Microsoft 365 Certified: Security Administrator Associate certification and Exam MS-500 retiring
As previously announced, we’re retiring the Microsoft 365 Certified: Security Administrator Associate certification and its required Exam MS-500: Microsoft 365 Security Administration, on June 30, 2023. Security-related roles have evolved to deliver solutions in specialized areas—like information protection, operations, or identity management—using multiple technologies. Because of this evolution, this certification no longer has a distinct role. Skills for security administrators are included in certifications like Microsoft Certified: Security Operations Analyst Associate (Exam SC-200: Microsoft Security Operations Analyst), Microsoft Certified: Identity and Access Administrator Associate (Exam SC-300: Microsoft Identity and Access Administrator), and Microsoft Certified: Information Protection Administrator Associate (Exam SC-400: Microsoft Information Protection Administrator), each reflecting the depth of knowledge for its particular specialization. Summary of updates to the Microsoft 365 Certified: Security Administrator Associate certification. The following questions and answers can help you determine how this news impacts your learning journey: I’ve been preparing for Exam MS-500. What are my options? If you’ve been preparing for Exam MS-500, be sure to pass the exam on or before June 30, 2023. I’ve already earned the Microsoft 365 Certified: Security Administrator Associate certification. What happens now? If you’ve already earned the Microsoft 365 Certified: Security Administrator Associate certification, it will stay on your transcript. What about renewal for the Microsoft 365 Certified: Security Administrator Associate certification? Learners with an active Microsoft 365 Certified: Security Administrator Associate certification can renew it for one year (one last time) up to six months after its retirement date of June 30, 2023. I’m considering earning the Microsoft 365 Certified: Security Administrator Associate certification. What should I do? If you’re considering earning the Microsoft 365 Certified: Security Administrator Associate certification, we recommend that you instead consider one or more of these certifications: Microsoft Certified: Security Operations Analyst Associate, Microsoft Certified: Identity and Access Administrator Associate, or Microsoft Certified: Information Protection Administrator Associate.Importing Terraform State in Azure
Some engineers start to provision services manually before they find out this might not be a good thing for the long run. So, they must use Terraform import. If you are using Hashicorp’s Terraform to manage your infrastructure, you can bring existing resources that have been provisioned outside of Terraform. This tuto help you to import Azure resources into a terraform state file. You can do that locally or if you want to initialise the tfstate in a remote local (form a Storage account) So, we are going to import a resource group, a virtual network and a subnet that are created manually; Screenshot from the portal (for the manual resources) First step : create a tf configuration file using manually created resource information (See Screenshot) ***** For you information, we use a tfstate stored remotely in a storage account Second Step : Import Resource details to terraform State After creating the configuration tf file, we can import these resources into it by using the "terraform import" command : terraform import terraform_id azure_resource_id 1- Resource Group : terraform import "azurerm_resource_group.rg_name_auto" "/subscriptions/xxxxxxxxxxxxxxxxxxxxxxxxxxxxxx/resourceGroups/d-210-rg-ado-si-p-to-6" You can find : **The terraform_id **The azure_resource_id 2- The Vnet : terraform import "azurerm_virtual_network.vnet_auto" "/subscriptions/xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxid/resourceGroups/d-210-rg-ado-si-p-to-6/providers/Microsoft.Network/virtualNetworks/d-210-vnet-ado-si-p-to-1" 3-The Subnet : terraform import azurerm_subnet.sub_auto /subscriptions/xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxid/resourceGroups/d-210-rg-ado-si-p-to-6/providers/Microsoft.Network/virtualNetworks/d-210-vnet-ado-si-p-to-1/subnets/d-210-snet-ado-si-p-to-2 So, before use this commands, please : 1- access the code folder : cd folder_code 2- connect to the subscription (where you have deployed the manual resources) : az login Select-AzSubscription -SubscriptionId "copy-past the id of the subsc" 3- Terraform init : terraform init -backend-config storage_account_name=xxxxxxxx -backend-config container_name=tfstate -backend-config resource_group_name=xxxxxxxx -backend-config key=xxxxxxx.tfstate Okey, now we can lunch the commands for import config : RG : Vnet: Subnet : Now, you can see the result : terraform state list you can see the content of each imported resource via the following commands: terraform state show azurerm_resource_group.rg_name_auto terraform state show azurerm_virtual_network.vnet_auto terraform state show azurerm_subnet.sub_auto Third Step : Test by running the terraform plan Finally, we can verify the imported resources in the tfstate file. For example, we can see the imported subnet. The purpose of this tutorial is to know the steps to import resources that are manually configured to the tfstate fileMy Azure Certification Journey...!!
I am pleased to share with you that i have successfully completed all 13 Microsoft azure certifications. It has been rewarding this year and amazing learning journey. Azure 13x Certified..!! here with i would like to share my certification journey which might be useful for your journey. My Azure Certification Journey: The journey has started some time in Jan 2021 during peak pandemic period with azure fundamentals AZ-900. The first attempt was successful in the new year beginning 2021, with this confidence i tried the remaining azure certifications and completed this milestone. it took 8 months for me to complete all 13 certifications. The journey was quite difficult but can be achieved if we put right focus and dedication towards the goal. of course, one need to sacrifice few things to achieve the same. I would like to thank to my company and MS for giving me this wonderful opportunity to enhance my azure skills. Still lots to do and help application teams to accelerate cloud adoption of Azure native solutions by accessing and providing Azure deep experience, proven frameworks, methodologies and architectural designs. https://www.credly.com/users/surya-narayana-reddy-vennapusa/badges Here with i am sharing some frequently asked questions, that will help you to understand more. What is the best way to learn and earn certifications ? The best way to learn the course is first to set a clear goal and try to start clearing one after one. continuous learning and some hands-on would help to achieve the same. MS provides free account and sandbox for practicing yourself during preparation. What are the learning sources/materials and tips ? https://docs.microsoft.com/ there are two ways to prepare ( online-free, instructor led-paid) and many other websites..like udemy, youtube..etc. Discuss with team members on azure topics and make a notes. Which ones are difficult to clear ? Professional certifications, had to clear two exams for solution architect (AZ-303,304) and two for devops (AZ-400). it took longer time to complete it. Did I fail any exam ? Yes. I failed twice before clearing Azure AI exam. The questions were different from what I expected but final attempt was successful. How did I manage the time ? Time management is critical to prep yourself. you need to plan both your work, learning and both are equally important. Spend couple of hours after office hours every day and start clearing modules and make clear notes which can be referred before taking exam. Spend more time over weekends or holidays and do practical sessions using sandbox. Try to prepare/gather some sample questionnaires and attempt before the actual exam. Udemy provide some sample questionnaire Is it mandatory to have hands-on experience ? i would say Yes for professional certifications, Not for fundamentals certifications. I am new to cloud, is it possible for me to shift the career in cloud ? Yes, it is possible. all you need is to have self-interest, dedication, passion towards what you are learning. How much time i need to spend for preparation ? it depends upon person to person, how fast you can grasp, understand concepts and test them in lab, sandbox if you are working , i would say daily 2 to 3 hours ( after office hours) and more time on weekends. What is the best time to attempt the exam ? we need to allocate time for it. i tried most of them during night, ie between 9:30 pm to 10:30 pm you have to maintain a very good discipline until exam is finished. please read do's and don’ts while scheduling the exam. What are the logistics required ? good internet speed without any network outage. good personal laptop/desktop with web cam. Official laptop doesn’t work due to firewall restrictions. you will also get notification from Pearson VUE prior to the exam for system test and other formalities. Do you have hands on experience ? Yes, i do have hands on experience and worked on most of the azure services either in office or sandbox lab training. I am part of cloud migration journey since inception and did bulldozer work to pave the road. Last but not the least: Believe yourself, everything is possible in this world. I strongly believe that there is no shortcut to clear certifications. You have to take it seriously, experience it and clear. can you share certification roadmap ? where to start ? Pl refer below flow diagram and understand where to start. Please fee free to share your comments and like if information is useful. i will respond as soon as i can. Wish you all the best and good luck for your cloud journey.Microsoft Azure Virtual Training Days: Fundamentals
Hello, I am currently preparing for the azure fundamentals certification and i was hoping to participate in a virtual training day in order to get prepared better, but also to acquire the free voucher for the exams. I have noticed that there are no available dates, and that in the specification underneath 'Virtual Training Day: Fundamentals" the text has been changed from "you’ll be eligible to take the Microsoft Azure Fundamentals certification exam at no cost" to you’ll be eligible to take the Microsoft Azure Fundamentals certification exam". Is the acquisition of the free voucher still available? And if so, are there new dates coming out soon?New certification and required exam for Microsoft 365 Certified: Teams Voice Engineer Expert
As previously announced, we’re retiring the Microsoft 365 Certified: Teams Voice Engineer Expert certification and its required Exam MS-720: Microsoft Teams Voice Engineer, on June 30, 2023. The certification will be replaced by the new Microsoft 365 Certified: Collaboration Communications Systems Engineer Associate certification, available now. We’re expanding the definition of the role—the current certification is focused on one technology, but as we carefully examine what organizations are looking for, it’s clear that the role has evolved to include a broader-based set of skills to build and manage solutions across multiple communications technologies, including audiovisual (AV), telephony systems, and team meeting room solutions. To earn the new certification, you’ll need to pass the new Exam MS-721: Collaboration Communications Systems Engineer. Because this certification embraces a wider range of skills rather than deep expertise in a single technology, the level will be associate. The associate-level certification will not require any prerequisites—you need only pass the new exam. Updates to the Microsoft 365 Certified: Teams Voice Engineer Expert certification. The following questions and answers can help you determine how this news impacts your learning journey: Q. I’ve already earned the Microsoft 365 Certified: Teams Voice Engineer Expert certification. What happens now? A. If you’ve already earned the Microsoft 365 Certified: Teams Voice Engineer Expert certification, you’ll see both the original Microsoft 365 Certified: Teams Voice Engineer Expert and the new Microsoft 365 Certified: Collaboration Communications Systems Engineer Associate certifications on the transcript in your profile on Microsoft Learn. In November 2023, the renewal window opens for the new Microsoft 365 Certified: Collaboration Communications Systems Engineer Associate certification. Be sure to pass the renewal assessment before the certification expires, to prove that you’re current on the skills for the new definition of the role. The Microsoft 365 Certified: Teams Voice Engineer Expert certification will stay on your transcript in your profile on Microsoft Learn, and you’ll be able to renew it through December 2023 (extending the expiration for one year). After it expires, it will move to the certification history section of your transcript. Q. I’m preparing for Exam MS-720, and I’ve already earned the Microsoft 365 Certified: Teams Administrator Associate certification that’s required for the Microsoft 365 Certified: Teams Voice Engineer Expert certification. What should I do? A. If you’re preparing for Exam MS-720, and you’ve already earned the Microsoft 365 Certified: Teams Administrator Associate certification, you can continue preparation for Exam MS-720 and take it before it retires in June 2023. If you pass the exam, you’ll earn the Microsoft 365 Certified: Teams Voice Engineer Expert certification and will be granted the new Microsoft 365 Certified: Collaboration Communications Systems Engineer Associate certification. Q. I’m considering earning the Microsoft 365 Certified: Teams Voice Engineer Expert certification but haven’t started yet. What should I do? A. If you’re considering earning the Microsoft 365 Certified: Teams Voice Engineer Expert certification, we recommend that you instead work toward the new Microsoft 365 Certified: Collaboration Communications Systems Engineer Associate certification. You can get started with the skills measured on Exam MS-721 and this curated collection of collaboration communications systems engineer learning paths.The Microsoft Certified: Azure IoT Developer Specialty certification to be retired
As previously announced, we’re retiring the Microsoft Certified: Azure IoT Developer Specialty certification and its required Exam AZ-220: Microsoft Azure IoT Developer, on July 31, 2023, to focus on developing a path more aligned to the way that real-world professionals approach IoT. Skills needed for IoT projects cut across multiple roles, including developer, architect, administrator, and data analyst. The following questions and answers can help you determine how this news impacts your learning journey: Q. I’ve been preparing for Exam AZ-220. What are my options? A. If you’ve been preparing for Exam AZ-220, be sure to pass it on or before July 31, 2023. If you pass Exam AZ-220 on or before July 31, 2023, you’ll earn the Azure IoT Developer Specialty certification and it will be active for one year. Q. I’ve already earned the Azure IoT Developer Specialty certification. What happens now? A. If you’ve already earned the Azure IoT Developer Specialty certification, it will stay on your transcript. Q. What about renewal for the Azure IoT Developer Specialty certification? A. Learners with an active Azure IoT Developer Specialty certification can renew it for one year (one last time) up to six months after its retirement date of July 31, 2023. The renewal window ends on January 31, 2024.The Microsoft 365 Certified: Exchange Online Support Engineer Specialty certification to be retired
As previously announced, we’re retiring the Microsoft 365 Certified: Exchange Online Support Engineer Specialty certification and its required Exam MS-220: Troubleshooting Microsoft Exchange Online on July 31, 2023. We encourage support engineers who are interested in planning, deploying, and troubleshooting the messaging infrastructure in hybrid and cloud enterprise environments to instead consider pursuing the Microsoft 365 Certified: Messaging Administrator Associate certification. Updates to the Microsoft 365 Certified: Exchange Online Support Engineer Specialty certification. The following questions and answers can help you determine how this news impacts your learning journey: Q. I’ve been preparing for Exam MS-220. What are my options? A. If you’ve been preparing for Exam MS-220, be sure to pass it on or before July 31, 2023. If you pass Exam MS-220 on or before July 31, 2023, you’ll earn the Microsoft 365 Certified: Exchange Online Support Engineer Specialty certification and it will be active for one year. Q. I’ve already earned the Microsoft 365 Certified: Exchange Online Support Engineer Specialty certification. What happens now? A. If you’ve already earned the Microsoft 365 Certified: Exchange Online Support Engineer Specialty certification, it will stay on your transcript. Q. What about renewal for the Microsoft 365 Certified: Exchange Online Support Engineer Specialty certification? A. Learners with an active Microsoft 365 Certified: Exchange Online Support Engineer Specialty certification can renew it for one year (one last time) up to six months after its retirement date of July 31, 2023. The renewal window ends on January 31, 2024.
Econometrics models vs machine learning algorithms
Econometrics models and machine learning algorithms are used in data analysis, but they have different approaches and are often applied in distinct contexts. Here's a comparison between econometrics models and machine learning algorithms Econometrics Models: Purpose: Econometrics models are primarily used in economics to study and quantify relationships between economic variables. They are designed to explain and predict economic phenomena based on theoretical and statistical principles. Underlying Assumptions: Econometrics models often rely on strong assumptions about the underlying economic structure. These assumptions are derived from economic theories and may include concepts like linearity, homoscedasticity, and normality. Interpretability: Econometric models are typically designed to be interpretable. The coefficients in these models often have clear economic interpretations, allowing researchers to make sense of the relationships between variables. Causal Inference: Econometrics places a strong emphasis on causal inference. Researchers often aim to establish causal relationships between variables, which involves controlling for potential confounding factors. Data Size: Econometric models have traditionally been applied to smaller datasets, and they are often used when the researcher has a strong theoretical basis for the relationships being studied. Machine Learning Algorithms: Purpose: Machine learning algorithms are used for a broader range of tasks, including prediction, classification, clustering, and pattern recognition. They are applied in various domains beyond economics, such as computer vision, natural language processing, and healthcare. Underlying Assumptions: Machine learning algorithms are less concerned with making strong assumptions about the underlying data distribution or structure. They focus more on learning patterns and relationships directly from the data. Interpretability: Many machine learning algorithms, especially complex ones like deep neural networks, are considered "black-box" models. While efforts are being made to improve interpretability, understanding the internal workings of these models can be challenging. Causal Inference: While some machine learning algorithms can estimate causal relationships, they are often used more for prediction rather than explicitly establishing causation. Techniques like feature importance can provide insights into predictive factors but may not imply causation. Data Size: Machine learning algorithms, especially deep learning models, thrive on large datasets. They can automatically learn complex patterns and relationships from vast amounts of data, which is particularly advantageous in tasks like image recognition and natural language processing. Choosing Between Econometrics Models and Machine Learning Algorithms: Nature of the Problem: Consider the nature of the problem you are trying to solve. If you are interested in understanding causal relationships in economic phenomena and have a solid theoretical foundation, econometrics models may be more appropriate. If the focus is on prediction and dealing with large datasets, machine learning algorithms might be more suitable. Interpretability vs. Performance: If interpretability is crucial and you need to understand the economic mechanisms at play, econometrics models may be preferred. If maximizing predictive performance is the primary goal and interpretability is less critical, machine learning algorithms might be more suitable. Data Availability: The amount and nature of your data can influence the choice between econometrics and machine learning. Econometrics models may be more suitable for smaller datasets with a strong theoretical basis, while machine learning algorithms can excel with large, complex datasets. In practice, there is a growing trend of combining elements from both approaches. Some researchers and practitioners leverage econometric models for causal inference and combine them with machine learning techniques for prediction, creating a hybrid approach that benefits from the strengths of both methodologies. Integration between Econometrics Models and Machine Learning Algorithms: Integrating econometrics models with machine learning algorithms can leverage the strengths of both approaches, allowing for more robust analyses that encompass both causal inference and predictive modeling. Here are some ways in which integration can be achieved: Hybrid Models: Create hybrid models that combine elements of econometrics and machine learning. For example, you could use an econometric model to capture the causal relationships in the data and then incorporate machine learning algorithms to improve predictive accuracy. This approach is often referred to as "econometric-ML hybrid models." Feature Engineering: Use machine learning techniques for feature engineering to enhance the set of variables available for your econometric model. Machine learning can identify complex patterns and relationships in the data, which can then be used as additional features in an econometric model. Variable Selection: Apply machine learning algorithms for variable selection. Some machine learning techniques, such as LASSO (Least Absolute Shrinkage and Selection Operator) or recursive feature elimination, can help identify the most relevant variables for your econometric model. Predictive Analytics with Causal Variables: Integrate causal variables identified in econometric models into machine learning algorithms for predictive analytics. This way, you can incorporate the insights gained from the econometric analysis into the machine learning model. Counterfactual Analysis: Use machine learning to simulate counterfactual scenarios. Econometric models often deal with counterfactual analysis, and machine learning models can assist in generating scenarios or predictions in the absence of specific interventions or changes. Ensemble Methods: Employ ensemble methods that combine predictions from both econometric models and machine learning models. This can be done by averaging or combining the predictions, taking advantage of the complementary strengths of each model. Sequential Modeling: Implement a sequential modeling approach where you first estimate an econometric model and then use the residuals or other relevant information as inputs to a machine learning model. This sequential approach allows you to capture both the linear relationships emphasized in econometrics and the non-linear patterns detected by machine learning. Model Validation and Sensitivity Analysis: Use machine learning techniques for model validation and sensitivity analysis of econometric models. Machine learning can help assess the robustness of econometric results under different conditions and identify potential sources of bias or uncertainty. Time Series Analysis: Combine econometric time series models with machine learning techniques for time series forecasting. Machine learning algorithms, such as recurrent neural networks or Long Short-Term Memory (LSTM) networks, can capture temporal dependencies and patterns in the data that traditional econometric time series models may miss. Transfer Learning: Apply transfer learning techniques to leverage knowledge gained from one domain (e.g., econometrics) and transfer it to improve the performance of a machine learning model in a related domain. Successful integration requires a deep understanding of both econometrics and machine learning, as well as the specific characteristics of the data and problem at hand. It's essential to carefully validate and interpret the results of integrated models to ensure that the combined approach adds value to the analysis.
