QSDA or QSBA, which exam is yours?

QSDA is the data architect exam and QSBA is the business analyst one. The first tests how you get data into Qlik Sense and shape it; the second tests what you build on top once it is there. They are separate credentials for separate jobs, and picking the wrong one is the most expensive mistake available here.

The clean test is where your work happens. If you spend your time in the data load editor, writing scripts, joining tables, deciding what the model should look like and why a chart is slow, QSDA is the exam that describes you. If you spend it in the app, choosing visualisations, building sheets and working with business users on what they want to see, the business analyst route fits better.

There is overlap in one place, and the blueprint puts it first. Twenty percent of QSDA is identifying requirements, which includes working out what business users actually need, who the stakeholders are, and what granularity the numbers should sit at. A data architect who cannot do that builds technically sound models that answer nobody’s question, so Qlik tests it before it tests any script.

Qlik lists both credentials, and several others, on its certifications overview. Read the role description rather than the title before booking anything.

What are the QSDA exam facts?

QSDA is 50 questions in 90 minutes at a 62 percent pass mark, priced at 250 USD, delivered as a proctored multiple choice exam you sit remotely. Five topics are published, all of them weighted, and the exam is platform-neutral across client-managed Qlik Sense and Qlik Cloud.

One number in that table is worth stating carefully. Some exam catalogues list this paper at 120 minutes. Qlik’s own exam details page gives 90, and that is the figure used throughout here. The difference is not academic: 50 questions in 90 minutes is 108 seconds each rather than 144, which is enough to change how you pace a scenario-heavy paper.

Qlik is also unusually open about how the pass mark works. It states that exam content is updated periodically, that the number and difficulty of questions may change, and that the passing score is adjusted to maintain a consistent standard. So 62 percent is not a fixed bar you can calibrate against forever; it is the current expression of a constant standard, and the standard is what you are actually preparing for.

Price is the one field Qlik does not publish on that page, so treat 250 USD as the catalogue figure and confirm it when you book.

Where does the exam weight actually sit?

In the script and the model. Data transformations is 38 percent and data model design is 28, which is 66 percent of the paper in two topics. Identify requirements takes 20 percent, data connectivity 8 and validation 6. The distribution is published by both the exam catalogue and Qlik, and the two agree exactly.

Read those numbers against how people usually prepare and the mismatch is obvious. Candidates tend to revise connectors, because connectors are concrete and easy to list. Connectivity is 8 percent, roughly four questions. Meanwhile the load script, which is harder to revise because it is a skill rather than a list, is nearly two fifths of the paper.

The practical instruction is to spend your time in proportion. If you have ten study sessions, six or seven belong in the script and the model, two in requirements, and the remaining one across connectivity and validation together.

What does the 38 percent transformations topic cover?

Everything that happens between a raw source and a usable model. Six objectives: building data content to requirements, handling nulls and blanks so filtering behaves, documenting load scripts, date handling, script organisation and cleansing, and the variables needed for incremental loading in the extract layer.

Incremental loading is the one to take seriously. It is named explicitly, it sits in the extract layer, and it is production work rather than exam theory: you are being asked how to load only what has changed since the last run, which means understanding what drives the decision and which variables carry the state. Anyone who has only ever done full reloads on small datasets will find this unfamiliar.

Null and blank handling earns its place for a subtler reason. The objective ties it to filtering, not to correctness, and that connection is the insight. A null that is technically harmless in the data can make a selection behave in a way a user reads as a bug, so the exam wants you to reason about the consequence in the application rather than about the value in the field.

Two objectives are about discipline rather than technique: documenting load scripts, and script organisation and cleansing. They look like filler and are not. A script nobody can follow is a maintenance liability, and Qlik has chosen to examine that directly. Qlik’s load script documentation is the reference if your habits were learned on the job rather than from the manual.

What does data model design ask for?

Four objectives across 28 percent: determining measures and attributes from each source, identifying the appropriate type of data model, optimising the model for Qlik Sense specifically, and implementing data structures efficiently. It is the judgement half of the exam, where transformations is the execution half.

The phrase doing the work is “appropriate type of data model”. You are expected to choose, with reasons, rather than to apply one shape everywhere. That means recognising when a star schema is right, when a snowflake is acceptable, when to denormalise for speed, and when the answer is to split a model rather than stretch one.

“Optimised for Qlik Sense” is deliberate too. Generic warehouse modelling instincts do not transfer cleanly, because Qlik’s associative engine rewards different choices from a relational query engine: synthetic keys and circular references are problems here that would be unremarkable elsewhere, and column cardinality affects memory in ways that matter at load time.

Requirements feed straight into this, which is why the blueprint puts them first. Granularity, aggregation level and the need for slowly changing dimensions are all named in the 20 percent requirements topic, and each is a decision that constrains the model before you write a line of script. Recognising that need early is exactly what the exam is testing.

The three light topics, and why light is not skippable

Requirements at 20 percent, connectivity at 8 and validation at 6 make up the remaining third. Only two of them are genuinely small, and treating any of them as optional is how a candidate with strong scripting skills still fails.

Requirements is not light at all, despite sitting outside the two heavy topics. Twenty percent is roughly ten questions, and its objectives are the ones least improved by technical practice: identifying stakeholders from a scenario, settling metrics and levels of granularity, and determining the appropriate level of security. These are consulting questions, and they reward having sat in the meeting rather than having read about it.

Connectivity at 8 percent is genuinely small, and it is also the easiest topic to secure. Two objectives, both concrete: which sources and connectors a situation needs, and the right way to create those connections. A single focused session covers it, and it would be careless to lose four questions to something that finite.

Validation at 6 percent is the smallest topic on the paper and the one most often left to chance. Two objectives, testing scripts and testing data, and they are worth understanding as separate activities: a script can run perfectly and still produce a model that is wrong. Three questions is not many, but at a 62 percent pass mark the margin is thin enough that three is worth having.

What does Qlik expect you to have built already?

Production applications, not practice ones. Qlik publishes a recommended experience list, and it reads as a description of a working data architect rather than a study prerequisite, which tells you how the questions will be framed.

• Practical experience developing multiple production-quality Qlik Sense applications
• Ability to write Qlik Sense load scripts and validate data
• A basic understanding of extract, transform and load as a discipline
• Creating and using connectors to various data sources
• Understanding the QVD layer and the architecture of the Qlik platform
• Ability to architect data to provide optimal performance
• Familiarity with SQL and relational databases

The QVD layer entry is the one to check yourself against honestly. QVD files are Qlik’s own storage format and the layered extract, transform and load pattern built on them is how serious Qlik estates are organised. If you have never worked in a layered architecture, the incremental loading material in the transformations topic will feel abstract, because it exists to serve exactly that pattern.

The SQL entry matters for a different reason. Much of what a Qlik data architect does is decide what to push back to the source and what to do in the script, and that decision needs enough SQL to know which is cheaper. The exam is platform-neutral across client-managed and cloud deployments, so nothing here depends on where your Qlik sits. A related view of how Qlik frames a specialist credential appears in the QAIS specialist guide.

How should you prepare?

Work in the script, in proportion to the weightings, on a model you did not build. Reading about transformations does not prepare you for a paper where nearly two fifths of the questions describe a situation and ask which method is correct.

1. Split your study time to match the weights, with roughly two thirds of it in transformations and model design.
2. Build one layered extract, transform and load setup with a QVD layer, even a small one, so the architecture is something you have assembled.
3. Implement incremental loading end to end and work out for yourself which variables carry the state between runs.
4. Take a model somebody else built and find its synthetic keys and circular references, because diagnosing is closer to the exam than designing.
5. Practise the requirements objectives out loud: given a scenario, name the stakeholders, the granularity and the security level.
6. Spend one session on connectivity and one on validation, which is enough for 14 percent of the paper.
7. Work a set of QSDA sample questions to calibrate how the script material is actually asked.

If you are coming from the application side, invert the emphasis and expect the adjustment to take longer than you plan. The front-end instincts that make a good sheet designer do not help with a slow model, and the exam is almost entirely about the second problem. An older look at the same credential is available in the site’s earlier data architect overview, useful for the role framing rather than the current figures.

Frequently Asked Questions

How many questions are on the QSDA exam?

Fifty questions in 90 minutes, which is about 108 seconds each.

What is the passing score for QSDA?

Sixty two percent. Qlik states that it adjusts the passing score as content and question difficulty change, so that the standard stays consistent.

How long is the exam, 90 or 120 minutes?

Ninety minutes. Some catalogues list 120, but Qlik’s own exam details page gives 90, and that is the figure to plan against.

What is the difference between QSDA and QSBA?

QSDA is the data architect exam, covering the load script and the data model. QSBA is the business analyst exam, covering what gets built in the application once the data is there.

What are the five topics and their weightings?

Identify requirements at 20 percent, data connectivity at 8, data model design at 28, data transformations at 38, and validation at 6.

Does the exam cover Qlik Cloud or client-managed Qlik Sense?

Both. Qlik states the exam is platform-neutral, so the content applies to either deployment.

How much does QSDA cost?

Two hundred and fifty US dollars as listed. Qlik does not publish a price on its exam details page, so confirm the figure when you book.

Is the exam proctored?

Yes. It is sat remotely with the machine locked down and webcam supervision throughout.

Do I need SQL for this exam?

Qlik lists familiarity with SQL and relational databases as recommended experience. You are not tested on writing SQL, but the modelling decisions assume you understand what the source can do.

What do I get for passing?

The Qlik Sense Data Architect certification and a digital badge issued through Credly.

Conclusion

QSDA is the script and model exam. Fifty questions in 90 minutes, 62 percent to pass, 250 USD, and five topics in which transformations and model design take 66 percent between them.

Prepare in that proportion. Incremental loading, null handling for filtering, script discipline and choosing a model type for Qlik’s engine rather than for a relational one are where the questions live, and none of them yields to revision without a keyboard. Requirements at 20 percent is the topic experience helps with most and study helps with least.

Check the duration before you plan your pacing, since the catalogue figure and Qlik’s own differ, and remember that the 62 percent bar moves with the paper by design. Then work the two heavy topics until the script is something you write rather than something you recognise.

What is the Talend administrator certification?

The Talend administrator certification is Qlik’s credential for people who install, configure and operate a Talend Data Integration platform rather than build Jobs on it. It carries the exam code TDICA, awards the Talend Data Integration Certified Administrator badge, and covers seven topic areas spanning system architecture, the administration console, deployment, monitoring and version control.

The scope is the platform itself. Where a developer credential asks how you transform data, this one asks how the environment that runs the transformation is set up: which module talks to which, where users and roles live, how a Job reaches an execution server, and how you find out when something has stopped working.

That framing matters because it is easy to assume a Talend certification is a Talend Studio certification. This one barely touches Studio as a development tool. It touches Studio as a client that has to talk to a server, which is a different subject entirely.

Administrator or developer: which Talend exam are you sitting?

Qlik runs several Talend credentials, and the two that get confused are the administrator exam and the developer exam. TDICA is for the person who runs the platform. The Data Integration Developer credential is for the person who builds Jobs inside Talend Studio. They share a product, a vendor and a naming convention, and almost nothing else.

The syllabus makes the split visible. TDICA’s System Architecture topic asks you to define the developer’s role and the purpose of each module, which is a description of someone else’s job rather than your own. From there the paper moves to the administration database, authentication methods, users, roles, groups, project authorisations, and how to apply a patch. None of those are things a Job developer does.

Here is the practical test. If your week is spent deciding which component transforms which field, the developer exam is yours. If your week is spent answering why someone else’s Job did not fire at three in the morning, this is yours. Working a set of exam-style items is the fastest way to confirm it, and the sample sets on the money site’s TDICA practice exam make the administrative slant obvious within a handful of questions.

Getting this wrong is a $250 mistake plus the study time, which is reason enough to spend ten minutes on the distinction before booking.

How is the TDICA exam delivered and scored?

TDICA is 50 questions in 60 minutes with a 70 percent pass mark, priced at $250 USD and registered through Qlik. Qlik publishes the question count, duration and pass mark on its own exam-details page, and those figures match the money site’s syllabus exactly. Passing awards the Talend Data Integration Certified Administrator badge.

Fifty questions in 60 minutes is 72 seconds each, which is brisk. It is the same per-question budget as a 75 item, 90 minute paper, but compressed into a single hour, so there is less room to recover if you stall early.

Seventy percent of 50 questions is 35 correct answers and a margin of 15. With seven topics and no published weightings, that margin is the only real guidance on how thin you can afford to be anywhere, and it is not much.

Who can actually book this exam?

Qlik states on its own exam page that the certification is available to Customers, Partners and Employees. That is an eligibility condition rather than a recommendation, and it is not stated on the money site’s syllabus page, so it is easy to miss until you try to register.

For most people reading about this exam it will not be a barrier, because anyone operating a Talend platform is by definition working for a customer or a partner. It does mean that someone studying Talend independently, with no organisational relationship, should confirm their access before building a study plan around it.

Qlik also publishes an experience expectation: a minimum of six months working with Talend products, plus knowledge of data integration architecture including advanced features such as parallelization, and hands-on exposure to installation, configuration, project management, user administration, deployment strategies and troubleshooting. That list reads almost exactly like the syllabus, which is a good sign that the exam is written to the experience rather than to a course.

The full statement sits on Qlik’s Talend administrator exam details page, which is the authoritative source for eligibility and is worth reading before you plan anything else.

What does the syllabus cover?

Seven topic areas, published as names and objectives with no percentage weightings attached to any of them. The distribution of objectives, however, is anything but even, and that is the most useful planning signal available in the absence of formal weights.

Counted that way the paper has a clear centre of gravity. Talend Administration Center holds seven of the twenty listed objectives, Deployment holds four, System Architecture three, and the remaining three topics hold six between them. A study plan that follows the objective count rather than the topic count will be aimed at the right place.

Two topics are genuinely small and should be treated as such. Data Integration concepts is one objective: define data integration. Talend Studio configuration is also one: explain how the console and the client communicate. Neither justifies a week, and both are the kind of thing you can settle in an hour. The one objective under it asks you to define data integration, which in practice means being able to place Talend against the extract transform load pattern rather than reciting a definition.

Why does Talend Administration Center dominate the paper?

Talend Administration Center is the web console from which a Talend platform is run, and it holds more exam objectives than any other topic because it is where nearly every administrative decision is actually made. Its seven objectives cover the admin database, the configuration page, authentication, users and roles and groups, project authorisations, Git repository setup, and patching.

The identity half

Three of the seven objectives are about who can do what: authentication methods, then users, roles and groups, then projects and authorizations. Those are separate objectives for a reason, because they are separate layers. Authentication decides how someone proves who they are. Roles and groups decide what kind of user they are. Project authorisations decide which work they can touch.

Questions in this area tend to describe a requirement and ask which layer solves it, so knowing the boundaries between the three is worth more than memorising any single screen.

The platform half

The other objectives are about keeping the console itself healthy: creating and configuring the administration database, working the configuration page, and applying a patch. Patching is the one candidates most often have never done, because in many organisations it happens once a year and somebody else does it.

The Talend Administration Center user guide is the reference for all of this, and it is one of the six documents the money site’s syllabus page names as recommended reading. It is worth working through rather than searching, because the exam’s objectives follow its structure closely.

What does the deployment topic actually ask for?

Deployment is the second largest topic with four objectives, and it describes the path a Job takes from being written to being run on a schedule. Configure a JobServer, configure a JobServer cluster, deploy a Job through the conductor, add triggers, and build an execution plan.

The objective wording contains a distinction worth noticing. Deploying a Job in the conductor is specified as either a compiled Job or an Artifact repository Job. Those are two different delivery models with different implications for how builds are stored and promoted, and a question that names one of them is testing whether you know the difference rather than whether you can click deploy.

Execution plans are the piece that turns individual Jobs into a pipeline. A trigger fires one thing. A plan sequences several, with dependencies between them, which is how a real nightly load is actually assembled. If you have only ever scheduled single Jobs, this is the objective to spend lab time on.

JobServer clustering is the availability story. A single JobServer is a single point of failure for every scheduled Job on the platform, and the syllabus lists the cluster as its own configuration task rather than as an advanced footnote.

Git appears on the syllabus twice, and that is deliberate

Git turns up in two separate places: once as an objective inside Talend Administration Center, where you configure the repository, and once as its own topic, where you configure a repository and use Git features from within Talend Studio. That duplication is not sloppy drafting. It is the two ends of the same connection.

On the console side, the repository is a platform setting: where project source lives, and how the server reaches it. On the Studio side, it is a developer workflow: branching, committing and pulling from inside the client. An administrator has to set up the first so that the second works, which is why the syllabus tests both.

This is also the clearest place where the administrator role touches software delivery practice rather than infrastructure. Version control for integration projects is a governance question as much as a technical one, and the discipline around it is set out in frameworks like the DAMA body of knowledge, which treats data integration as a managed function rather than a set of scripts.

Anyone who has prepared for another Talend credential will recognise the pattern of a syllabus that lists areas without weighting them. The Talend API Services Developer exam is laid out the same way, and the same planning response applies: count objectives, not topics.

How should you prepare for TDICA?

Preparation for TDICA should follow the objective count rather than the topic list, which means Talend Administration Center gets the largest share and Deployment the second. Qlik’s own recommended route is the Talend Administration Center Essentials learning path plus a dedicated preparation module, and six months of hands-on platform work underneath it.

1. Confirm your eligibility first, since Qlik makes this certification available to customers, partners and employees rather than to the open public, and there is no point planning around an exam you cannot register for.
2. Measure your experience against Qlik’s stated minimum of six months with Talend products, and be honest about whether you have actually administered a platform or only used one.
3. Work the Talend Administration Center Essentials learning path, because it is the route Qlik names and the syllabus follows its shape closely.
4. Build the identity layers separately in a lab, configuring an authentication method, then users and roles and groups, then project authorisations, so the three stay distinct in your head rather than merging into one idea.
5. Stand up a JobServer and then a JobServer cluster, and deploy the same Job both as a compiled Job and through the artifact repository so the two delivery models are experiences rather than definitions.
6. Build an execution plan with real dependencies between Jobs, not just a trigger on a single Job, since the plan is the objective that separates scheduling from orchestration.
7. Configure Git from the console and then use it from Talend Studio, so the two halves of the version control topic connect.
8. Read the Talend Administration Center User Guide end to end rather than searching it, and finish with timed sets at 50 questions in 60 minutes so the 72 second pace is familiar.

Three to five weeks is realistic for someone already running a Talend platform. Someone who has only used Talend Studio should expect longer, because most of this exam is about a console they may rarely open.

If you are building a wider Qlik profile rather than a purely Talend one, the vendor’s analytics-side credentials follow a different shape entirely, and the Qlik AI Specialist exam is a useful comparison for how much the syllabus style varies across the portfolio.

Frequently Asked Questions

How many questions are on the TDICA exam?

Fifty questions in 60 minutes, which is about 72 seconds each. Qlik publishes the same figures on its own exam-details page as the money site’s syllabus does.

What is the passing score for the Talend administrator certification?

Seventy percent, meaning 35 correct answers out of 50 and a margin of 15. Qlik does not publish a scaled score for this exam.

How much does TDICA cost?

$250 USD, registered through Qlik. The price is published on the money site’s syllabus page rather than on Qlik’s exam-details page.

What is the difference between TDICA and the Talend developer certification?

TDICA is about running the platform: the administration console, identity, deployment, monitoring and version control setup. The developer credential is about building Jobs in Talend Studio. They share a product and very little else.

Are the TDICA topics weighted?

No percentages are published for any of the seven topics. The objective counts are the next best signal: Talend Administration Center holds seven of the twenty listed objectives and Deployment holds four.

Can anyone take this exam?

Qlik states that the certification is available to Customers, Partners and Employees. Anyone operating a Talend platform in an organisation will normally qualify, but an independent learner should confirm access before planning around it.

How much experience does Qlik recommend?

A minimum of six months working with Talend products, plus knowledge of data integration architecture including advanced features such as parallelization, and hands-on exposure to installation, configuration, project management, user administration, deployment and troubleshooting.

Does the exam cover Talend Cloud?

The published syllabus is built around Talend Administration Center, the on-premises console, together with Talend Studio, JobServers and the conductor. A candidate whose estate is cloud only should know that before booking.

Is there a prerequisite certification?

None is published. Qlik sets an experience expectation rather than a certification prerequisite, and there is no required exam to sit first.

Why does Git appear twice on the syllabus?

Once as a console setting inside Talend Administration Center, where the repository is configured for the platform, and once as its own topic covering Git features used from within Talend Studio. They are the two ends of the same connection.

Conclusion

TDICA is a platform administration exam wearing a data integration name: 50 questions, 60 minutes, $250, a 70 percent bar, and seven topics whose objective counts tell you far more than their names do. Talend Administration Center holds seven of the twenty objectives, Deployment four, and three topics between them hold six.

Decide first whether you are the person who builds Jobs or the person who keeps them running, because that single question picks the exam for you. Then check your eligibility, count objectives rather than topics when you plan, and spend the bulk of your lab time in the console and on the deployment path where the paper actually lives.

What Does the Qlik QAIS Exam Cover?

Qlik QAIS, the Qlik AI Specialist certification, is a 50-question, 90-minute exam requiring 73 percent to pass, priced at $250 USD. It covers five weighted domains: Introduction to Artificial Intelligence (30%), Business Applications for AI (30%), Fundamentals of Qlik Answers (15%), Fundamentals of Qlik Machine Learning (15%), and Fundamentals of Insight Advisor (10%). Candidates who build the data models those AI features read from can pair it with the QSDA data modelling exam.

Why the 73% threshold matters

The 73 percent threshold is high: you can afford roughly thirteen wrong answers out of fifty. Combined with 108 seconds per question, this is an exam that gives you time to think but little room to guess. Qlik administration credentials follow a different preparation rhythm, as the Talend platform administration exam shows.

Read that table carefully before planning. The two conceptual domains carry 60 percent between them and are largely vendor-neutral: candidates who prepare by clicking through Qlik features are optimising for the smaller half of the exam. Official details, including the 90 minute duration, are on the Qlik exam details page.

Introduction to AI Is 30%: What Must You Know?

The AI introduction domain covers the subsets of artificial intelligence, including generative AI and machine learning, and the ways humans communicate with AI systems. At 30 percent it is joint-largest, and it tests conceptual clarity rather than mathematics.

How AI, ML and generative AI nest

The nesting relationship is the foundation and appears repeatedly. Artificial intelligence is the broad field; machine learning is a subset in which systems learn patterns from data rather than following explicit rules; deep learning is a subset of machine learning using multi-layer neural networks; generative AI is a category of application producing new content. Getting these levels the wrong way round is a reliable way to lose marks.

The three learning paradigms

Within machine learning, know the three learning paradigms. Supervised learning trains on labelled examples to predict a known target. Unsupervised learning finds structure in unlabelled data, as in clustering. Reinforcement learning learns through reward signals from interaction. Questions describe a problem and expect you to identify which paradigm fits.

• Supervised: labelled training data, predicts a defined target
• Unsupervised: no labels, discovers structure such as clusters or segments
• Reinforcement: learns from reward and penalty through interaction
• Generative: produces new content rather than classifying existing content

Communication with AI systems means prompting, and the exam treats it as a skill with structure rather than an art. Understand that context, explicit instruction, and specified output format all materially change results, and that vague prompts produce vague output regardless of model quality.

Explaining hallucination

Hallucination is the limitation you must be able to explain. A language model generates plausible continuations rather than retrieving verified facts, so it can produce confident, fluent, entirely incorrect statements. That single property explains why retrieval grounding exists, which is exactly where the Qlik Answers domain picks up.

How Does the Exam Test Business Applications of AI?

Business Applications for AI is the other 30 percent domain, covering use cases for generative AI and machine learning alongside the LLM application project lifecycle. It tests whether you can match a business problem to the appropriate AI approach, and recognise when AI is the wrong answer.

Predictive versus generative use cases

The core discrimination is between predictive and generative use cases. Predictive machine learning suits questions with a defined answer derivable from historical data: which customers will churn, which transactions are fraudulent, what demand will be next quarter. Generative AI suits producing content or synthesising information: drafting a summary, answering a question from documents, generating a description.

The category trap

Applying the wrong category is the trap the exam sets most often. Using a language model to forecast numeric demand is a category error: it may produce a plausible-looking number with no predictive basis. Conversely, a classification model cannot draft a narrative explanation.

Feasibility assessment

Feasibility assessment is the second theme. Machine learning requires sufficient historical data with the outcome recorded, and a problem where a useful pattern plausibly exists. The exam expects you to recognise when data volume, label quality, or the absence of a stable relationship makes a proposed use case unviable, and to say so rather than proceeding.

Value framing

Value framing rounds out the domain. A model producing accurate predictions nobody acts on delivers nothing, so use case selection should consider whether a decision actually changes as a result. Questions frequently present competing proposals and ask which delivers business value rather than which is technically most interesting.

What Is the LLM Application Project Lifecycle?

The LLM application project lifecycle is named explicitly in the syllabus and describes how a language model application moves from framing to production: define the use case, select a model, adapt it, evaluate it, and deploy with monitoring. The exam tests the sequence and the decisions at each stage.

Model selection

Model selection is the first substantive decision. Larger models are more capable and more expensive per request; smaller models may be entirely sufficient for constrained tasks. The reasoning the exam wants is fitness for the specific task rather than maximum capability.

Prompting, RAG, and fine-tuning

Adaptation is where most candidates need clarity, because three approaches are commonly confused. Prompt engineering changes how you ask, with no change to the model: cheapest and fastest. Retrieval augmentation supplies relevant external content at query time so answers are grounded in your data. Fine-tuning modifies model weights through additional training, most expensive, and warranted when you need changed behaviour or style rather than changed knowledge.

Which adaptation to choose

The examinable heuristic: if the problem is that the model lacks your organisation’s information, retrieval augmentation is the answer, not fine-tuning. Fine-tuning teaches behaviour, not facts: a distinction the exam draws deliberately because it is so commonly muddled in practice.

Evaluation and monitoring

Evaluation and monitoring close the lifecycle. Language model output is not verifiable by a simple accuracy metric, so evaluation combines automated checks with human review, and monitoring must continue after deployment because both usage patterns and underlying data change.

How Does Qlik Answers Use Retrieval Augmented Generation?

Fundamentals of Qlik Answers is worth 15 percent and centres on retrieval augmented generation: the technique that grounds language model responses in an organisation’s own documents rather than relying on what the model absorbed during training.

The four-step RAG process

The RAG process is the examinable mechanism and follows four steps. Source documents are broken into chunks and converted into vector embeddings stored in an index. At query time the question is embedded the same way, semantically similar chunks are retrieved, and those chunks are supplied to the language model as context alongside the question.

Why grounding matters

Two consequences follow, and both are reliable question material. First, answers are grounded in retrieved content, which substantially reduces hallucination because the model is summarising supplied material rather than generating from memory. Second, answer quality depends on retrieval quality: if the relevant chunk is not retrieved, the model cannot use it, and no amount of model capability compensates.

Chunk size and configuration

That second point drives the practical configuration decisions. Chunk size matters: chunks too small lose surrounding context, chunks too large dilute relevance and waste the context window. Source curation matters equally: indexing outdated or contradictory documents produces confidently wrong grounded answers, which are arguably worse than ungrounded ones because they carry an implicit citation.

Permissions in retrieval

Know that permissions must carry through to retrieval. A user should not receive an answer synthesised from documents they are not entitled to read, which means access control has to apply at the retrieval layer rather than only at the document store. Product capabilities are described on the Qlik Answers product page.

What Does the Qlik Machine Learning Domain Require?

Fundamentals of Qlik Machine Learning is worth 15 percent and covers AutoML foundations and workflow: the process of building a predictive model without writing code. It tests understanding of the workflow and the decisions within it rather than algorithm implementation.

The AutoML proposition

The AutoML proposition is that the platform handles algorithm selection, hyperparameter tuning, and comparison automatically, leaving the practitioner to frame the problem, prepare the data, interpret the results, and decide whether the model is fit for use. The exam expects you to know which parts remain human responsibility, because those are the parts AutoML cannot automate away.

The AutoML workflow

The workflow runs from dataset preparation through target selection, training, evaluation, and deployment for prediction. Target selection is where problems most often originate: choosing a target that leaks information about itself produces a model with excellent apparent accuracy and no real predictive power. Data leakage is a likely exam concept.

Choosing the right metric

Model evaluation requires knowing which metric suits which problem. Accuracy misleads on imbalanced data: a model predicting the majority class every time scores well and predicts nothing useful. Precision, recall, and their balance matter more, and which one you optimise depends on whether false positives or false negatives cost more in the business context.

Feature importance

Feature importance is the interpretability tool worth understanding. It shows which inputs most influenced predictions, which supports both trust and debugging: a feature that dominates unexpectedly is often a leak rather than an insight. Details of the capability appear on the Qlik AutoML product page.

Why Does Insight Advisor Emphasise Prompting?

Fundamentals of Insight Advisor is the smallest domain at 10 percent, roughly five questions, covering insight generation through effective prompting. It addresses natural language interaction with analytics: asking questions of data in plain language rather than building a chart.

Lowering the barrier to analysis

The capability lowers the barrier to analysis, letting business users ask questions without knowing the data model or a query language. The examinable concept is that the quality of what you get back depends heavily on how the question is framed.

Effective prompting

Effective prompting here means being specific about the measure, the dimension, and the time period. “How are sales?” is ambiguous, which measure, sliced by what, over what period. “What were total sales by region last quarter compared with the previous quarter?” specifies all three and produces a usable answer.

Why the data model matters

Know also that natural language interpretation depends on the underlying data model. Well-named fields and defined business logic dramatically improve results, because the system maps language to model elements. This is the domain’s genuine insight: natural language analytics quality is largely determined by data modelling discipline, not by the language interface itself.

Which Governance and Risk Concepts Appear?

Governance runs through several domains rather than occupying its own. QAIS expects awareness of bias, transparency, data privacy, and the human oversight appropriate to AI-assisted decisions: practical responsibility rather than regulatory detail.

Bias

Bias is the most examinable concern. Models learn from historical data, so a model trained on decisions that were historically skewed will reproduce that skew while appearing objective because it is arithmetic. Understanding that removing a protected attribute does not remove bias when correlated proxies remain is the sophisticated version of this point.

Transparency and explainability

Transparency and explainability connect to the feature importance material. Stakeholders asked to act on a prediction reasonably want to know what drove it, and in some contexts an unexplainable model is unusable regardless of accuracy.

Data privacy in RAG

Data privacy applies acutely to retrieval augmented generation, where the system is deliberately supplied with organisational documents. Know that permissions must be enforced at retrieval and that sensitive data requires deliberate handling rather than being indexed by default.

Human oversight

Human oversight is the closing principle. AI output should inform decisions rather than make them unsupervised, particularly where consequences are significant: the reasoning formalised in frameworks such as the NIST AI Risk Management Framework.

Who Should Pursue the QAIS Credential?

QAIS suits Qlik practitioners adding AI capability, analytics professionals who need credible AI literacy, and BI developers implementing Qlik’s AI features. It assumes analytics familiarity but requires no programming or data science background.

Why 60% is transferable

The unusual value is that 60 percent of the exam is transferable. The AI fundamentals and business application domains are vendor-neutral, so preparation builds genuine literacy rather than product knowledge with a short shelf life. That is rare among vendor AI certifications.

For Qlik specialists

For Qlik specialists it is the natural next step as the platform’s AI capabilities move from novelty to expectation. Clients and stakeholders increasingly ask what AI can do for their analytics, and answering that credibly, including saying when the answer is “nothing useful yet”, is a differentiating skill.

For broader Qlik practitioners

Practitioners with a broader Qlik background will find the platform domains straightforward and the conceptual domains the real work. Those who have covered Qlik business analyst material or Qlik data integration tooling already have the modelling foundation that Insight Advisor and AutoML both depend on.

How Should You Prepare for QAIS?

Five to seven weeks at five to six hours per week suits Qlik practitioners. Effective QAIS preparation weights time toward the two conceptual domains carrying 60 percent, while still getting hands-on with the three Qlik capabilities.

1. Weeks one to two: AI fundamentals. Build a precise mental model of AI, machine learning, deep learning, and generative AI as nested categories. Learn the three learning paradigms and be able to classify described problems into them.
2. Weeks three to four: business applications. Practise matching business problems to predictive or generative approaches, and deliberately identify problems where AI is not viable. Work the LLM lifecycle, especially the prompt engineering, retrieval, and fine-tuning distinction.
3. Week five: Qlik Answers. Build a RAG setup with your own documents. Ask questions the sources cannot answer and observe the behaviour, then adjust chunking and see how retrieval quality shifts.
4. Week six: AutoML and Insight Advisor. Train a model end to end, review feature importance, and deliberately introduce a leaking feature to see the effect. Practise vague versus specific natural language queries.
5. Week seven: review and timed practice. Full-length practice weighted toward the conceptual domains.

The habit that matters most is resisting the assumption that more sophisticated is better. The exam repeatedly offers fine-tuning where retrieval is correct, or a generative approach where a predictive one is required, and the right answer is consistently the simplest approach that solves the stated problem. Timed work through the QAIS practice exam questions is the fastest way to test whether that discipline has taken hold at the 73 percent standard.

Frequently Asked Questions

How many questions are on the QAIS exam?

The exam contains 50 questions to be completed in 90 minutes, allowing roughly 108 seconds per question. The timing suits the conceptual, scenario-based style.

What is the passing score for QAIS?

You need 73 percent, meaning approximately 37 of the 50 questions correct. That is a demanding threshold leaving room for around thirteen incorrect answers.

How much does the Qlik AI Specialist exam cost?

The exam fee is $250 USD. Qlik provides learning materials through its education platform, and practice exams are available separately.

Which domains carry the most weight?

Introduction to Artificial Intelligence and Business Applications for AI each carry 30 percent, together accounting for 60 percent. Both are largely vendor-neutral conceptual material.

Do I need programming or data science experience?

No. QAIS tests conceptual understanding and platform capability rather than implementation. Familiarity with analytics helps considerably, but no coding or statistical modelling background is required.

What is retrieval augmented generation?

A technique that retrieves relevant content from your own documents at query time and supplies it to a language model as context. This grounds answers in your data and substantially reduces hallucination.

When should you fine-tune instead of using retrieval?

Fine-tuning suits changing model behaviour or style. If the problem is that the model lacks your organisation’s information, retrieval augmentation is correct: fine-tuning teaches behaviour, not facts.

Why is accuracy a poor metric on imbalanced data?

Because a model predicting the majority class every time scores highly while predicting nothing useful. Precision and recall, weighted by the relative cost of false positives and false negatives, are more informative.

What is data leakage in AutoML?

When a feature contains information about the target that would not be available at prediction time. It produces excellent apparent accuracy and no real predictive power, and often shows up as one feature dominating importance.

How long should I study for QAIS?

Five to seven weeks at five to six hours per week suits Qlik practitioners. Weight the time toward the AI fundamentals and business application domains rather than the Qlik feature domains.

Conclusion

QAIS is more useful than its vendor label suggests, because 60 percent of it is transferable AI literacy rather than product knowledge. The conceptual domains reward genuine understanding of what the categories of AI are and which business problems each actually fits.

Two distinctions decide most questions. Predictive versus generative determines which approach a use case needs, and retrieval versus fine-tuning determines how a language model application should be adapted: retrieval when the model lacks your facts, fine-tuning when it needs different behaviour.

At a 73 percent threshold there is limited room for guessing, so prepare the conceptual material properly rather than relying on platform familiarity. And when a question offers a sophisticated option and a simple one, check whether the simple one already solves the stated problem: it usually does.