Qlik Talend Data Quality Implementer (TDQCI) Certification Exam Sample Questions
Welcome! Preparing for the Qlik Talend Data Quality Implementer (TDQCI) certification exam can be a daunting task, but we're here to make it easier for you. Here are the sample questions that will help you become familiar with the Qlik TDQCI exam style and structure. We encourage you to try our Demo Qlik Talend Data Quality Implementer Certification Practice Exam to measure your understanding of the exam structure in an environment that simulates the actual test environment.
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Qlik TDQCI Sample Questions:
01. Cascade Manufacturing has just loaded a new supplier_contact table and wants a quick first check limited only to the phone_number field, looking at whether entries follow a consistent digit pattern and how many rows are blank, without comparing that field to anything else in the table.
Which type of data quality analysis is being described?
a) A column analysis focused on the phone_number field's value patterns and completeness.
b) A table analysis examining relationships between the phone_number field and the other fields in the same table.
c) A functional dependency analysis testing whether the supplier name determines the phone number.
d) A redundancy analysis identifying supplier records that describe the same company more than once.
02. A new data quality analyst at Lattice Telecom asks a colleague to explain what a redundancy analysis looks for when applied to a customer table.
Which explanation is accurate?
a) It looks for columns whose values are missing more often than a defined threshold allows.
b) It looks for differences between the customer table and a completely separate table holding the same customers after an overnight data load between systems.
c) It looks for rows that, despite differences in formatting or minor detail, actually describe the same real-world customer more than once.
d) It looks for values in one column that do not conform to an expected pattern, such as an invalid postal code.
03. Silverpine Logistics moved a shipment dataset from an operational system into a reporting warehouse. A reviewer wants to confirm that the values now in the warehouse still match what existed in the source system before the move, rather than simply assessing whether the warehouse dataset looks internally clean.
Which task does this reviewer's goal describe?
a) Consolidation, which merges duplicate records that Matching has already grouped together within one dataset
b) Data reconciliation, which compares data between a source and a target to verify accuracy after a move
c) Matching, which looks within a dataset for records that likely represent the same real-world entity
d) Profiling, which characterizes a single dataset's own condition without reference to any other system
04. Briarwood Insurance imports policyholder address data from several regional systems. Street types are recorded inconsistently — some rows spell out the full word while others use an abbreviation, and capitalization varies from row to row.
Which data quality component is intended to bring these values into one consistent form?
a) Matching, which looks for records across systems that likely represent the same policyholder
b) Profiling, which reports on how inconsistent the address values currently are across the imported files
c) Consolidation, which combines address records that have already been confirmed as duplicates
d) Standardization, which normalizes inconsistent representations of a value into a single common form
05. A data governance lead at Northfield Utilities is deciding which Talend Cloud application should be used specifically for tasks that require a human to review, curate, or arbitrate over questionable records before they are trusted.
Which application is designed for this purpose?
a) Data Preparation, which lets business users shape and transform raw data on their own
b) Talend Studio data quality components, which apply developer-authored cleansing logic rather than human review workflows
c) Data Stewardship, which is built for human review, arbitration, and validation of data issues
d) Data Inventory, which is built for discovering and assessing source data rather than routing records for human curation
06. Copperfield Bank needs to give a test team a copy of a production customer file, but sensitive values such as account holder names must not be exposed in the copy the test team receives.
Which kind of component is designed to replace such sensitive values before the file is shared?
a) A data privacy component such as tDataMasking, designed to obscure or replace sensitive values
b) A matching component, designed to compare account holder records against one another for likely duplicates
c) A consolidation component, designed to merge duplicate account holder records into a single surviving record
d) A profiling analysis, designed to summarize how many records contain a populated account holder name field
07. An analyst at Vantage Media applies several transformation steps to a raw dataset in Data Preparation, then saves the whole sequence so it can be reapplied later.
What has the analyst created?
a) A connection, because saving any set of steps against a dataset creates a new managed connection to that data source.
b) A semantic type definition, since the saved steps are automatically registered as a new data pattern for future datasets.
c) A resolution campaign, since any saved sequence of data changes is organized as a campaign in Talend Cloud.
d) A recipe — a saved sequence of preparation steps that can be reapplied to reshape data.
08. Marlowe Textiles has already used Data Inventory to assess its supplier data and Data Preparation to reshape it. Several records still disagree with each other and need a person to decide which value is correct before the data can be trusted.
Which stage of the workflow handles this remaining need?
a) Routing the records directly into a Talend Studio matching component and skipping any person-driven review
b) Routing the records into Data Stewardship, where a person can review and resolve the conflicting values
c) Sending the records back through Data Preparation to reshape the same values a second time
d) Sending the records back through Data Inventory a second time to reassess the same underlying values
09. A steward at Coral Reef Media wants to define a rule that will be used to assess data quality across the assets Data Stewardship manages, rather than performing a one-time manual correction.
What is this kind of definition called within Data Stewardship?
a) A data quality rule, defined so it can be applied to assess quality across the managed assets going forward
b) A connection type, which describes how a source system is reached rather than how quality is assessed
c) A campaign owner assignment, which designates who is responsible for a campaign rather than defining an assessment rule
d) A predefined role, which describes a person's permissions rather than a repeatable data assessment rule
10. Tidewater Logistics is designing an end-to-end cleansing pipeline for a newly onboarded shipment dataset. The team wants to diagnose the data, normalize its formatting, identify duplicate shipment records, and then produce one clean record per shipment, in that logical order.
Which sequence of components matches this intended order?
a) Consolidation, then Matching, then Profiling, then Standardization
b) Matching, then Profiling, then Standardization, then Consolidation
c) Profiling, then Standardization, then Matching, then Consolidation
d) Standardization, then Consolidation, then Profiling, then Matching
Answers:
| Question: 1 | Answer: a | Question: 2 | Answer: c |
| Question: 3 | Answer: b | Question: 4 | Answer: d |
| Question: 5 | Answer: c | Question: 6 | Answer: a |
| Question: 7 | Answer: d | Question: 8 | Answer: b |
| Question: 9 | Answer: a | Question: 10 | Answer: c |
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