100+ Free Qlik AI Practice Questions

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Question 1

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Which AI subset focuses on creating new content (text, images, code) rather than classifying existing data?

Generative AI (GenAI)

Supervised machine learning

Rule-based systems

Clustering

to track

2026 Statistics

Key Facts: Qlik AI Exam

Questions

Qlik Learning

120 min

Time Limit

Qlik Learning

73%

Passing Score

Qlik Learning

$250

Exam Fee

buy-learning.qlik.com

5 domains

Content Areas

Qlik Learning

AutoML + RAG

Core Qlik AI Tech

Qlik Cloud

As of April 15, 2026, the Qlik AI Specialist exam is a 50-question, 120-minute online proctored exam with a 73% passing score on the Qlik Learning platform. It is Qlik's highest published cut score across current product certifications. Domain weights are Introduction to AI (30%), Business Applications for AI (30%), Qlik Answers (15%), Qlik AutoML (15%), and Insight Advisor (10%). Standard price is $250 USD on buy-learning.qlik.com.

Sample Qlik AI Practice Questions

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

1Which AI subset focuses on creating new content (text, images, code) rather than classifying existing data?

A.Generative AI (GenAI)

B.Supervised machine learning

C.Rule-based systems

D.Clustering

Explanation: Generative AI creates novel content using models (often LLMs) trained on large corpora. Supervised ML classifies or regresses; rule-based systems follow fixed logic; clustering groups existing data.

2What does LLM stand for?

A.Large Language Model

B.Long Linear Machine

C.Learned Logistic Model

D.Layered Logic Matrix

Explanation: LLM stands for Large Language Model. LLMs are neural networks trained on vast text corpora; they underpin tools like ChatGPT, Qlik Answers, and many modern AI assistants.

3What is Retrieval Augmented Generation (RAG)?

A.An LLM pattern that retrieves relevant context from an external knowledge base and passes it to the LLM to ground responses

B.LLM training from scratch

C.A type of model compression

D.A new programming language

Explanation: RAG combines retrieval (search across a curated knowledge base) with generation (LLM output conditioned on the retrieved context). It reduces hallucination and grounds answers in governed data.

4Which Qlik product uses RAG to answer business questions from governed knowledge bases?

A.Qlik Answers

B.Qlik AutoML

C.Qlik Replicate

D.Qlik Compose

Explanation: Qlik Answers is Qlik's generative AI assistant. It retrieves from governed knowledge bases (documents, wiki content, FAQs) and answers with LLM-generated responses grounded in those sources.

5Which Qlik tool lets analysts build predictive ML models (classification, regression) without code?

A.Qlik AutoML

B.Qlik Compose

C.Qlik Replicate

D.Qlik NPrinting

Explanation: Qlik AutoML is the automated ML experience in Qlik Cloud. It automates feature engineering, model selection, training, and deployment for supervised learning problems — all without writing code.

6Which Qlik Sense feature answers natural-language questions with generated charts from the loaded app data?

A.Insight Advisor

B.Qlik Answers

C.Qlik Replicate

D.Qlik Compose

Explanation: Insight Advisor in Qlik Sense accepts natural-language questions and returns suggested charts based on the data model and master items. It is the NL analytics layer inside Qlik Sense.

7Which metric is MOST appropriate for evaluating a binary classification model?

A.Accuracy, F1, and ROC AUC

B.RMSE only

C.R-squared only

D.Gini coefficient only

Explanation: Binary classification is typically evaluated with accuracy, precision, recall, F1, and ROC AUC. RMSE/R-squared are regression metrics. Gini is related but usually reported alongside AUC.

8Which metric is commonly used for REGRESSION problems?

A.RMSE (Root Mean Squared Error)

B.F1

C.ROC AUC

D.Recall

Explanation: RMSE, MAE, and R-squared are the standard regression metrics. F1, ROC AUC, and recall belong to classification.

9Which ML workflow step defines the business outcome and success criteria?

A.Problem framing

B.Model training

C.Hyperparameter tuning

D.Deployment

Explanation: Problem framing identifies the business question, the target variable, and what 'success' means. Skipping it often leads to technically correct but business-useless models.

10Which LLM lifecycle step takes raw prompts and retrieved context and produces the LLM call?

A.Prompt construction

B.Training

C.Tokenisation

D.Compression

Explanation: Prompt construction assembles system prompts, user inputs, and retrieved context into the final prompt passed to the LLM. It is the core of RAG, agent, and assistant designs.

About the Qlik AI Exam

The Qlik AI Specialist Certification validates that you understand the fundamentals of AI and machine learning, can identify appropriate business applications, and can apply Qlik's AI-powered features: Qlik AutoML for predictive models, Qlik Answers for generative AI assistants, and Insight Advisor for natural language analytics. It is designed for analysts and developers delivering AI-infused Qlik apps in 2026.

Assessment

50 multiple-choice questions

Time Limit

120 minutes

Passing Score

73%

Exam Fee

$250 USD (Qlik)

Qlik AI Exam Content Outline

30%

Introduction to Artificial Intelligence

AI subsets, machine learning vs generative AI, NLP, LLMs, tokens, prompts, embeddings, and foundational AI terminology.

30%

Business Applications for AI

GenAI and ML use case selection, the LLM project lifecycle, production considerations (latency, cost, RAG), ML workflow fundamentals, data governance, and AI ethics.

15%

Fundamentals of Qlik Answers

Qlik Answers workflow, retrieval augmented generation (RAG), knowledge bases, and use case evaluation.

15%

Fundamentals of Qlik AutoML

AutoML workflow (experiment, train, deploy, predict), supervised learning concepts, and evaluating when AutoML fits a business problem.

10%

Fundamentals of Insight Advisor

Natural language analytics in Qlik Sense, Insight Advisor Chat, best practices for prompt creation and business vocabulary.

How to Pass the Qlik AI Exam

What You Need to Know

Passing score: 73%
Assessment: 50 multiple-choice questions
Time limit: 120 minutes
Exam fee: $250 USD

Keys to Passing

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

Qlik AI Study Tips from Top Performers

1Memorize the ML workflow: problem framing, data prep, train, validate, deploy, monitor.

2Know the LLM application lifecycle: prompt, retrieve, generate, evaluate, deploy, iterate.

3Understand RAG: why it grounds LLM answers in governed data and reduces hallucination.

4Know when to pick AutoML (predictive ML) vs Qlik Answers (generative AI) vs Insight Advisor (NL analytics).

5Study AutoML metrics: classification (F1, accuracy, ROC AUC) vs regression (RMSE, MAE, R-squared).

6Review AI ethics, governance, bias, and data privacy considerations — they show up in scenario questions.

Frequently Asked Questions

How many questions are on the Qlik AI Specialist exam?

Qlik's current Qlik AI Specialist exam details page lists 50 multiple-choice questions in a 120-minute window. The exam is delivered online through the Qlik Learning platform with remote proctoring.

What score do you need to pass the Qlik AI Specialist exam?

The published passing score is 73%, the highest among current Qlik product certifications. Qlik reserves the right to adjust passing scores to keep the standard consistent, so plan to hit 80%+ on timed practice before scheduling.

What does the Qlik AI Specialist exam test?

Five domains: Introduction to AI (30%), Business Applications for AI (30%), Qlik Answers (15%), Qlik AutoML (15%), and Insight Advisor (10%). Roughly 60% of the exam is vendor-neutral AI and ML concepts; the remaining 40% is applied Qlik AI product knowledge across AutoML, Answers, and Insight Advisor.

What is Qlik AutoML?

Qlik AutoML is the automated machine learning capability in Qlik Cloud. It lets analysts build supervised ML models (classification and regression) without code by picking a target, selecting training features, training an experiment, and deploying the best model. Predictions can then be consumed in Qlik Sense apps and automations.

What is Qlik Answers?

Qlik Answers is Qlik's generative AI assistant. It uses retrieval augmented generation (RAG) against governed knowledge bases to produce grounded, citation-backed answers to business questions. The exam tests when Qlik Answers is the right tool versus AutoML or Insight Advisor.

Do I need deep ML background to pass Qlik AI Specialist?

No. The exam is positioned as a specialist-level conceptual certification, not a data scientist exam. You should understand AI and ML vocabulary, the ML workflow, LLM lifecycle, and Qlik's three AI pillars well enough to identify appropriate use cases, not implement a custom model from scratch.