Talend Talend-Core-Developer - Talend Core Certified Developer Exam

Comprehensive and Detailed Explanation:

The tRESTRequest component in Talend Studio is designed to handle RESTful web service requests. It supports the following HTTP methods:​

GET: Retrieves data from the server.​

POST: Submits data to the server, often causing a change in state or side effects.​

PUT: Replaces all current representations of the target resource with the request payload.​

PATCH: Applies partial modifications to a resource.​

DELETE: Removes the specified resource from the server.​

Therefore, the correct answer isA. GET, POST, PUT, PATCH, and DELETE.​

Comprehensive and Detailed Explanation:

In Talend Cloud Data Preparation, users can view and interact with datasets through various interfaces:​

Dataset overview (Option C):

Provides a general summary of the dataset, including metadata, quality metrics, and other pertinent information.​

Dataset patterns (Option D):

Displays recurring patterns within the dataset, helping users understand data distributions and identify anomalies.​

Dataset sample (Option E):

Allows users to view a sample of the dataset's records, facilitating data exploration and preparation tasks.​

Why not other options?

Option A:"Data preparation overview" is not a standard feature or terminology within Talend Cloud Data Preparation.​

Option B:"Dataset properties" typically refers to metadata and configuration settings, not the data content itself.​

In a tMap component, naming an output row correctly helps in managing data flow efficiently. The correct methods are:

A. Click the name of the table in the Map Editor window and edit it.

Open tMap, locate the output table, and click its name to edit it directly.

B. Assign the name when defining a new output table in the Map Editor window.

When adding a new output table, you can name it immediately in the Map Editor.

D. Assign the name when connecting a new output component.

When you connect an output component to tMap, you can assign a custom row name.

Comprehensive and Detailed Explanation:

After enabling the Observability Metric Collector in Talend's configuration file (org.talend.ipaas.rt.observability.cfg), it's essential to set additional parameters to control the behavior of metric collection:​

Frequency (Option C):Determines how often metrics are collected. Setting this ensures that data is gathered at appropriate intervals for monitoring needs.​

Lifetime (Option D):Specifies how long the collected metrics are retained. This helps manage storage and ensures that outdated metrics are purged.​

Configuring these parameters ensures that the metric collection aligns with organizational requirements for monitoring and performance analysis.​

Comprehensive and Detailed Explanation:

The defaultREST endpoint port for tRESTRequest in Talend Studio is 8090.

Port 8090 (Correct Answer - Option D):

When running a REST service insideTalend Studio, the default listening port is8090.

This can be changed in the component settings if required.

Other port options explained:

5040 & 5070: Not default ports used for Talend REST services.

8040: Used inTalend Runtime, not in Studio.

Comprehensive and Detailed Explanation:

The tMap component in Talend allows for various data transformation and mapping operations. The correct operations include:

A. Map data using filters, constraints, and simple explicit joins.

tMap allows mapping fields between sources and destinations with filters and constraints.

B. Perform full outer joins.

Unlike tJoin, tMap supports full outer joins, allowing all records from both tables to be included.

C. Transform data from single or multiple sources to single or multiple destinations.

You can map multiple input sources to multiple output targets with transformations.

To call a different job within a job you are developing, you can use the tRunJob component. This component allows you to execute another job as a subjob within a parent job. To pass the parameters to the job you want to call, you can use the context parameters. Context parameters are variables that can store values that can be changed at runtime or between different contexts. You can define context parameters in the Contexts tab of your job and assign them values for each context. You can also pass context parameters from the parent job to the child job by using the Context Param tab of the tRunJob component. This way, you can parameterize the properties or expressions of the child job with the values from the parent job.

You do not need to use a file, command line options, or Java function parameters to pass parameters to a different job. These methods are not supported by Talend Studio and may cause errors or unexpected results. References: Talend Open Studio: Open-source ETL and Free Data Integration | Talend, [tRunJob properties - 7.3], [Contexts - 7.3]

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:

To enhance the Trust Scoreâ„¢ of a dataset:

Improve the quality of the dataset using Pipeline Designer (Option A):

Utilizing Talend's Pipeline Designer allows you to cleanse, transform, and enrich your data, thereby improving its quality and, consequently, its Trust Score™.​

Add ratings to the dataset (Option D):

Encouraging users to rate the dataset increases its popularity metric, positively impacting the overall Trust Score™.​

Why not other options?

Option B: The Trust Score™ is automatically calculated based on specific metrics; it cannot be manually altered.​

Option C: While using the dataset in ETL jobs indicates its utility, it does not directly influence the Trust Score™ unless it affects metrics like usage or popularity.​