Welcome! Preparing for the Qlik AI Specialist Certification (QAIS) 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 QAIS exam style and structure. We encourage you to try our Demo Qlik AI Specialist Certification 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 QAIS Sample Questions:
01. Which element does Insight Advisor rely on when translating a user’s natural-language query into a chart?
a) Script-level variable definitions
b) Associations, field roles, and metadata
c) AutoML model endpoints
d) RAG document ingestion
02. What is one advantage of machine learning models compared to manually crafted decision rules?
a) They guarantee perfect interpretability
b) They require no data for training
c) They automatically adapt to new patterns in data
d) They eliminate the need for evaluation
03. Which capability allows Qlik AutoML to help users understand how specific inputs affect a prediction?
a) Explainability charts such as SHAP values
b) Prediction caching
c) RAG context injection
d) Notebook-based scripting
04. During LLM app development, which step helps ensure better alignment with user intent?
a) Increasing model size indefinitely
b) Iterating and testing improved prompt designs
c) Removing all contextual data
d) Completely avoiding fine-tuning
05. Which statement describes a key limitation of GenAI for enterprise use?
a) It cannot generate any structured outputs
b) It only works with supervised learning
c) It cannot summarize long documents
d) It may produce inconsistent or fabricated details
06. What role does a Large Language Model (LLM) primarily play within the broader domain of artificial intelligence?
a) Understanding and generating human-like natural language
b) Performing supervised classification using labeled tabular datasets
c) Managing automated data pipelines and ETL jobs
d) Rendering visualizations directly from structured data sources
07. Why are embeddings essential for semantic search?
a) They match documents based solely on exact keywords
b) They compress data for faster network transfers
c) They provide vector representations enabling similarity scoring
d) They enforce deterministic query results
08. Which business challenge is commonly encountered when integrating AI into existing data pipelines?
a) AI requires no data preparation
b) AI workloads never require cloud resources
c) Models automatically update without supervision
d) Latency and scalability constraints
09. Which output is commonly produced by Qlik AutoML after model training?
a) Feature importance scores
b) RAG retrieval logs
c) NLP embeddings
d) Insight Advisor narrative summaries
10. In the Qlik AutoML workflow, which step typically occurs after feature engineering but before model deployment?
a) Gathering user feedback
b) Hyperparameter tuning
c) Creating insight briefs
d) Integrating LLM prompt templates
Answers:
|
Question: 1 |
Answer: b |
Question: 2 |
Answer: c |
|
Question: 3 |
Answer: a |
Question: 4 |
Answer: b |
|
Question: 5 |
Answer: d |
Question: 6 |
Answer: a |
|
Question: 7 |
Answer: c |
Question: 8 |
Answer: d |
|
Question: 9 |
Answer: a |
Question: 10 |
Answer: b |
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