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AI for IT Pro Quiz

Show your expertise in artificial intelligence, machine learning, and data-driven decision-making.

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AI Pro

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Question 1

What is a key consideration when deploying AI models in a highly regulated industry?

Ensuring the model is as large as possible for better accuracy

Prioritizing interpretability and compliance with AI governance frameworks

Avoiding any human intervention in decision-making

Using open-source models exclusively

Question 2

What is a key benefit of using Azure Cognitive Services in an AI project?

Provides pre-trained AI models for vision, speech, language, and decision-making

Replaces all custom-trained AI models

Automates Kubernetes cluster management

Primarily used for data visualization in Power BI

Question 3

"What function of Azure AI Translator should you use to convert the Russian word ""спасибо"" in Cyrillic characters to ""spasibo"" in Latin characters?"

Translate

Detect

Transliterate

BreakSentence

Question 4

A data scientist trained a deep learning model with TensorFlow. The deployed model is compute-intensive and needs to use the most optimal inference server for similar workloads. Which model type is compatible with compute-intensive and no-code deployments?

Mlflow

Triton

Custom

ML experiments

Question 5

What is the purpose of a convolutional layer in a CNN?

To detect features in images

To reduce the dimensionality of data

To increase the number of parameters

To perform data augmentation

Question 6

What is the main reason why prompt engineering is critical when using large language models?

It ensures models produce more accurate and contextually relevant responses

It eliminates the need for training large AI models

It reduces the cost of AI inference by limiting API calls

It prevents AI from making any mistakes in text generation

Question 7

Which of the following is a popular deep learning framework?

TensorFlow

Hadoop

Spark

SQL

Question 8

How does immutability in AI tools ensure data consistency?

Improved data integrity

Easier concurrency and state management

Faster data processing

Reduced memory usage

Question 9

What is transfer learning in machine learning?

Training a model from scratch using only labeled data

Using a pre-trained model and fine-tuning it for a new task

A method for reducing training data size by removing noisy samples

A technique to store and retrieve training data efficiently

Question 10

What is the primary benefit of using quantization in AI models?

It improves the ethical reasoning of AI systems

It reduces memory usage and speeds up inference

It increases the number of parameters in the model

It eliminates bias in training datasets

Question 11

Which component is critical in training AI models?

CPU

RAM

GPU

Optical Drive

Question 12

What was the main limitation of early chatbots like Eliza?

They relied on predefined responses and keyword matching

They used deep learning to generate responses

They could fully understand natural human conversations

They were designed to perform complex tasks autonomously

Question 13

Which of the following is a common activation function in neural networks?

Sigmoid

Linear

Polynomial

Exponential

Question 14

In Azure Machine Learning, what is the purpose of the Azure ML Workspace?

To store and manage ML experiments, models, and compute resources

To deploy machine learning models as web applications

To write and execute SQL queries on structured data

To create and manage virtual machines for AI workloads

Question 15

What is Microsoft Copilot primarily designed for?

Automating CI/CD pipelines in DevOps

Providing AI-powered code suggestions and assistance in development

Managing cloud storage in Azure

Creating and deploying containerized applications

Question 16

Which of the following optimizers is most commonly used to improve convergence in deep learning models?

Stochastic Gradient Descent (SGD)

Adam

Newton's Method

RMSProp

Question 17

What is the most effective way to reduce hallucinations in a Large Language Model?

Increase the model’s temperature parameter

Use Retrieval-Augmented Generation (RAG) to provide real-time factual data

Avoid training the model with diverse datasets

Reduce the model's training time to prevent overfitting

Question 18

What is the purpose of using embeddings in natural language processing (NLP)?

To increase the dimensionality of text data for better accuracy.

To represent words in dense vector forms.

To ensure all text data is normalized.

To classify documents into predefined categories.

Question 19

Which concept in neural networks involves incorporating past information in sequential data?

Convolutional Layers (CNN)

Recurrent Layers (RNN)

Batch Normalization

Attention Mechanism

Question 20

Which approaches are essential for training ML models to handle large data efficiently?

Distributed computing

Increasing model complexity

Reducing dataset size

Batch processing

Quiz Results

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