connected Lookup: Receives input directly from other transformations in the mapping.
   • Unconnected Lookup: Is not connected directly to the pipeline and must be called within an expression or another transformation.

4. What are the active and passive transformations:

   • Active Transformation: Alters the number of rows passing through it, such as Filter or Router.
   • Passive Transformation: Does not change the number of rows passing through it, such as Expression or Lookup.

5. What are the different methods to perform remove duplicates in CDI:

   • Sorter Transformation: Sorts the data and then removes duplicates using the "Distinct" option.
   • Aggregator Transformation: Groups data and removes duplicates using the "Group By" option.
   • Expression Transformation: Uses conditional logic to identify and remove duplicates.

6. What is indirect file load and how can we implement that in IICS:

   • Indirect file load is loading data from files whose file names or locations are stored in another file. In IICS, this can be implemented using parameter files or dynamic file names.

7. How will you read Source JSON file in IICS:

   • Use a JSON parser transformation to parse the JSON data and extract the required fields for further processing.

8. Describe Rank, Aggregator, Normalizer transformation:

   • Rank Transformation: Ranks data based on a specified condition.
   • Aggregator Transformation: Performs aggregate calculations like SUM, AVG, etc., on groups of data.
   • Normalizer Transformation: Converts multiple columns of data into multiple rows or vice versa.

9. IIF vs Decode function in the expression:

   • IIF Function: Evaluates a condition and returns one value if the condition is true and another if it's false.
   • Decode Function: Compares a value to a list of values and returns the corresponding result.

10. Router vs filter in IICS:

    • Router Transformation: Routes rows of data to different outputs based on conditions.
    • Filter Transformation: Filters rows of data based on a condition and passes only the rows that meet the condition.

11. How to reset sequence generator when we migrate from DEV to QA:

    • Change the starting value of the sequence generator to ensure it starts from the desired value in the QA environment.

12. Union vs File list:

    • Union Transformation: Combines data from multiple pipelines into a single pipeline.
    • File List: Lists files in a directory and provides metadata about those files.

13. What is SQL override and Lookup Override:

    • SQL Override: Allows you to provide custom SQL code for a Source Qualifier transformation to override the default query generated by the mapping.
    • Lookup Override: Allows you to provide custom SQL code for a Lookup transformation to override the default lookup query.

14. How to execute UNIX/Power shell/python commands in IICS Mapping:

    • Use a Command task within the mapping to execute UNIX/PowerShell/Python commands.

15. What is the biggest mapping you handled as a developer? (SCD TYPE 2):

    • Provide details about a specific mapping, its purpose, the number of transformations used, and any challenges faced during development.

16. Data cache and index cache in Join transformer:

    • Data Cache: Stores data rows temporarily during the mapping execution.
    • Index Cache: Stores index information for quick access during join operations.

17. Hierarchical parser vs structural parser:

    • Hierarchical Parser: Parses hierarchical data formats like XML or JSON.
    • Structural Parser: Parses flat file or positional file formats.

18. Types of parameters in mapping (input and INOUT parameters) and its usage:

    • Input Parameters: Passed into the mapping from outside the mapping.
    • INOUT Parameters: Passed into the mapping and can be modified within the mapping.

19. SUBSTR,INSTR,ERROR,LKP,DATE functions:

    • SUBSTR: Returns a substring from a string.
    • INSTR: Returns the position of a substring within a string.
    • ERROR: Returns the error message associated with the last error encountered.
    • LKP: Performs a lookup operation to retrieve data from a source.
    • DATE: Manipulates date values.

20. What is gcid in the normalizer:

    • The GCID (Group Continuation Identifier) in the normalizer transformation identifies continuation records in a grouped set of data.

21. SCD Type 1,2,3:

    • Type 1: Overwrites the existing dimension with new data.
    • Type 2: Maintains historical data by creating new records for changes.
    • Type 3: Stores partial history by adding new columns to track changes.

22. Mapping level Performance tuning:

    • Optimize SQL queries, use partitioning, reduce transformations, utilize pushdown optimization, and optimize session properties.

23. Web service consumer transformation:

    • Allows you to call a web service and process the response within the mapping.

24. Fatal and nonfatal errors:

    • Fatal Error: Stops the session immediately when encountered.
    • Nonfatal Error: Allows the session to continue processing despite encountering errors.

25. Exception handling and user-defined errors:

    • Use error handling strategies such as error logging, error handling transformations, and custom error messages.

26. Types of caches:

    • Data Cache
    • Index Cache
    • Static Cache
    • Dynamic Cache
    • Persistent Cache
    • Re-cache (Refill Cache)
    • Shared Cache

27. How to call Stored Procedure in IICS:

    • Use the Stored Procedure transformation to call stored procedures within a mapping.

28. How to call unconnected lookup object in IICS:

    • Use an expression transformation to call an unconnected lookup within the mapping.

29. How to return multiple values from unconnected lookup:

    • Use multiple output ports in the unconnected lookup transformation to return multiple values.

30. What is incremental load, what are the different approaches to implement that:

    • Incremental load is the process of loading only new or changed data since the last load. Approaches include using flags, timestamps, or change data capture (CDC).

31. Difference between Upsert and data driven:

    • Upsert: Updates existing records and inserts new records based on a key.
    • Data Driven: Determines the data flow dynamically based on conditions

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32. In join transformation, which object will be master and which object will be details, based on what metrics we decide that?

    • In a join transformation, the master object is typically the object with fewer unique records, and the detail object is the one with more unique records. This decision is based on the cardinality between the two datasets.

33. How to convert Rows into columns in IICS?

    • You can use the Pivot transformation in IICS to convert rows into columns. The Pivot transformation aggregates data based on a key and then transposes the aggregated values from rows into columns.

34. Difference between REST V2 connection and Web service consumer:

    • REST V2 Connection: Provides a connection to a RESTful web service API. It is more flexible and can handle complex APIs with various authentication methods and payload formats.
    • Web Service Consumer: A transformation used within a mapping to call a SOAP-based web service. It is specifically designed for consuming SOAP services and supports WSDL-based interactions.

35. How to create business service and how to use it in IICS:

    • To create a business service in IICS, you define it within the Administration Console by specifying the service type, endpoint, authentication details, etc. Once created, you can use it in mappings by adding a service task and configuring it to use the business service.

36. How to pass multiple rows to input request for web service call:

    • You can pass multiple rows to the input request of a web service call by using a mapping that generates XML/JSON data containing all the rows, and then passing this data to the web service using the web service consumer transformation.

37. How to do decrypt and encrypt the PGP encrypted source flat file:

    • To decrypt a PGP encrypted source flat file in IICS, you can use the PGP Decryptor transformation. To encrypt a flat file, you can use the PGP Encryptor transformation. These transformations provide options to specify keys, passphrases, etc., for encryption and decryption.

38. How to copy/move/remove files from one folder to another folder using file processor connection:

    • You can use the File Transfer task in IICS to copy, move, or remove files between folders using a file processor connection. Simply configure the task with the appropriate action (copy, move, delete), source and target folders, and file filter criteria.

39. Can we move the file to SFTP location using IICS connections?

    • Yes, you can move files to an SFTP location using IICS connections. You would typically use a File Transfer task with an SFTP connection to transfer the files. You'll need to configure the task with the source and target locations, along with any necessary authentication details.

40. Can we use a command in source instead of file list?

    • Yes, you can use the Command task within a mapping to execute a command that generates a list of files, and then pass this list to subsequent mapping components. This can be useful when you need to dynamically generate the list of files to process based on certain conditions or criteria.

Level-2 (Mapping Tasks, Synchronization, Replication, Mass Ingestion):

1. How do you implement performance tuning in Informatica mapping Tasks?

   • Performance tuning in Informatica mapping tasks involves optimizing SQL queries, utilizing appropriate indexes, partitioning data, minimizing transformations, and enabling pushdown optimization.

2. Error Handling mechanism in data integration:

   • Error handling mechanisms include configuring error handling transformations, defining error handling rules, logging errors to appropriate tables or files, and retrying failed operations based on specific criteria.

3. What is the mapping task?

   • A mapping task is a unit of work in Informatica that represents a data flow operation. It includes mappings, workflows, and other components needed to execute the data integration process.

4. How to schedule the Mapping?

   • You can schedule a mapping using the Informatica Scheduler or a third-party scheduler by defining the schedule parameters such as frequency, start time, recurrence, etc., in the workflow properties.

5. What is the blackout period in the schedule?

   • A blackout period is a designated time frame during which scheduled tasks are temporarily suspended or disabled to prevent them from running. This is often done for maintenance or system updates.

6. What is a parameter file and how do you use it in the mapping?

   • A parameter file is a text file containing parameters and their corresponding values used to configure mappings, workflows, and sessions. You can use it in the mapping by referencing the parameter file path and parameters within the mapping.

7. How to enable verbose mode in Informatica data integration?

   • You can enable verbose mode in Informatica by configuring the session properties and setting the session log level to verbose. This will generate detailed logs with additional information for troubleshooting and debugging purposes.

8. What is cross-schema pushdown optimization?

   • Cross-schema pushdown optimization is a performance optimization technique in Informatica that allows SQL queries to be pushed down to the source or target databases across different database schemas, improving query performance by reducing data movement.

9. Tell me below advanced session properties:

   • Rollback transactions on error: Specifies whether to rollback transactions when an error occurs during session execution.
   • Commit on the end of file: Specifies whether to commit transactions at the end of each file.
   • Recovery Strategy: Defines the recovery mechanism to use in case of session failure.
   • DTM process: Data Transformation Manager process responsible for executing the session.
   • Incremental Aggregation: Enables incremental aggregation to process only changed data since the last session run.
   • Pushdown Optimization: Determines

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whether to push transformation logic to the database for processing.

• Session Retry on deadlock: Specifies whether to retry the session in case of deadlock errors.
• Stop on error: Determines whether to stop the session immediately upon encountering an error.

10. Difference between Linear task flow and Taskflow:

    • Linear Task Flow: Represents a simple sequence of tasks executed one after another in a linear manner.
    • Taskflow: Provides a more flexible and complex orchestration capability by allowing branching, conditional execution, parallel processing, and looping.

11. Limitations of Data synchronization task:

    • Some limitations of the Data Synchronization task in IICS include its inability to handle complex transformations, limited support for custom error handling, and difficulty in managing dependencies between tasks.

12. Use of Replication task:

    • The Replication task in IICS is used to replicate data between source and target systems, ensuring consistency and synchronization between databases. It supports both full and incremental data replication strategies.

13. What is incremental load, full load, and initial load:

    • Incremental Load: Loads only new or changed data since the last load, reducing processing time and resources.
    • Full Load: Loads all data from the source regardless of whether it has changed since the last load.
    • Initial Load: The first load of data from the source to the target, often performed to populate the target database initially.

14. How to perform upsert in Informatica mapping and required constraints to implement:

    • Upsert (Update or Insert) operations can be performed in Informatica mappings using the Update Strategy transformation or by using the MERGE statement in SQL overrides. Constraints such as unique keys or primary keys are required to determine whether to update existing records or insert new ones.

15. How to run Pre and Post SQL commands, Pre and Post Processing commands:

    • Pre and Post SQL commands can be configured in session properties to execute SQL commands before and after the session runs. Pre and Post Processing commands can be specified in workflow properties to execute shell commands or scripts before and after workflow execution.

16. Difference between Synchronization vs Replication Task:

    • Synchronization Task: Compares data between source and target systems and synchronizes changes bidirectionally to ensure consistency.
    • Replication Task: Copies data from a source to a target system, often used for data warehousing or disaster recovery purposes.

17. Different types of mass ingestion tasks:

    • Mass ingestion tasks in IICS include tasks such as file-based ingestion, database ingestion, and cloud-based ingestion. These tasks are designed to efficiently load large volumes of data into the target system.

18. What is Data Masking:

    • Data Masking is a technique used to obfuscate sensitive data in non-production environments to protect privacy and comply with regulations. It involves replacing sensitive data with realistic but fictitious data to preserve the data's format and characteristics.

19. How to configure maplets in IICS:

    • Maplets are reusable sets of transformations that can be configured and reused across multiple mappings. To configure a maplet, you define its input/output ports, add transformations, and configure their properties within the maplet designer.

20. Use of control table in ETL:

    • Control tables are used in ETL processes to track metadata, manage job execution, and maintain job dependencies. They often contain information such as job status, start/end times, error messages, and dependencies between tasks.

21. Explain the below components in task flow:

    • Data task: Represents a unit of work within a task flow, such as data integration, replication, or synchronization.
    • Output variables: Variables that store output values from a task and can be used as input for subsequent tasks.
    • Sub-data task: A nested task within a task flow used to encapsulate and execute a sequence of tasks.
    • Custom error handling: Custom error handling logic defined within a task flow to handle errors and exceptions.
    • Email notifications: Notifications sent via email to notify users about task flow status, completion, or errors.
    • Command task: Executes external commands or scripts as part of the task flow execution.

22. How to call CAI process in DI job:

    • You can call a CAI (Cloud Application Integration) process in a DI (Data Integration) job by using the Process Task in the task flow and configuring it to invoke the desired CAI process.

23. How to read parameter file values into MCT:

    • You can read parameter file values into MCT (Mapping Configuration Task) by passing the parameter file path and parameters as input variables to the MCT, and then using these variables within the mapping.

24. How to execute python /Unix /Powershell script using Command task (windows Secure agent):

    • You can execute Python, Unix, or PowerShell scripts using the Command task in IICS by configuring the task with the appropriate command and script file path, and specifying any required arguments or options.

25. How to execute multiple mapping task instances simultaneously:

    • You can execute multiple mapping task instances simultaneously by configuring a workflow with multiple

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mapping tasks and setting the concurrency level to run them concurrently. This can be achieved by configuring the workflow properties and defining the dependencies between tasks.

26. What is the use of Stop on Error property in MCT:

    • The "Stop on Error" property in MCT (Mapping Configuration Task) determines whether the task flow execution should stop immediately upon encountering an error within the MCT. If enabled, the task flow will terminate upon error; if disabled, the execution may continue depending on other error handling configurations.

27. What is the use of email notification option in MCT?

    • The email notification option in MCT allows you to configure email notifications to be sent based on specific conditions or events within the task flow. You can define recipients, email subject, message body, and trigger conditions to notify stakeholders about task flow status, completion, or errors.

28. How to use fixed-width delimited file in source:

    • To use a fixed-width delimited file as a source in IICS, you can define the column widths and data format in the source properties. Specify the start and end positions for each column to parse the data correctly during ingestion.

29. How to create Business service and its use case:

    • To create a business service in IICS, you define it within the Administration Console by specifying the service type, endpoint, authentication details, etc. Business services are used to interact with external systems, such as REST APIs, SOAP services, databases, etc., and facilitate data integration processes.

30. What is the use of hierarchical schema, can we create without schema:

    • Hierarchical schema defines the structure of hierarchical data formats, such as XML or JSON, to parse and process data correctly. While it's possible to parse hierarchical data without a schema, defining a schema provides structure and ensures data integrity during processing.

31. How to send an email using notification step in taskflow:

    • To send an email using a notification step in a task flow, you configure the task with the email recipients, subject, message body, and trigger conditions. The notification step will send an email based on the specified criteria, such as task completion, success, failure, etc.

32. Can we send output response to Task flow:

    • Yes, you can send the output response to a task flow by configuring output variables in the task properties. These output variables capture the task's output or result, which can be used as input for subsequent tasks or for conditional branching within the task flow.

33. How to send variables data to mapping columns in task flow:

    • You can send variable data to mapping columns in a task flow by mapping the variables to the mapping parameters or using them directly in expressions within the mapping. This allows you to pass dynamic data from the task flow to the mapping for processing.

34. How to get values from mapping columns to task flow variables?

    • You can retrieve values from mapping columns to task flow variables by capturing the output of the mapping task in output variables and then mapping these variables to task flow variables. Alternatively, you can use expressions or functions to assign mapping column values to task flow variables.

35. How to implement custom error handling in task flow:

    • Custom error handling in a task flow can be implemented by using conditional branches, decision tasks, and error handling tasks. You can define conditions based on task outcomes, error codes, or other criteria, and configure actions to handle errors accordingly, such as retrying tasks, sending notifications, or executing alternate paths.

36. How to do audit logging in IICS with data task output response variables?

    • You can perform audit logging in IICS with data task output response variables by capturing relevant task information, such as task name, status, start/end times, and output response variables, and writing this information to a designated audit log table or file using SQL commands or stored procedures executed in the post-processing phase of the task.

37. How to Trigger the task flow based on file event:

    • You can trigger a task flow based on a file event by configuring a File Listener task in the task flow and specifying the file path, event type (e.g., file creation, modification), and trigger conditions. When the specified file event occurs, the File Listener task will automatically trigger the associated task flow for execution.

38. How to create file listener and how to trigger task flow:

    • To create a file listener in IICS, you define it within the task flow by adding a File Listener task and configuring its properties, such as file path, event type, trigger conditions, and associated task flow. Once configured, the file listener will monitor the specified file or directory for the specified event and trigger the associated task flow accordingly.

39. When do we use file event and when do we use schedule?

    • File event triggers are used when you want to initiate a task flow based on specific file-related events, such as file creation, modification, or deletion. Schedule triggers, on the other hand, are used when you want to execute a task flow at predefined time intervals or at specific times of the day, regardless of file-related events.

40. How can we trigger IICS task flow using a third-party scheduler?

    • You can trigger an IICS task flow using a third-party scheduler by exposing the task flow as a web service or API endpoint within IICS and then invoking this endpoint from the third-party scheduler using HTTP requests or other communication protocols. Alternatively, you can use IICS connectors or adapters to integrate with the third-party scheduler and automate the task flow execution.

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41. What is include dependency check in assets export:

    • Include dependency check in assets export is a feature that ensures all dependent objects associated with the selected assets are included during export. This helps maintain the integrity of the exported assets by including any related mappings, connections, or other dependencies required for proper functionality.

42. Best practices for IICS code migrations (export and import):

    • Some best practices for IICS code migrations include:
      • Perform thorough testing before migrating code to ensure functionality is preserved.
      • Document dependencies and ensure they are included during export.
      • Use version control to track changes and rollback if necessary.
      • Follow a structured deployment process with proper approvals and validation.
      • Communicate changes to stakeholders and provide training as needed.

43. How to implement versioning in IICS:

    • Versioning in IICS can be implemented by utilizing the built-in version control features provided by the platform. You can create different versions of assets such as mappings, workflows, or task flows, and track changes over time. Version control allows you to revert to previous versions if needed and facilitates collaboration among team members.

44. What is asset level permissions and how to use that (ACL):

    • Asset level permissions in IICS determine the level of access and actions users or groups can perform on specific assets such as folders, mappings, or connections. Access Control Lists (ACLs) define permissions based on roles or users and can be configured to restrict or grant access to view, edit, execute, or delete assets.

45. Different types of semi-structured data and how to read in IICS:

    • Semi-structured data includes formats like XML, JSON, and CSV, which have a flexible structure but may not conform to a rigid schema. In IICS, you can read semi-structured data using specialized connectors or parsers tailored to each format. For example, you can use XML or JSON parser transformations to parse and extract data from XML or JSON files, respectively. Additionally, you can utilize flat file or hierarchical parsers to handle CSV or positional file formats. These parsers allow you to define the structure of the data and extract relevant information for further processing in the integration workflows.