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Machine Learning in Azure Online Test

About the test:

The Machine Learning in Azure test evaluates a candidate's knowledge and skills in using Azure Machine Learning for various stages of the machine learning lifecycle. It covers topics such as data preparation, model building and evaluation, model deployment, hyperparameter tuning, and more. The test includes both conceptual multiple-choice questions and coding questions to assess practical programming knowledge and hands-on experience.

Covered skills:

  • Data preparation and feature engineering
  • Azure ML algorithms
  • Azure ML Pipelines
  • Azure AutoML
  • Azure ML Interpretability
  • Azure ML Model Deployment
  • Model building and evaluation
  • Model deployment and management
  • Hyperparameter tuning
  • Azure ML Designer
  • Azure ML Model Explainability

9 reasons why
9 reasons why

Adaface Machine Learning in Azure Test is the most accurate way to shortlist Machine Learning Engineers



Reason #1

Tests for on-the-job skills

The Machine Learning in Azure Online Test helps recruiters and hiring managers identify qualified candidates from a pool of resumes, and helps in taking objective hiring decisions. It reduces the administrative overhead of interviewing too many candidates and saves time by filtering out unqualified candidates at the first step of the hiring process.

The test screens for the following skills that hiring managers look for in candidates:

  • Able to create and manage Azure ML workspaces
  • Proficient in data preprocessing and cleaning techniques in Azure ML
  • Capable of building and evaluating machine learning models in Azure ML
  • Skilled in utilizing Azure ML algorithms for model development
  • Experienced in deploying and managing machine learning models in Azure ML
  • Familiar with Azure ML Pipelines for automated machine learning workflows
  • Knowledgeable in hyperparameter tuning techniques in Azure ML
  • Competent in utilizing Azure AutoML for automated model training
  • Proficient in designing machine learning workflows with Azure ML Designer
  • Able to interpret machine learning models in Azure ML
  • Capable of explaining the predictions and outputs of Azure ML models
  • Skilled in deploying machine learning models as web services on Azure
  • Adept at utilizing Azure ML for model deployment and management
Reason #2

No trick questions

no trick questions

Traditional assessment tools use trick questions and puzzles for the screening, which creates a lot of frustration among candidates about having to go through irrelevant screening assessments.

View sample questions

The main reason we started Adaface is that traditional pre-employment assessment platforms are not a fair way for companies to evaluate candidates. At Adaface, our mission is to help companies find great candidates by assessing on-the-job skills required for a role.

Why we started Adaface
Reason #3

Non-googleable questions

We have a very high focus on the quality of questions that test for on-the-job skills. Every question is non-googleable and we have a very high bar for the level of subject matter experts we onboard to create these questions. We have crawlers to check if any of the questions are leaked online. If/ when a question gets leaked, we get an alert. We change the question for you & let you know.

How we design questions

These are just a small sample from our library of 10,000+ questions. The actual questions on this Machine Learning in Azure Test will be non-googleable.

🧐 Question

Medium

Backup and Restore Strategy
Databases
Backup
Recovery
Solve
You are a database administrator for an organization that uses Azure SQL Database for its operations. The organization has a strict data retention policy and has set up the following backup strategy:

1. Full backups are taken every Sunday at midnight.
2. Differential backups are taken every day at midnight, excluding Sunday.
3. Transaction log backups are taken every hour on the hour.

On Wednesday at 2:30 PM, a failure occurred, and the latest backup files available are: full backup from the previous Sunday, differential backups for Monday and Tuesday, and transaction log backups up to Wednesday 2 PM.

In order to restore the database to the most recent point in time with the minimum amount of data loss, in what order should you restore the backups?
A: Restore the full backup, then the differential backup for Tuesday, then the differential backup for Wednesday, then each transaction log backup from midnight on Wednesday to 2 PM on Wednesday.

B: Restore the full backup, then the differential backup for Wednesday, then each transaction log backup from midnight on Wednesday to 2 PM on Wednesday.

C: Restore the full backup, then each differential backup from Monday and Tuesday, then each transaction log backup from midnight on Wednesday to 2 PM on Wednesday.

D: Restore the full backup, then the differential backup for Monday, then each transaction log backup from midnight on Monday to 2 PM on Wednesday.

E: Restore the full backup, then the differential backup for Tuesday, then each transaction log backup from midnight on Tuesday to 2 PM on Wednesday.

Medium

Resolving Connection Issues
Virtual Machines
Networking
Security
Solve
You are an Azure Administrator and you manage a Linux VM running an internal web application in Azure. The web application communicates with a database server hosted on another VM in the same Virtual Network (VNet).

Recently, users have reported that the web application is not accessible. After initial troubleshooting, you have identified that the web application VM is unable to establish a connection with the database server VM on port 5432.

You have checked and confirmed the following:

1. Both VMs are up and running without any issues.
2. Both VMs are located in the same VNet and subnet.
3. Both VMs can successfully ping each other.
4. A Network Security Group (NSG) is associated with the subnet, and it has a rule allowing all outbound traffic from the web application VM.
5. The NSG rule for inbound traffic to the database VM on port 5432 has a higher priority than the default deny all rule.

Given the information provided, what could be the most likely reason for the issue and the appropriate resolution?
A: Add a route table to the subnet to enable communication between the VMs.

B: The NSG rule priority for the inbound traffic to the database VM is not set correctly. Adjust the priority to be lower than the default rule.

C: Check if a firewall is enabled on the database VM that might be blocking the port. If so, configure it to allow connections on port 5432.

D: The issue is related to the DNS resolution. Update the DNS settings in the VNet to enable name resolution between the VMs.

E: The web application is not correctly configured to connect to the database. Update the connection string in the web application configuration.

Medium

Resolving NSG Configuration Issues
Virtual Machines
Security
Solve
You are an Azure Administrator in a software development company. A Linux VM is deployed on Azure, hosting an application server running on port 5000, set to start whenever the VM is booted up.

The VM is associated with a Network Security Group (NSG) having the following inbound security rules:

- Rule 100 (Priority: 100): Allow SSH (port 22) from any source
- Rule 200 (Priority: 200): Allow HTTP (port 80) from any source
- Rule 400 (Priority: 400): Allow TCP traffic on port 5000 from any source
- Rule 300 (Priority: 300): Deny all inbound traffic from any source

The outbound security rules are configured to allow all traffic to any destination.

Internal users have been attempting to connect to the application server on port 5000 but they are consistently facing connection timeouts. You've confirmed the application server is up and running, and you can connect to the server locally on the VM.

What is the most probable cause of the problem and how would you fix it?
A: The inbound rule to allow TCP traffic on port 5000 is conflicting with the rule to allow HTTP on port 80. Remove Rule 200.

B: Rule 300 to deny all inbound traffic is being processed before Rule 400 to allow traffic on port 5000. Modify the priority of Rule 400 to a value less than 300.

C: The application server should be configured to listen on a well-known port instead of port 5000. Change the server settings.

D: The NSG is missing an inbound rule to allow ICMP traffic. Add a new rule with a lower priority.

E: The NSG needs to have an outbound rule specifically allowing traffic to port 5000. Add a new outbound rule.
🧐 QuestionπŸ”§ Skill

Medium

Backup and Restore Strategy
Databases
Backup
Recovery

2 mins

Azure
Solve

Medium

Resolving Connection Issues
Virtual Machines
Networking
Security

2 mins

Azure
Solve

Medium

Resolving NSG Configuration Issues
Virtual Machines
Security

2 mins

Azure
Solve
🧐 QuestionπŸ”§ SkillπŸ’ͺ DifficultyβŒ› Time
Backup and Restore Strategy
Databases
Backup
Recovery
Azure
Medium2 mins
Solve
Resolving Connection Issues
Virtual Machines
Networking
Security
Azure
Medium2 mins
Solve
Resolving NSG Configuration Issues
Virtual Machines
Security
Azure
Medium2 mins
Solve
Reason #4

1200+ customers in 75 countries

customers in 75 countries
Brandon

With Adaface, we were able to optimise our initial screening process by upwards of 75%, freeing up precious time for both hiring managers and our talent acquisition team alike!


Brandon Lee, Head of People, Love, Bonito

Reason #5

Designed for elimination, not selection

The most important thing while implementing the pre-employment Machine Learning in Azure Test in your hiring process is that it is an elimination tool, not a selection tool. In other words: you want to use the test to eliminate the candidates who do poorly on the test, not to select the candidates who come out at the top. While they are super valuable, pre-employment tests do not paint the entire picture of a candidate’s abilities, knowledge, and motivations. Multiple easy questions are more predictive of a candidate's ability than fewer hard questions. Harder questions are often "trick" based questions, which do not provide any meaningful signal about the candidate's skillset.

Science behind Adaface tests
Reason #6

1 click candidate invites

Email invites: You can send candidates an email invite to the Machine Learning in Azure Test from your dashboard by entering their email address.

Public link: You can create a public link for each test that you can share with candidates.

API or integrations: You can invite candidates directly from your ATS by using our pre-built integrations with popular ATS systems or building a custom integration with your in-house ATS.

invite candidates
Reason #7

Detailed scorecards & benchmarks

View sample scorecard
Reason #8

High completion rate

Adaface tests are conversational, low-stress, and take just 25-40 mins to complete.

This is why Adaface has the highest test-completion rate (86%), which is more than 2x better than traditional assessments.

test completion rate
Reason #9

Advanced Proctoring


Learn more

About the Machine Learning in Azure Assessment Test

Why you should use Pre-employment Machine Learning in Azure Online Test?

The Machine Learning in Azure Test makes use of scenario-based questions to test for on-the-job skills as opposed to theoretical knowledge, ensuring that candidates who do well on this screening test have the relavant skills. The questions are designed to covered following on-the-job aspects:

  • Understanding the basics of Azure Machine Learning
  • Data preprocessing and cleaning techniques using Azure ML
  • Feature engineering and selection with Azure ML
  • Building and evaluating machine learning models in Azure ML
  • Working with various Azure ML algorithms for classification and regression
  • Deploying and managing models in Azure ML
  • Creating and executing Azure ML pipelines
  • Tuning hyperparameters in Azure ML
  • Utilizing Azure AutoML for automated model selection and tuning
  • Designing machine learning workflows using Azure ML Designer

Once the test is sent to a candidate, the candidate receives a link in email to take the test. For each candidate, you will receive a detailed report with skills breakdown and benchmarks to shortlist the top candidates from your pool.

What topics are covered in the Machine Learning in Azure Online Test?

  • Data preparation and feature engineering

    Data preparation and feature engineering involve transforming raw data into a format suitable for ML models and creating new features to improve model performance. This skill is measured in the test to evaluate a candidate's proficiency in data preprocessing and feature extraction techniques.

  • Model building and evaluation

    Model building and evaluation focus on creating ML models using different algorithms and techniques, along with assessing their performance and accuracy. This skill is measured in the test to gauge a candidate's ability to construct effective ML models and evaluate their results with appropriate metrics.

  • Azure ML algorithms

    Azure ML algorithms include a range of pre-built ML models and techniques that can be used for various types of data analysis and prediction tasks. This skill is measured in the test to determine a candidate's familiarity with different Azure ML algorithms and their suitability for specific scenarios.

  • Model deployment and management

    Model deployment and management involve the processes of deploying ML models into production environments, monitoring their performance, and making necessary updates and improvements. This skill is measured in the test to assess a candidate's understanding of the end-to-end ML model lifecycle and their ability to implement deployment and management strategies using Azure ML.

  • Azure ML Pipelines

    Azure ML Pipelines enables the creation and orchestration of ML workflows, automating the steps involved in data preparation, model training, and deployment. This skill is measured in the test to evaluate a candidate's proficiency in designing and implementing ML pipelines using Azure ML.

  • Hyperparameter tuning

    Hyperparameter tuning involves finding the optimal values for the hyperparameters of an ML model to maximize its performance and generalization. This skill is measured in the test to assess a candidate's knowledge and expertise in applying techniques for hyperparameter tuning using Azure ML.

  • Azure AutoML

    Azure AutoML is a feature in Azure ML that automates the process of model selection and hyperparameter tuning, enabling the development of high-performing ML models with minimal manual intervention. This skill is measured in the test to gauge a candidate's understanding of Azure AutoML and their ability to utilize its capabilities for efficient ML model development.

  • Azure ML Designer

    Azure ML Designer is a no-code tool in Azure ML that allows users to visually build, train, and deploy ML models using a drag-and-drop interface. This skill is measured in the test to determine a candidate's familiarity with Azure ML Designer and their ability to leverage its functionalities for ML model development.

  • Azure ML Interpretability

    Azure ML Interpretability focuses on understanding and interpreting the factors influencing the predictions made by ML models. This skill is measured in the test to evaluate a candidate's knowledge and skills in analyzing and interpreting the results and behaviors of ML models using Azure ML Interpretability features.

  • Azure ML Model Explainability

    Azure ML Model Explainability deals with providing explanations for the predictions made by ML models, helping to build trust and understanding in their decision-making process. This skill is measured in the test to assess a candidate's proficiency in utilizing Azure ML Model Explainability features to provide transparent and interpretable ML models.

  • Azure ML Model Deployment

    Azure ML Model Deployment involves deploying trained ML models as web services or APIs, enabling real-time predictions and integration with other applications. This skill is measured in the test to gauge a candidate's ability to deploy ML models in production environments using Azure ML deployment techniques.

  • Full list of covered topics

    The actual topics of the questions in the final test will depend on your job description and requirements. However, here's a list of topics you can expect the questions for Machine Learning in Azure Test to be based on.

    Azure ML workspaces
    Azure ML Studio
    Azure ML datasets
    Azure ML experiments
    Data preprocessing
    Feature engineering
    Feature selection
    Exploratory data analysis
    Data visualization
    Model building
    Model evaluation
    Cross-validation
    Evaluation metrics
    Azure ML algorithms
    Supervised learning
    Unsupervised learning
    Classification algorithms
    Regression algorithms
    Clustering algorithms
    Dimensionality reduction
    Azure ML model deployment
    Web service deployment
    Model versioning
    Model monitoring
    Azure ML Pipelines
    Pipeline creation
    Pipeline scheduling
    Pipeline monitoring
    Hyperparameter tuning
    Grid search
    Random search
    Bayesian optimization
    Azure AutoML
    Automated model selection
    Automated hyperparameter tuning
    Automated feature engineering
    Azure ML Designer
    Drag-and-drop model development
    Custom module creation
    Pipeline execution
    Azure ML Interpretability
    Feature importance
    Model explanations
    Local interpretability
    Global interpretability
    Azure ML Model Explainability
    Interpretable machine learning
    Explainable AI
    Counterfactual explanations
    Azure ML Model Deployment
    Azure Kubernetes Service (AKS)
    Azure Container Instances (ACI)
    Application insights
    Azure ML Model Management
    Model versioning
    Model deployment slots
    Model scaling and performance
    Continuous integration and deployment (CI/CD)

What roles can I use the Machine Learning in Azure Online Test for?

  • Machine Learning Engineer
  • Data Scientist
  • Data Analyst
  • AI Engineer
  • Software Engineer
  • Data Engineer
  • Business Analyst
  • Research Scientist
  • AI Consultant
  • AI Researcher

How is the Machine Learning in Azure Online Test customized for senior candidates?

For intermediate/ experienced candidates, we customize the assessment questions to include advanced topics and increase the difficulty level of the questions. This might include adding questions on topics like

  • Interpreting and explaining models using Azure ML Interpretability
  • Deploying models for production with Azure ML Model Deployment
  • Understanding Azure Databricks and its integration with Azure ML
  • Utilizing Azure ML for Natural Language Processing (NLP) tasks
  • Implementing computer vision solutions using Azure ML
  • Working with time series data in Azure ML
  • Handling distributed computing with Azure ML
  • Utilizing Azure ML for reinforcement learning
  • Applying anomaly detection techniques in Azure ML
Singapore government logo

The hiring managers felt that through the technical questions that they asked during the panel interviews, they were able to tell which candidates had better scores, and differentiated with those who did not score as well. They are highly satisfied with the quality of candidates shortlisted with the Adaface screening.


85%
reduction in screening time

Machine Learning in Azure Hiring Test FAQs

Can I combine multiple skills into one custom assessment?

Yes, absolutely. Custom assessments are set up based on your job description, and will include questions on all must-have skills you specify. Here's a quick guide on how you can request a custom test.

Do you have any anti-cheating or proctoring features in place?

We have the following anti-cheating features in place:

  • Non-googleable questions
  • IP proctoring
  • Screen proctoring
  • Web proctoring
  • Webcam proctoring
  • Plagiarism detection
  • Secure browser
  • Copy paste protection

Read more about the proctoring features.

How do I interpret test scores?

The primary thing to keep in mind is that an assessment is an elimination tool, not a selection tool. A skills assessment is optimized to help you eliminate candidates who are not technically qualified for the role, it is not optimized to help you find the best candidate for the role. So the ideal way to use an assessment is to decide a threshold score (typically 55%, we help you benchmark) and invite all candidates who score above the threshold for the next rounds of interview.

What experience level can I use this test for?

Each Adaface assessment is customized to your job description/ ideal candidate persona (our subject matter experts will pick the right questions for your assessment from our library of 10000+ questions). This assessment can be customized for any experience level.

Does every candidate get the same questions?

Yes, it makes it much easier for you to compare candidates. Options for MCQ questions and the order of questions are randomized. We have anti-cheating/ proctoring features in place. In our enterprise plan, we also have the option to create multiple versions of the same assessment with questions of similar difficulty levels.

I'm a candidate. Can I try a practice test?

No. Unfortunately, we do not support practice tests at the moment. However, you can use our sample questions for practice.

What is the cost of using this test?

You can check out our pricing plans.

Can I get a free trial?

Yes, you can sign up for free and preview this test.

I just moved to a paid plan. How can I request a custom assessment?

Here is a quick guide on how to request a custom assessment on Adaface.

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40 min tests.
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