Career upgrade: Learn practical AI skills for better jobs and higher pay.
Level up
PracticeVideosBlogFlashcardsEspañol
All Practice Exams

100+ Free Talend Big Data Developer Practice Questions

Pass your Talend Big Data Certified Developer using Talend Studio exam on the first try — instant access, no signup required.

✓ No registration✓ No credit card✓ No hidden fees✓ Start practicing immediately
100+ Questions
100% Free
1 / 100
Question 1
Score: 0/0

A Kafka topic has 6 partitions and the consumer group has 8 consumer instances. What happens?

A
B
C
D
to track
Same family resources

Explore More Talend Certifications

Continue into nearby exams from the same family. Each card keeps practice questions, study guides, flashcards, videos, and articles in one place.

Talend Data Integration Administrator

Practice Questions

Talend Data Integration Developer

Practice Questions

Talend Data Integration Solution Architect

Practice Questions
2026 Statistics

Key Facts: Talend Big Data Developer Exam

55

Questions

Talend Academy

90 min

Time Limit

Talend Academy

70%

Passing Score

Talend Academy

6 months

Recommended Experience

Talend Academy

3 plans

Learning Plans

Big Data Basics, Spark Batch, Spark Streaming

WebAssessor / Qlik Learning

Delivery

Talend Academy

As of May 2026, the Talend Academy page lists the Big Data Certified Developer exam as 55 questions, 90 minutes, and a 70% passing score. Talend recommends at least six months of hands-on experience plus working knowledge of Hadoop (HDFS, Hive, HBase, YARN), Spark, Kafka, and cloud storage. The exam aligns to the Big Data Basics, Big Data Spark Batch, and Big Data Spark Streaming learning plans. Because Talend is delivered through Qlik Learning, candidates should confirm pricing through Qlik Learning before scheduling.

Sample Talend Big Data Developer Practice Questions

Try these sample questions to test your Talend Big Data Developer exam readiness. Each question includes a detailed explanation. Start the interactive quiz above for the full 100+ question experience with AI tutoring.

1Which file system underlies most Talend Big Data Jobs that read or write to a Hadoop cluster?
A.HDFS (Hadoop Distributed File System)
B.NTFS
C.ZFS
D.S3 only
Explanation: HDFS is the foundational distributed file system in the Hadoop ecosystem and is the default storage layer Talend Studio targets through tHDFS* components. While S3 and ADLS are also supported, HDFS is the canonical Hadoop file system.
2In a YARN-managed Hadoop cluster, which component is responsible for arbitrating cluster resources between competing applications?
A.ResourceManager
B.NameNode
C.DataNode
D.JobTracker
Explanation: The YARN ResourceManager is the master daemon that tracks cluster capacity and schedules containers across NodeManagers. NameNode handles HDFS metadata, DataNode stores HDFS blocks, and JobTracker is the obsolete MapReduce v1 scheduler.
3Where in Talend Studio do you create a Hadoop cluster connection that can be reused across many Big Data Jobs?
A.Repository > Metadata > Hadoop Cluster
B.Window > Preferences > Talend > Hadoop
C.Run view > Advanced settings
D.Project Settings > Stats and Logs
Explanation: Hadoop cluster connections are centralized under Repository > Metadata > Hadoop Cluster. Once the cluster connection is defined there, you can derive HDFS, Hive, HBase, and other connection metadata that all Jobs reuse.
4Which Talend component opens a reusable HDFS connection so downstream tHDFS components can share it within the same Job?
A.tHDFSConnection
B.tHDFSPut
C.tHDFSInput
D.tHDFSList
Explanation: tHDFSConnection establishes a single, named connection to an HDFS NameNode that other tHDFS* components can attach to via the 'Use an existing connection' option. tHDFSPut, tHDFSInput, and tHDFSList all consume connections rather than opening shared ones.
5You need to copy a local CSV file from the Talend JobServer host into HDFS. Which component is the most direct fit?
A.tHDFSPut
B.tHDFSGet
C.tHDFSInput
D.tFileCopy
Explanation: tHDFSPut uploads local files to an HDFS target path, mirroring the 'hdfs dfs -put' CLI command. tHDFSGet does the reverse direction, tHDFSInput reads HDFS file contents row by row, and tFileCopy works only on local file systems.
6Which file format is generally the best fit for analytical Hive queries that scan only a few columns out of many?
A.Parquet
B.Plain text
C.JSON
D.XML
Explanation: Parquet is a columnar storage format with predicate pushdown and column pruning, so analytical queries that touch a subset of columns read far less data than row-oriented formats. ORC is a similar columnar option also widely used with Hive.
7Which component in a Standard Job iterates over file paths in an HDFS directory so downstream subjobs can process each file?
A.tHDFSList
B.tHDFSInput
C.tHDFSExist
D.tHDFSCompare
Explanation: tHDFSList enumerates files in an HDFS directory and exposes globals such as CURRENT_FILEPATH that an iterate-style trigger can feed into tHDFSInput or other components. tHDFSExist only checks if a single path exists, and tHDFSCompare compares two files.
8What is the main behavioral difference between an internal (managed) Hive table and an external Hive table?
A.Dropping an internal table also deletes the underlying HDFS data, while dropping an external table only removes the metadata
B.External tables cannot be queried with HiveQL
C.Internal tables cannot be partitioned
D.External tables always use ORC format
Explanation: When you DROP a managed/internal Hive table, both metadata and data files are deleted. For external tables, only the metadata is removed; the underlying HDFS files remain so they can be reused or shared with other engines.
9Which Talend component issues HiveQL DDL such as CREATE TABLE on the connected Hive metastore?
A.tHiveCreateTable
B.tHiveInput
C.tHiveLoad
D.tHiveRow
Explanation: tHiveCreateTable specifically wraps CREATE TABLE statements with options for partitioning, bucketing, format, and external locations. tHiveRow can also run arbitrary HiveQL but tHiveCreateTable is purpose-built for DDL with a UI for columns and partitions.
10A Hive table is partitioned by event_date. Which query benefits most from partition pruning?
A.SELECT * FROM events WHERE event_date = '2026-05-01'
B.SELECT COUNT(*) FROM events
C.SELECT * FROM events WHERE user_id = 42
D.SELECT DISTINCT event_type FROM events
Explanation: Filtering on the partition column lets Hive read only the matching partition directory and skip the rest of the table. Filters on non-partition columns or full scans force Hive to read every partition.

About the Talend Big Data Developer Exam

The Talend Big Data Certified Developer using Talend Studio credential validates the ability to design, build, debug, and deploy Talend Big Data Jobs against the Hadoop and Spark ecosystem. It covers Hadoop cluster metadata, HDFS, Hive, HBase, Spark Batch and Spark Streaming Jobs, Kafka, Kerberos-secured environments, and Spark on YARN tuning.

Assessment

55 multiple-choice questions

Time Limit

90 minutes

Passing Score

70%

Exam Fee

Contact Qlik Learning (Qlik (Talend))

Talend Big Data Developer Exam Content Outline

15%

Big Data Basics and Hadoop Ecosystem

Define Big Data and the Hadoop ecosystem (HDFS, YARN, Hive, HBase), differentiate Talend architecture from Big Data architecture, and place cloud storage and the Hadoop file system into context.

15%

Hadoop Cluster Metadata and Connections

Use Talend Repository to centralize Hadoop cluster connection metadata and derive HDFS, Hive, HBase, and YARN connections that every Big Data Job reuses.

15%

Hive and Hadoop Data Management

Read and write Hive tables (tHiveInput, tHiveOutput, tHiveLoad, tHiveCreateTable), handle partitions and buckets, and operate on HDFS and HBase data via tHDFS* and tHBase* components.

20%

Spark Batch Jobs

Design Spark Batch Jobs with tMap, tAggregateRow, tJoin, tFilterRow, and tSortRow; choose between broadcast and shuffle joins; understand partitioning, repartition vs coalesce, and Spark executor memory and core tuning.

20%

Spark Streaming and Kafka

Build Streaming Jobs with tKafkaInput, tKafkaOutput, and tKafkaCreateTopic; configure windowing (sliding vs tumbling), microbatch interval, checkpointing, watermarks, and Kafka consumer offset semantics.

15%

Big Data Environment Configuration

Configure Spark on YARN (client vs cluster deploy mode), set up Kerberos (krb5.conf, JAAS, principal/keytab) for secured clusters, and deploy Jobs via Talend Administration Center and JobServer or remote engines.

How to Pass the Talend Big Data Developer Exam

What You Need to Know

  • Passing score: 70%
  • Assessment: 55 multiple-choice questions
  • Time limit: 90 minutes
  • Exam fee: Contact Qlik Learning

Keys to Passing

  • Complete 500+ practice questions
  • Score 80%+ consistently before scheduling
  • Focus on highest-weighted sections
  • Use our AI tutor for tough concepts

Talend Big Data Developer Study Tips from Top Performers

1Master Hadoop cluster metadata: define one cluster connection in the Repository and derive HDFS, Hive, HBase, and YARN children rather than retyping cluster details per Job.
2Be precise about Spark Batch joins: when does Talend tMap broadcast a lookup, and when does it shuffle? Know spark.sql.autoBroadcastJoinThreshold and broadcast() hints.
3Practice Spark Streaming windowing: sliding vs tumbling windows, batch interval tuning, checkpointing, and watermarks for late events.
4Memorize Kafka basics that Talend exposes: partitions vs consumer groups, auto.offset.reset = earliest vs latest, manual commits with tKafkaCommit, and idempotent producers.
5Walk through Kerberos end to end: krb5.conf, JAAS, principal, keytab, kinit, klist, and how Talend surfaces these in the cluster connection or Spark configuration.
6Tune Spark on YARN deliberately: client vs cluster deploy mode, executor memory and cores, yarn.scheduler.maximum-allocation-mb, and shuffle partitions.

Frequently Asked Questions

How many questions are on the Talend Big Data Developer exam?

The Talend Academy page lists 55 questions with a 90 minute time limit. Candidates need 70% to pass and receive the Talend Big Data Certified Developer using Talend Studio badge on success.

What experience does Talend recommend before taking the exam?

Talend recommends at least six months of hands-on Talend product experience plus working knowledge of Hadoop (HDFS, Hive, HBase, YARN), Spark, Kafka, and cloud storage. Completing the Big Data Basics, Big Data Spark Batch, and Big Data Spark Streaming learning plans is the standard preparation path.

How much does the Talend Big Data Developer exam cost?

Since Talend certification is delivered through the Qlik Learning platform, the exam page does not publish a standalone price. Confirm pricing on Qlik Learning before scheduling, because pricing can vary by region and by whether the exam is bundled with training.

What topics matter most on the exam?

Expect heavy emphasis on Hadoop cluster metadata, HDFS/Hive/HBase components, Spark Batch Job design (tMap joins, broadcast vs shuffle, repartition vs coalesce), Spark Streaming with Kafka (windowing, checkpointing, watermarks), and Spark on YARN configuration. Kerberos configuration (krb5.conf, JAAS, principal/keytab) is a recurring environment topic.

Does the exam cover Talend Cloud or only Talend Studio?

The Big Data Certified Developer exam is a Talend Studio Big Data exam focused on Studio Job design and Spark execution. Talend Cloud Management Console topics are covered by separate Talend Cloud exams; this exam emphasizes the Big Data Studio components and Spark on YARN execution.

How long should I study for this exam?

Most engineers with Spark and Hadoop exposure need 40-60 hours over five to eight weeks. New developers often need additional lab time on Spark Batch, Streaming, and a Kerberos-secured cluster to be ready for the 55-question, 90-minute exam.