MLStackMLSCCafé
 
 
Sign in with GoogleSign in with Google. Opens in new tab
Master Your ML & DSML Interview
2103 Curated Machine Learning, Data Science, Python & LLMs Interview Questions
Answered To Get Your Next Six-Figure Job Offer
👨‍💻 Having Full-Stack & Coding Interview? Check  FullStack.Cafe - 3877 Full-Stack, Coding & System Design Questions and AnswersHaving Full-Stack & Coding Interview? Check 👨‍💻 FullStack.Cafe - 3877 Full-Stack, Coding & System Design Questions and Answers

Top 30 Azure ML Interview Questions

Entry Junior Mid Senior Expert
Sign in with GoogleSign in with Google. Opens in new tab
Learning Progress:

Azure ML Theoretical Questions

Q1:   

How to transfer data automatically into Azure Machine Learning?

  Related To: MLOps
Add to PDF   Entry 
Q2:   

What is a Scoring Script in Azure Machine Learning?

  
Add to PDF   Junior 
Q3:   

What's the difference between Datastore and Data Asset in Azure Machine Learning?

  
Add to PDF   Junior 
Q4:   

How can you create a pipeline in Azure Machine Learning?

  Related To: MLOps
Add to PDF   Junior 
Q5:   

What types of environments in Azure Machine Learning do you know?

  
Add to PDF   Junior 
Q6:   

What are some options to create a Machine learning pipeline in Azure?

  Related To: MLOps
Add to PDF   Junior 
Q7:   

Why would you use components in the context of an Azure Machine Learning pipeline?

  
Add to PDF   Junior 
Q8:   

What are the types of deployments in Azure ML batch endpoints?

  
 Add to PDF   Mid 
Q9:   

What types of compute targets in Azure Machine Learning do you know?

  
 Add to PDF   Mid 
Q10:   

How to define a machine learning job in Azure Machine Learning?

  Related To: MLOps
 Add to PDF   Mid 
Q11:   

How to create a batch deployment in Azure Machine Learning?

  
 Add to PDF   Mid 
Q12:   

When would you need to use a Compute Cluster in Azure Machine Learning?

  
 Add to PDF   Mid 
Q13:   

What's the difference between Compute Instance and Compute Target in Azure machine learning? When would you use each one?

  
 Add to PDF   Mid 
Q14:   

What's the difference between a Pipeline Job and Pipeline Component in Azure Machine Learning?

  
 Add to PDF   Mid 
Q15:   

What's the difference between HyperDrive and AutoML services?

  Related To: MLOps
 Add to PDF   Mid 
Q16:   

What types of Azure Data Factory pipelines can be used for Azure Machine Learning?

  
 Add to PDF   Mid 
Q17:   

How to detect data drift in Azure Machine Learning?

  Related To: Data Processing, MLOps
 Add to PDF   Mid 
Q18:   

What are the steps to put an ML model in production with Azure Machine Learning?

  
 Add to PDF   Mid 
Q19:   

Name some metrics you would monitor for Online Endpoints and Online Deployments

  
 Add to PDF   Mid 
Q20:   

What's the difference between Azure ML Notebooks VS Azure Databricks?

  
 Add to PDF   Mid 
Q21:   

When would you use Online Endpoint vs Batch Endpoint in Azure Machine Learning?

  
 Add to PDF   Mid 
Q22:   

What's the difference between Endpoints and Deployments in Azure Machine Learning?

  
 Add to PDF   Mid 
Q23:   

Name some training methods available in Azure Machine Learning

  
 Add to PDF   Mid 
Q24:   

How can you run distributed ML in Azure?

  Related To: MLOps
 Add to PDF   Senior 
Q25:   

Can you provide a high-level overview of how can you run in parallel a single pipeline step?

  
 Add to PDF   Senior 
Q26:   

What kind of drifts can you monitor in Azure Machine Learning?

  
 Add to PDF   Senior 
Q27:   

What types of Online Endpoints do you know in Azure Machine Learning?

  
 Add to PDF   Senior 
Q28:   

What tools can you use to interpret your model in Azure Machine Learning?

  
 Add to PDF   Senior 
Q29:   

When would you use Nebula in Azure Machine Learning?

  
 Add to PDF   Senior 
Q30:   

What is reuse in a Azure Machine Learning pipeline?

  
 Add to PDF   Expert 
 

Amazone runs the internet as we know it. Amazon Web Services (AWS) offers a comprehensive suite of machine learning (ML) services that cater to various needs and expertise levels. Follow along and learn the 23 most common AWS machine-learning intervi...

Azure Machine Learning (Azure ML) is a cloud-based service for creating and managing machine learning solutions. It’s designed to scale, distribute, and deploy machine learning models to the cloud. Follow along and learn the 23 most common Azure Mach...

Hadoop is an open-source big data processing framework. It leverages distributed computing to store and process large datasets in a fault-tolerant manner. According to recent reports, Apache Hadoop is one of the most sought-after big data skills with...

Apache Spark is a unified analytics engine for large-scale data processing. It is built to handle various use cases in big data analytics, including data processing, machine learning, and graph processing. Follow along and learn the 23 most common an...
Scala is a powerful language with functional programming capabilities that can be a good choice for data science, especially in big data and distributed computing scenarios. As an example, Apache Spark, a popular distributed data processing framework...
PyTorch popularity as a Deep Learning framework of choice is on the rise. As of December 2022, 62% of the academic papers were implemented in PyTorch whereas only 4% were for TensorFlow. Follow along and prepare effectively with these key 30 PyTorch ...
The use of Artificial Intelligence (AI) in machine learning and data science enabled advancements in areas such as natural language processing, computer vision, recommendation systems, fraud detection, predictive analytics, and personalized medicine....
Optimization algorithms are extensively used in training machine learning models. Data engineers employ algorithms like gradient descent, stochastic gradient descent, and variants (e.g., Adam, RMSprop) to optimize the model parameters and minimize th...
ChatGPT, an implementation of the GPT (Generative Pre-trained Transformer) model excels in understanding and generating human-like text, making it a powerful tool for NLP tasks. ML engineers and software developers can leverage ChatGPT's capabilities...
Large Language Models (LLMs), such as GPT-3.5, have revolutionized natural language processing by demonstrating the ability to generate human-like text and comprehend context. Follow along to understand the top 27 LLMs-related interview questions and...