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Databricks Databricks-Certified-Professional-Data-Scientist Exam Topics, Blueprint and Syllabus

Databricks Certified Professional Data Scientist Exam

Last Update May 8, 2024
Total Questions : 138

Our Databricks Certification Databricks-Certified-Professional-Data-Scientist exam questions and answers cover all the topics of the latest Databricks Certified Professional Data Scientist Exam exam, See the topics listed below. We also provide Databricks Databricks-Certified-Professional-Data-Scientist exam dumps with accurate exam content to help you prepare for the exam quickly and easily. Additionally, we offer a range of Databricks Databricks-Certified-Professional-Data-Scientist resources to help you understand the topics covered in the exam, such as Databricks Certification video tutorials, Databricks-Certified-Professional-Data-Scientist study guides, and Databricks-Certified-Professional-Data-Scientist practice exams. With these resources, you can develop a better understanding of the topics covered in the exam and be better prepared for success.

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Databricks Databricks-Certified-Professional-Data-Scientist Exam Overview :

Exam Name Databricks Certified Professional Data Scientist Exam
Exam Code Databricks-Certified-Professional-Data-Scientist
Actual Exam Duration The duration of the Databricks Databricks-Certified-Professional-Data-Scientist Exam is 3 hours.
What exam is all about The Databricks-Certified-Professional-Data-Scientist exam is a certification exam that tests the knowledge and skills of data scientists in using Databricks to perform data analysis, machine learning, and other data-related tasks. The exam covers topics such as data preparation, data exploration, feature engineering, model training and evaluation, and deployment. It is designed to validate the proficiency of data scientists in using Databricks to solve real-world data problems. Passing this exam demonstrates that a data scientist has the necessary skills and knowledge to work with Databricks and can be trusted to deliver high-quality data solutions.
Passing Score required It is recommended to check the official website of Databricks or contact their support team for the latest information on the passing score.
Competency Level required According to Databricks, the exam is designed for data scientists who have experience using Databricks to solve real-world problems. The exam covers topics such as data preparation, feature engineering, model training, and deployment. It is recommended that candidates have a strong understanding of machine learning concepts and programming skills in Python or Scala.
Questions Format Based on the exam objectives and format, the exam is likely to have a mix of multiple-choice, true/false, and scenario-based questions. The questions will test the candidate's knowledge and skills in various areas, including data exploration and visualization, machine learning, data engineering, and data analysis. The exam may also include coding challenges and practical exercises to assess the candidate's ability to apply their knowledge to real-world scenarios.
Delivery of Exam According to the official Databricks website, the exam is currently available in a remote proctored format, which means that candidates can take the exam from their own computer, with a proctor monitoring the exam remotely. The exam is also timed and consists of multiple-choice questions, coding challenges, and data analysis problems.
Language offered The Databricks-Certified-Professional-Data-Scientist Exam is offered in English language.
Cost of exam You can visit the official website of Databricks or contact their customer support team to get the latest pricing information.
Target Audience The Databricks-Certified-Professional-Data-Scientist certification is designed for data scientists who want to demonstrate their expertise in using Databricks to build and deploy machine learning models at scale. The target audience for this certification includes: 1. Data scientists who are familiar with machine learning concepts and want to learn how to use Databricks to build and deploy models. 2. Data engineers who want to expand their knowledge of machine learning and learn how to use Databricks to build and deploy models. 3. Data analysts who want to learn how to use Databricks to build and deploy machine learning models. 4. IT professionals who want to learn how to use Databricks to build and deploy machine learning models. 5. Anyone who wants to demonstrate their expertise in using Databricks to build and deploy machine learning models at scale.
Average Salary in Market The average salary of a Databricks Certified Professional Data Scientist may vary depending on several factors such as location, experience, industry, and job role. According to Glassdoor, the average salary for a data scientist in the United States is around $113,000 per year. However, having a Databricks certification may increase your chances of getting a higher salary.
Testing Provider You can visit the official Databricks website to learn more about the certification and how to register for the exam. Additionally, there are various online training providers that offer courses and practice exams to help you prepare for the certification exam.
Recommended Experience According to the Databricks website, the recommended experience for the Databricks-Certified-Professional-Data-Scientist exam includes: 1. Experience with data manipulation and analysis using SQL, Python, and/or R. 2. Experience with machine learning algorithms and techniques. 3. Experience with distributed computing and big data technologies such as Apache Spark. 4. Experience with data visualization and reporting tools. 5. Familiarity with cloud computing platforms such as AWS, Azure, or Google Cloud Platform. 6. Experience with data engineering and data pipeline development. 7. Familiarity with software development practices and version control systems such as Git. It is important to note that these are only recommendations, and individuals with different backgrounds and experiences may still be able to pass the exam with sufficient preparation and study.
Prerequisite Based on the information available on the official Databricks website, the prerequisites for this certification may include a strong understanding of data science concepts, proficiency in programming languages such as Python or R, experience with data manipulation and analysis using tools such as SQL and Pandas, and familiarity with machine learning algorithms and techniques. It is recommended to review the official exam guide and training resources provided by Databricks for more detailed information on the prerequisites and exam content.
Retirement (If Applicable) It is recommended to check with Databricks or their official website for the latest updates on the exam.
Certification Track (RoadMap): The certification track or roadmap for the Databricks Certified Professional Data Scientist exam includes the following steps: 1. Preparation: Candidates should have a strong understanding of data science concepts, programming languages such as Python and SQL, and experience with machine learning frameworks such as TensorFlow or PyTorch. 2. Registration: Candidates can register for the exam on the Databricks website and pay the exam fee. 3. Exam: The exam consists of multiple-choice questions and coding challenges that test the candidate's knowledge of data science concepts, programming languages, and machine learning frameworks. 4. Certification: Candidates who pass the exam receive the Databricks Certified Professional Data Scientist certification, which demonstrates their expertise in data science and machine learning using Databricks. 5. Continuing Education: To maintain their certification, candidates must complete continuing education requirements, such as attending Databricks training courses or participating in industry conferences.
Official Information https://academy.databricks.com/exam/databricks-certified-professional-data-scientist
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Databricks Databricks-Certified-Professional-Data-Scientist Exam Topics :

Section Weight Objectives
A complete understanding of the basics of machine learning  
  • bias-variance tradeoff
  • in-sample vs. out-of sample data
  • categories of machine learning
  • applied statistics concepts
A intermediate understanding of the steps in the machine learning lifecycle  
  • data preparation
  • feature engineering
  • model training, selection, and production
  • interpreting models
A complete understanding of basic machine learning algorithms and techniques  
  • linear, logistic, and regularized regression
  • tree-based models like decision trees, random forest and gradient boosted trees
  • unsupervised techniniques like K-means and PCA
  • specific algorithms like ALS for recommendation and isolation forests for outlier detection
A complete understanding of the basics of machine learning model management  
  • logging and model organization with MLflow