Free Talend Talend-Core-Developer Practice Exam with Questions & Answers

Comprehensive and Detailed Explanation:

To filter records based on a specific country dynamically, the best approach isusing a tDBInput component with a context variable in the SQL query (Option A).

Step-by-Step Process:

Define a Context Variable:

In theContext Variablessection of Talend Studio, create a new variable (context.country) and set its value dynamically.

Configure tDBInput:

Drag and drop the tDBInput component onto theDesigner.

Set up thedatabase connectionusing either Built-in or Repository mode.

In theQuery field, write:

SELECT id, name, address, country FROM customers WHERE country = '" + context.country + "'

Execute the Job:

The Job will retrieveonly those records where the country column matches the value of the context variable.

The value of context.country can be modified at runtime, making the querydynamic.

Why not other options?

tMatchGroup (Option B):Used for record deduplication, not filtering.

Run if Trigger (Option C):Controls execution flow but does not filter records inside tDBInput.

tFilterColumns (Option D):Removes unwanted columns but does not filter records based on conditions.

Comprehensive and Detailed Explanation:

The validity of a dataset in Talend's Trust Score™ framework is determined by:​

Number of valid and invalid values across the dataset sample (Option B):

This metric assesses the quality of the data by evaluating the proportion of valid entries compared to invalid ones. A higher number of valid values indicates better data quality.​

Use of semantic types across the dataset sample (Option C):

Semantic types help in understanding the meaning and context of data fields. Consistent and correct application of semantic types ensures that data is interpreted accurately, contributing to its validity.​

Why not other options?

Option A: User ratings and certification pertain to the popularity axis, reflecting user trust and endorsement, not the intrinsic validity of the data.​

Option D: The number of empty rows relates to the completeness axis, indicating missing data, rather than directly affecting validity.​

Trace Debug and Java Debug are two modes that allow you to debug your jobs in Talend Studio. Trace Debug mode allows you to trace each row processed by your job components and see the values of each column in each row. Java Debug mode allows you to debug your job code in Java or Perl and see the values of each variable or expression in your code.

The characteristics that distinguish Trace Debug from Java Debug are:

Trace Debug mode allows row-by-row inspection of data flows, while Java Debug mode does not. Data flows are the links that show the data transfer between components in your job design workspace. In Trace Debug mode, you can see the data flow on each trace and inspect the values of each column for each row processed by your job. You can also use filters to display only the rows that match a condition or an expression. In Java Debug mode, you cannot see the data flow on each trace or inspect the values of each column for each row.

Trace Debug mode supports breakpoints based on input data condition, while Java Debug mode does not. Breakpoints are points where the execution of your job pauses and waits for your action. You can use breakpoints to inspect your data or debug your logic at specific points of your job execution. In Trace Debug mode, you can set breakpoints on traces based on a condition or an expression that involves input data columns. For example, you can set a breakpoint to pause your job when a customer name contains a certain string or when a product price exceeds a certain value. In Java Debug mode, you can only set breakpoints on lines of code based on a condition or an expression that involves variables or expressions.

The characteristics that do not distinguish Trace Debug from Java Debug are:

Both modes require a separate perspective. A perspective is a set of views and editors that are arranged in a specific way to support a specific task. In Talend Studio, you can switch between different perspectives, such as Integration, Debug, Profiling, etc. To use Trace Debug mode, you need to switch to the Debug perspective by clicking on the Open Perspective button and selecting Debug. To use Java Debug mode, you need to switch to the Java perspective by clicking on the Open Perspective button and selecting Java.

Neither mode requires Java development experience. Although Java Debug mode allows you to debug your job code in Java or Perl, you do not need to have Java development experience to use it. You can use the graphical interface of Talend Studio to design your job components and properties without writing any code. You can also use the Code view to see the generated code of your job in Java or Perl and modify it if needed. However, you do not need to write any code from scratch or understand all the details of the code to use Java Debug mode. References: Talend Open Studio: Open-source ETL and Free Data Integration | Talend, [Trace Debug mode - 7.3], [Java Debug mode - 7.3]

To design a job that can run in two contexts, Test and Production, and run it as a standalone job outside Talend Studio, you need to do the following steps:

Define the context variables and values for each context in the Contexts tab of your job. A context variable is a variable that can store a value that can be changed at runtime or between different contexts. You can use context variables to parameterize the properties or expressions of your job components.

Build the job with the desired context selected in the Build Job window. You can access this option by right-clicking on your job in the Repository tree view and selecting Build Job. This will open a dialog box where you can configure the build settings, such as destination folder, archive name, context, etc. You need to select the context that you want to use for your job execution from the drop-down menu.

Extract the content of the archive file that contains your job executable files and libraries. The archive file also contains two executable files: a batch file (.bat) for Windows platforms and a shell script (.sh) for Linux platforms. You need to run the appropriate file for your platform by double-clicking on it or using a command line tool. This will launch the job and display its output in a console window.

If you need to run the job in a different context, you need to build a separate copy of the job with the other context selected in the Build Job window. You cannot change the context of an already built job without rebuilding it.

You do not need to build the job with the Context scripts option selected in the Build Job window, edit the script according to the context in which you want to run the job, set the desired context as the default, rebuild the job if you need to run it in a different context, or be prompted for the context. These methods are not correct or available in Talend Studio and may cause errors or unexpected results. References: Talend Open Studio: Open-source ETL and Free Data Integration | Talend, [Build Job - 7.3], [Contexts - 7.3]

Comprehensive and Detailed Explanation:

In Talend Cloud Data Inventory, when a user creates a dataset using a shared connection, the following applies:​

User B has full access to Dataset_1 (Option D):

Once User B creates Dataset_1 using the shared connection, they become the owner of that dataset. Even if User A later revokes access to the connection, User B retains full access to Dataset_1. The dataset's accessibility is independent of the connection's sharing status after its creation.​

Why not other options?

Option A:User B's access to Dataset_1 is not limited; they have full ownership and control.​

Option B:Revoking the connection does not remove User B's access to datasets they have already created.​

Option C:User A's access to Dataset_1 depends on the sharing settings applied by User B; by default, User A does not have access unless granted.​

To identify execution times to verify the effectiveness of your changes, you can use one of these methods:

Reading the time stamps from the execution console in the Run view. This method allows you to see the start and end time of each subjob and component in your job, as well as the total execution time of the job. You can also see the number of rows processed by each component and the status of the job (success or failure).

Observing the execution times that annotate the flows in the Designer. This method allows you to see the execution time of each flow (main, lookup, reject, etc.) between components in your job. You can also see the number of rows processed by each flow and the throughput (rows per second) of each flow.

You cannot use these methods to identify execution times:

Observing the execution time in the Code view. This method does not show you the execution time of your job or its components, but only the generated code of your job in Java or Perl. The Code view is useful for debugging or customizing your code, but not for measuring performance.

Comparing time stamps in Trace Debug mode. This method does not show you the execution time of your job or its components, but only the values of each column for each row processed by your job. The Trace Debug mode is useful for tracing data quality or transformation issues, but not for measuring performance. References: Talend Open Studio: Open-source ETL and Free Data Integration | Talend, [Run view - 7.3], [Designer - 7.3], [Code view - 7.3], [Trace Debug mode - 7.3]

Comprehensive and Detailed Explanation:

In Talend Pipeline Designer, theconfiguration panelprovides options for managing and modifying the settings of components within a pipeline.

Modify processor values (Option A):

Users can adjust values for processors such as filters, joins, transformations, and aggregations within the configuration panel.

This helps fine-tune data processing rules directly in the pipeline.

Display and update dataset connections (Option B):

The configuration panel allows users to change dataset connections, updating the source or target locations of data.

Users can modify database credentials, file paths, or API endpoints as needed.

Why not other options?

Option C:The ability to preview changes made by processors is handled in thepreview panel, not the configuration panel.

Option D:Pipeline export settings are managed separately in theexport settings menu, not in the configuration panel.

Comprehensive and Detailed Explanation:

After publishing a Job from Talend Studio to Talend Cloud, you can execute it using the Talend Management Console (TMC) by following these steps:​

Add a task and then run the task (Option A):

In TMC, navigate to theManagementsection and selectTasks.​

Click onAdd Task, choose the published artifact (your Job), configure necessary parameters, and save the task.​

Once the task is created, you can run it manually or schedule it for execution.​

Select the artifact name, add and configure a task, then run the task (Option D):

In TMC, go to theArtifactssection to view your published Jobs.​

Select the desired artifact and click onAdd Task.​

Configure the task settings as needed and save it.​

After creating the task, you can execute it immediately or set up a schedule.​

Why not other options?

Option B:TMC does not support directly running a Job by clicking on the artifact name without first creating a task.​

Option C:While you can select and configure an existing task, the initial step requires creating the task associated with the artifact.​

Comprehensive and Detailed Explanation:

In Talend Management Console (TMC), aplanis a sequence of tasks executed in a defined order. The types of tasks that can be added to a plan include:​

Jobs (Option D):Standard data integration tasks designed in Talend Studio.​

Big Data tasks (Option B):Tasks involving big data processing.​

Routes (Option C):Tasks related to messaging routes.​

However,Data services (Option A)refer to web services and are not typically added as tasks within a plan.​