Last Updated: Aug 30, 2026
No. of Questions: 140 Questions & Answers with Testing Engine
Download Limit: Unlimited
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| Certification Vendor: | Databricks |
|---|---|
| Exam Name: | Databricks Certified Professional Data Scientist Exam |
| Exam Number: | Databricks-Certified-Professional-Data-Scientist |
| Passing Score: | 70% |
| Real Exam Qty: | 59-60 |
| Certificate Validity Period: | 2 years |
| Related Certifications: | Databricks Certified Data Engineer Professional Databricks Certified Machine Learning Professional |
| Exam Format: | Scenario-based questions, Multiple-choice, Multiple-select |
| Available Languages: | English |
| Exam Price: | USD $200 (plus applicable taxes) |
| Exam Duration: | 120 minutes |
| Recommended Training: | Databricks Academy & Free Training Databricks Documentation |
| Exam Registration: | Kryterion Scheduling Portal Official Exam Page & Registration |
| Sample Questions: | DOWNLOAD DEMO |
| Exam Way: | Online proctored via Kryterion or authorized test center |
| Pre Condition: | Recommended: Proficiency in Python/Scala, Apache Spark, Databricks Lakehouse Platform, and practical data science experience; no mandatory prerequisites |
| Official Syllabus URL: | https://academy.databricks.com/exam/databricks-certified-professional-data-scientist |
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Preparation & Feature Engineering | 25% | - Feature construction, scaling, encoding, and selection - Using Databricks for data processing - Data cleaning, transformation, and validation |
| Topic 2: Model Development & Algorithms | 30% | - Spark MLlib and distributed algorithms - Regression models: linear, logistic, regularized - Unsupervised methods: clustering, PCA, anomaly detection - Tree-based models: decision trees, random forest, gradient boosting |
| Topic 3: Machine Learning Fundamentals | 25% | - In-sample vs out-of-sample evaluation - Statistical concepts and bias-variance tradeoff - Types of machine learning: supervised, unsupervised, reinforcement |
| Topic 4: Model Management & Deployment | 20% | - Experiment tracking and reproducibility with MLflow - Model deployment and serving on Databricks - Model registry and versioning - Monitoring and interpretability |
The Databricks Certified Professional Data Scientist exam (exam code Databricks-Certified-Professional-Data-Scientist) is the official Databricks exam that leads to the Databricks Certified Professional Data Scientist certification, sitting at the Professional level of the Databricks certification track. It is also connected with Databricks Certified Machine Learning Professional, Databricks Certified Data Engineer Professional. If this is the credential you are working toward, the 140 practice questions at Actual4Cert map directly to its objectives.
The Databricks Certified Professional Data Scientist exam includes 59-60 questions, and you have 120 minutes to complete them. Divide the time limit by the question count and you get a tight average pace per item, so train yourself to flag time-consuming questions and return to them later instead of getting stuck. Before test day, run at least one full timed session in the Actual4Cert test engine under the same limits — the clock should never surprise you.
The passing score for Databricks Certified Professional Data Scientist is 70%, and the official registration fee is USD $200 (plus applicable taxes). A failed attempt means paying that fee in full again, so your preparation budget deserves the same attention as your study plan. A practical rule: book your exam date only after you can finish a Actual4Cert practice test comfortably above the passing score more than once.
According to Databricks, candidates should meet the following before registering: Recommended: Proficiency in Python/Scala, Apache Spark, Databricks Lakehouse Platform, and practical data science experience; no mandatory prerequisites. Requirements can change, so confirm the latest details on the official exam page before you register.
You can register for the Databricks Certified Professional Data Scientist exam through the official channels below:
Exam delivery: Online proctored via Kryterion or authorized test center.
Databricks lists the following official training options for this exam:
Formal training builds the theory; pair it with the 140 practice questions from Actual4Cert to find out whether you are genuinely ready for the exam.
Yes. A free PDF demo of the Databricks-Certified-Professional-Data-Scientist practice questions is available to download, so you can judge the format and quality before paying anything. Every purchase also includes 365 days of free updates, and if your product expires you can extend the update service at a 50% discount.
Your order is covered by a conditional 100% money-back guarantee: if you take the corresponding exam within 60 days of purchase and do not pass, you may apply for a full refund. Exams taken within 3 days of purchase are not eligible, nor are free materials or expired orders, and the candidate name must match the payer name. To claim, submit a scanned enrollment slip and your official Score Report PDF within 2 days of the exam; claims are processed within 7 days. Prefer to keep studying? You can instead exchange your purchase for two free products of equal value while keeping the update service on your original one. Delivery itself is immediate: your product unlocks for instant download right after payment and a copy is emailed to you within a minute — if nothing arrives within 2 hours, contact our support team. There is no limit on the number of computers you can install it on.
The Databricks Certified Professional Data Scientist blueprint is divided into 4 major domains, starting with Model Management & Deployment (20%), Data Preparation & Feature Engineering (25%), and Machine Learning Fundamentals (25%). The full breakdown, including every subdomain and its weighting, is listed in the Exam Topics section above — review it against your own weak areas before scheduling the exam.
Question 1
What are the advantages of the mutual information over the Pearson correlation for text classification problems?
A. The mutual information doesn't assume that the variables are normally distributed.
B. The mutual information is easier to parallelize.
C. The mutual information has a meaningful test for statistical significance.
D. The mutual information can signal non-linear relationships between the dependent and independent variables.
Question 2
Which technique you would be using to solve the below problem statement? "What is the probability that individual customer will not repay the loan amount?"
A. Clustering
B. Linear Regression
C. Classification
D. Logistic Regression
E. Hypothesis testing
Question 3
You are working as a data science consultant for a gaming company. You have three member team and all other stake holders are from the company itself like project managers and project sponsored, data team etc.
During the discussion project managed asked you that when can you tell me that the model you are using is robust enough, after which step you can consider answer for this question?
A. Model planning
B. Data Preparation
C. Discovery
D. Model building
E. Operationalize
Question 4
Find out the classifier which assumes independence among all its features?
A. Linear Regression
B. Random forests
C. Neural networks
D. Naive Bayes
Question 5
What is the best way to evaluate the quality of the model found by an unsupervised algorithm like k-means clustering, given metrics for the cost of the clustering (how well it fits the data) and its stability (how similar the clusters are across multiple runs over the same data)?
A. The most stable clustering
B. The lowest cost clustering
C. The lowest cost clustering subject to a stability constraint
D. The most stable clustering subject to a minimal cost constraint
Solutions:
| Question 1 Answer: B | Question 2 Answer: D | Question 3 Answer: D | Question 4 Answer: D | Question 5 Answer: C |
Cathy
Elva
Ina
Lilith
Muriel
Riva
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