Qlik Talend Data Integration certification exams are designed to be challenging to ensure that you have the skills to successfully implement quality projects. Preparation is critical to passing.
This certification exam covers the Talend Big Data Basics, Talend Big Data Advanced – Spark Batch, and Talend Big Data Advanced – Spark Streaming learning plans. The emphasis is on the Talend and Big Data architectures, Hadoop ecosystems, Spark, Spark on YARN, Kafka, and Kerberos.
Certification Exam details
Duration: 90 minutes
Number of questions: 55
Passing score: 70%
Exam content is updated periodically. The number and difficulty of questions may change. The passing score is adjusted to maintain a consistent standard.
Recommended experience
At least six months of experience using Talend products General knowledge of Hadoop (HDFS, Hive, HBase, YARN), Spark, Kafka, Talend Big Data and cloud storage architectures, and Spark Universal Experience with Talend Big Data solutions and Talend Studio, including metadata creation, configuration, and troubleshooting
Preparation
To prepare for this certification exam, Qlik recommends:
Taking the Big Data Basics, Big Data Advanced - Spark Batch, and Big Data Advanced - Spark Streaming learning paths Studying the training material in the Talend Big Data Certified Developer preparation training module Reading the product documentation:
You should be comfortable using the Qlik Talend Help Center to search for documents that correspond with the exam topics Reading Qlik Community knowledge base articles
For more information about the recommended learning plans, go to the Qlik Learning Catalog.
Badge
After passing this certification exam, you are awarded the Talend Big Data Certified Developer using Talend Studio badge. To learn more about the criteria to earn this badge, refer to the Qlik Badging program page.
Certification Exam topics
Defining Big Data
Define Big Data Describe the Hadoop ecosystem Differentiate between Talend architecture and Big Data architecture Describe cloud storage architecture in a Big Data context
Configuring a Big Data environment
Manage Kerberos and security Manage Apache Knox security with Cloudera Data Platform (CDP)
Managing metadata in a Big Data environment
Manage Talend metadata stored in the repository Describe the main elements of a Hadoop cluster metadata Create Hadoop cluster metadata Create metadata connections to HBase, HDFS, YARN, and Hive
Managing data on Hadoop and cloud
Describe the principle usage of Hadoop (HDFS, HBase, and Hive) and cloud technologies Export and import big data files to HDFS Export and import big data files to the cloud Export data to an HBase table
Managing data using Hive
Import data to a Hive table Process data stored in a Hive table Analyze Hive tables in the Profiling perspective Manage Hive tables on Hive Warehouse Connector with CDP public cloud
Manage Big Data Jobs
Differentiate between Big Data Batch and Big Data Streaming Jobs Migrate and convert Jobs in a Big Data environment
Managing Spark in a Big Data Environment
Describe the principle usage of Spark Manage Spark Universal, including modes, environments, and distributions Configure Spark Batch and Streaming Jobs Troubleshoot Spark Jobs Optimize Spark Jobs at runtime
Managing a Spark cluster
Define Spark on YARN Describe the principle usage of YARN Manage YARN, including client and cluster Monitor Big Data Job executions Use Studio to configure resource requests to YARN
Streaming with Talend Big Data
Describe principle usage of Kafka Use Kafka components in Streaming Jobs Manage Big Data Streaming Jobs in Studio Tuning Streaming Jobs, including windowing, caching, and checkpointing
