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Databricks Databricks-Certified-Professional-Data-Scientist Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Model Development & Algorithms | 30% | - Tree-based models: decision trees, random forest, gradient boosting - Regression models: linear, logistic, regularized - Unsupervised methods: clustering, PCA, anomaly detection - Spark MLlib and distributed algorithms |
| Model Management & Deployment | 20% | - Monitoring and interpretability - Experiment tracking and reproducibility with MLflow - Model registry and versioning - Model deployment and serving on Databricks |
| Data Preparation & Feature Engineering | 25% | - Data cleaning, transformation, and validation - Using Databricks for data processing - Feature construction, scaling, encoding, and selection |
| Machine Learning Fundamentals | 25% | - In-sample vs out-of-sample evaluation - Statistical concepts and bias-variance tradeoff - Types of machine learning: supervised, unsupervised, reinforcement |
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Databricks points candidates toward these official training options:
Combine a course that fits your schedule with regular question practice for the steadiest progress.
Databricks sets the following prerequisites for the Databricks Certified Professional Data Scientist: Recommended: Proficiency in Python/Scala, Apache Spark, Databricks Lakehouse Platform, and practical data science experience; no mandatory prerequisites.
Confirm the current requirements on the official certification page before booking.
The Databricks Certified Professional Data Scientist blueprint is organized into these main domains:
- Model Management & Deployment (20%)
- Data Preparation & Feature Engineering (25%)
- Model Development & Algorithms (30%)
Further domains complete the official outline, and our question bank spans all of them.
Registration runs through the official channels listed here:
Choose a test center or online appointment that fits your calendar, and book ahead to lock in your preferred date.
We take responsibility for our materials. If you fail the corresponding exam within 60 days of purchase, send us a scanned copy of your enrollment slip and your official Score Report PDF within two days of the exam date, and we will process a full refund within seven days of verification. Please note the exclusions: exams taken within three days of purchase are not eligible, the candidate name must match the payer, and free or expired products are not covered. If you would rather switch direction, we can exchange your product for two others of equal value instead.
According to the latest exam information, the Databricks-Certified-Professional-Data-Scientist exam contains 59-60 questions and runs for 120 minutes minutes. Timing yourself during practice sessions is a smart habit — pacing problems sink more candidates than knowledge gaps.
At present, the passing score for the Databricks-Certified-Professional-Data-Scientist exam is 70% and the registration fee is USD $200 (plus applicable taxes). Both figures are set by Databricks and can change, so double-check them on the official site when you register.
Databricks Certified Professional Data Scientist Sample Questions:
Which of the following question statement falls under data science category?
- A. What happened in last six months?
- B. How many products have been sold in a last month?
- C. Where is a problem for sales?
- D. Which is the optimal scenario for selling this product?
- E. What happens, if these scenario continues?
Correct Answer: D,E 🗳️
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You are building a classifier off of a very high-dimensiona data set similar to shown in the image with 5000 variables (lots of columns, not that many rows). It can handle both dense and sparse input. Which technique is most suitable, and why?
- A. Logistic regression with L1 regularization, to prevent overfitting
- B. Naive Bayes, because Bayesian methods act as regularlizers
- C. Random forest because it is an ensemble method
- D. k-nearest neighbors, because it uses local neighborhoods to classify examples
Correct Answer: A 🗳️
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You are doing advanced analytics for the one of the medical application using the regression and you have two variables which are weight and height and they are very important input variables, which cannot be ignored and they are also highly co-related. What is the best solution for that?
- A. You will take cube root of height
- B. You will take square root of weight
- C. You would consider using BMI (Body Mass Index)
- D. You will take square of the height.
Correct Answer: C 🗳️
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Select the correct statement regarding the naive Bayes classification
- A. Independent variables can be assumed
- B. only the variances of the variables for each class need to be determined
- C. for each class entire covariance matrix need to be determined
- D. it only requires a small amount of training data to estimate the parameters
Correct Answer: A,B,D 🗳️
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Question-3: In machine learning, feature hashing, also known as the hashing trick (by analogy to the kernel trick), is a fast and space-efficient way of vectorizing features (such as the words in a language), i.e., turning arbitrary features into indices in a vector or matrix. It works by applying a hash function to the features and using their hash values modulo the number of features as indices directly, rather than looking the indices up in an associative array. So what is the primary reason of the hashing trick for building classifiers?
- A. It reduces the non-significant features e.g. punctuations
- B. It requires the lesser memory to store the coefficients for the model
- C. It creates the smaller models
- D. Noisy features are removed
Correct Answer: B 🗳️
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