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AI Pro - Digital Skill Cup 2026

Digital Skills Cup 2026

Welcome to the Digital Skill Cup 2026 for AI Pro

Once you register with your name and email, the quiz starts immediately.
You have 5 minutes to complete the quiz.
No pauses. No switching between windows, no copy-pasting.
Scoring is based on the number of correct answers and the lower time to complete the quiz, in case of a tie.
Use a valid email address and your real name for registration.
You have 1 attempt.
Good luck :)

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Digital Skill Cup 2026 - AI Pro All

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Question 1

What type of data does Microsoft Copilot use to generate responses?

Public internet data only

Organizational data and Microsoft Graph (with permissions)

Encrypted passwords

Raw database files

Question 2

When training deep neural networks, which statements about adaptive learning rate optimizers (Adam, RMSprop) are correct?

They maintain per-parameter learning rates based on gradient history

They always converge faster than stochastic gradient descent (SGD) with momentum across all tasks

They guarantee better generalization performance than SGD

They eliminate the need for learning rate scheduling

Question 3

The most effective mitigation against prompt injection in enterprise assistants is:

Increase temperature to reduce deterministic attacks.

Rely on user vigilance instead of technical controls.

Ask the model to ignore adversarial instructions and trust all links.

Constrain tool access and enforce allow‑listed data sources with content filtering and input/output validation.

Question 4

What is the primary role of a GPU in AI workloads?

To store large datasets

To accelerate parallel computations required for training neural networks

To manage network traffic

To improve database indexing

Question 5

Which statements about Parameter-Efficient Fine-Tuning (PEFT) methods like LoRA are correct?

They freeze the base model weights and train small adapter layers

They significantly reduce memory requirements during fine-tuning

They always achieve better performance than full fine-tuning

They enable efficient multi-task adaptation with separate adapters

Question 6

What is the primary benefit of using immutability in functional programming?

Improved readability of code

Easier concurrency and state management

Faster runtime execution

Reduced memory consumption

Question 7

What is the main advantage of using transformers in NLP?

Capturing long-range dependencies

Reducing dataset size

Improving image compression

Increasing disk access speed

Question 8

A human in the loop review is required before AI generated medical advice is published. Which workflow most effectively enforces this control?

Let authors self attest that they reviewed the content

Add a terms of service disclaimer to the website footer

Require a reviewer approval with role based access control and immutable logging before release

Place the model behind VPN

Question 9

Which techniques are commonly used to prevent data leakage during machine learning model training?

Train-test split before feature engineering

Feature scaling using the full dataset

Cross-validation with proper data separation

Leakage-aware pipeline design

Question 10

For function calling/tool use, when is it preferable over pure text generation?

When you want the model to ignore external systems.

When text output is unacceptable regardless of constraints.

When creative writing quality matters most.

When the task requires structured actions/data (e.g., querying a system, returning typed fields) with parameter validation.

Question 11

How does the vanishing gradient problem most directly impact the training of very deep neural networks?

It causes gradients to grow exponentially in deeper layers

It increases the memory footprint during deployment

It leads to unstable loss values during inference

It prevents effective learning in layers close to the input

Question 12

What is overfitting in machine learning?

When a model performs poorly on training data

When a model performs well on unseen data

When a model performs well on training data but poorly on new data

When a model is too small to learn patterns

Question 13

An ML Ops engineer needs to detect data drift in a production model predicting loan default probability. Which approach best balances sensitivity and false alarms?

Alert on any single feature Kolmogorov - Smirnov test with p < 0.05

Use population stability index (PSI) thresholds plus periodic performance backtesting on labeled samples

Retrain the model automatically whenever input schema changes

Compare daily means only for each feature

Question 14

What is the purpose of batch normalization in neural networks?

Encrypt data

Stabilize and accelerate training by normalizing layer inputs

Increase model capacity

Reduce number of features

Question 15

When deploying LLMs in production, which techniques are most effective at mitigating hallucinations without retraining the base model?

Increasing the temperature parameter to 1.5 to encourage more random outputs

Implementing RAG to ground responses in external data

Reducing the context window size to limit input complexity

Using system prompts to explicitly instruct the model to admit ignorance rather than hallucinating

Question 16

When deploying an Azure Machine Learning model for real‑time inference, the recommended endpoint type is:

Batch Endpoint

Managed Online Endpoint

Azure Kubernetes Service only

Storage Account Trigger

Question 17

What does "hallucination" mean in the context of an AI model?

A server crash during inference

A particularly creative image generation

Detecting patterns in noisy data

Generating factually incorrect or nonsensical information

Question 18

Which Microsoft service allows organizations to build, train, and deploy machine learning models?

Microsoft Sentinel

Azure AI Studio

Microsoft Intune

Copilot

Question 19

What is a practical symptom of exceeding the context window in long conversations?

The UI locks until memory is cleared.

The model always restarts with a new system prompt.

Earlier turns become truncated, causing the model to lose constraints or definitions introduced previously.

The model writes shorter sentences only.

Question 20

What is the primary benefit of using immutability in functional?

Improved readability of code

Easier concurrency and state management

Faster runtime execution

Reduced memory consumption

Quiz Results

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