What should be My Next Step After Microsoft DP-100 Exam?
I am glad to share that I have successfully passed the DP-100 – Designing and Implementing a Data Science Solution on Azure exam. Preparing for this certification was a challenging journey, as it required deep knowledge of data science and machine learning workloads on Azure, along with expertise in areas such as model training, pipeline implementation, deployment, and using language models for AI applications.
This achievement has definitely strengthened my skills and knowledge in Azure Machine Learning, MLflow, and Azure AI services. The exam was not easy, but with the right preparation resources I was able to build the confidence needed. In particular, the Microsoft DP-100 practice test by p2pexams.com played an important role in my preparation by helping me better understand key exam concepts and identify areas where I needed improvement.
Now that I have reached this milestone, I am looking forward to the next steps. What would you suggest I focus on next to further enhance my career in the field of data science and AI?
This achievement has definitely strengthened my skills and knowledge in Azure Machine Learning, MLflow, and Azure AI services. The exam was not easy, but with the right preparation resources I was able to build the confidence needed. In particular, the Microsoft DP-100 practice test by p2pexams.com played an important role in my preparation by helping me better understand key exam concepts and identify areas where I needed improvement.
Now that I have reached this milestone, I am looking forward to the next steps. What would you suggest I focus on next to further enhance my career in the field of data science and AI?
1 answers ( 0 marked as helpful)
Super learner
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Congrats on passing the DP-100! To continue advancing your career in data science and AI, here are some key next steps:
Specialize: Dive deeper into areas like deep learning, NLP, or MLOps to become an expert in a specific subdomain.
Certifications: Consider additional certifications like AI-102 (Azure AI Engineer) or Google Cloud Professional Data Engineer to broaden your skill set.
Hands-on Projects: Build real-world, end-to-end projects and contribute to open-source projects or Kaggle competitions to enhance your portfolio.
Stay Updated & Network: Join AI communities, attend webinars, and share your knowledge on blogs or YouTube to build your personal brand.
Explore Advanced Topics: Delve into reinforcement learning, AI ethics, or quantum machine learning if you’re interested in cutting-edge tech.
Career Growth: Update your LinkedIn profile, seek mentorship, and explore job opportunities to apply your new skills.
Specialize: Dive deeper into areas like deep learning, NLP, or MLOps to become an expert in a specific subdomain.
Certifications: Consider additional certifications like AI-102 (Azure AI Engineer) or Google Cloud Professional Data Engineer to broaden your skill set.
Hands-on Projects: Build real-world, end-to-end projects and contribute to open-source projects or Kaggle competitions to enhance your portfolio.
Stay Updated & Network: Join AI communities, attend webinars, and share your knowledge on blogs or YouTube to build your personal brand.
Explore Advanced Topics: Delve into reinforcement learning, AI ethics, or quantum machine learning if you’re interested in cutting-edge tech.
Career Growth: Update your LinkedIn profile, seek mentorship, and explore job opportunities to apply your new skills.