Which statements are true when comparing a Joblet to a tRunJob component?
Choose 3 answers
Answer : B, D, E
A Joblet is a reusable piece of a job that can be used in multiple jobs as a single component. A tRunJob component is a component that allows you to call another job as a subjob within a parent job. When comparing a Joblet to a tRunJob component, these statements are true:
A Joblet uses the same context variables of the job in which it is used, unlike a tRunJob component. A context variable is a variable that can store a value that can be changed at runtime or between different contexts. A Joblet inherits the context variables from the job that contains it and does not have its own context variables. A tRunJob component can pass context variables from the parent job to the child job, or use a specific context for the child job.
Building a Joblet typically requires the use of generic input and trigger components. A Joblet can have one or more input and output flows that connect it with other components in a job. To create these flows, you need to use generic input and trigger components, such as tJobletInput, tJobletOutput, tJobletTriggerInput, and tJobletTriggerOutput. These components allow you to define schemas and triggers for your Joblet without depending on specific components.
The nested job called by a tRunJob component cannot use the same context variables of the job in which it is used. A nested job is a job that is called by another job using a tRunJob component. A nested job can have its own context variables or receive context variables from its parent job, but it cannot use the same context variables as its parent job. This means that if you have two context variables with the same name in both jobs, they will be treated as separate variables and will not share values.
These statements are false when comparing a Joblet to a tRunJob component:
The performance of tRunJob component is better than running an equivalent job using a Joblet. The performance of a Joblet is better than running an equivalent job using a tRunJob component. This is because a Joblet is integrated into the main code of the job and does not require launching another JVM process or loading another metadata object like a tRunJob component does. Reference: Talend Open Studio: Open-source ETL and Free Data Integration | Talend, [Joblets - 7.3], [tRunJob properties - 7.3], [Contexts - 7.3]
You are building a complex Job and want to explore different options for optimizing execution times using parallelism.
How can you identify execution times to verify the effectiveness of your changes?
Choose 2 answers
Answer : B, D
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. Reference: 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]
Which options can you use to add a Joblet to your talend Job?
Choose 3 answers
Answer : B, D, E
To add a Joblet to your Talend Job, you can use one of these options:
Type the Joblet name on the Studio canvas, then select it from the Palette drop-down menu. This will create a Joblet container on your canvas that contains all the components and links of your Joblet.
Drag the Joblet from Repository tree view to designer canvas. This will also create a Joblet container on your canvas that contains all components and links of your Joblet.
Drag Joblet from Palette to design workspace. This will open a dialog box where you can select an existing Joblet from Repository or create a new one.
You cannot use a tRunJob component and select Joblet from drop-down menu, nor right-click Joblet from Palette and select Add option. These methods are not available in Talend Studio and may cause errors or unexpected results. Reference: Talend Open Studio: Open-source ETL and Free Data Integration | Talend, [Joblets - 7.3]
You need a list of all customers whose first name contains "Tom" and who are older than 18. Which processor should be used?
Answer : C
Comprehensive and Detailed Explanation:
To filter customer records based on first name containing 'Tom' and age greater than 18, the Filter processor is the correct choice.
Filter (Option C) -- Correct Answer:
The Filter processor allows users to set conditional rules to extract only the required data.
Users can specify conditions such as:
first_name CONTAINS 'Tom'
AND
age > 18
This ensures that only relevant records are included in the output.
Why not other options?
Option A (Join): Used to combine data from multiple datasets based on a key field, not for filtering.
Option B (Aggregate): Used for summarizing data, such as calculating counts, sums, or averages.
Option D (Data Sampling): Used to select a random subset of data, not for filtering based on conditions.
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