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


"Databricks Certified Professional Data Scientist Exam", also known as Databricks-Certified-Professional-Data-Scientist exam, is a Databricks Certification. With the complete collection of questions and answers, PrepAwayTest has assembled to take you through 140 Q&As to your Databricks-Certified-Professional-Data-Scientist Exam preparation. In the Databricks-Certified-Professional-Data-Scientist exam resources, you will cover every field and category in Databricks Certification Certification helping to ready you for your successful Databricks Certification.

  • Exam Code: Databricks-Certified-Professional-Data-Scientist
  • Exam Name: Databricks Certified Professional Data Scientist Exam
  • Total Questions: 140

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

Certification Vendor:Databricks
Exam Name:Databricks Certified Professional Data Scientist Exam
Exam Number:Databricks-Certified-Professional-Data-Scientist
Exam Duration:120 minutes
Real Exam Qty:59-60
Related Certifications:Databricks Certified Data Engineer Professional
Databricks Certified Machine Learning Professional
Certificate Validity Period:2 years
Exam Format:Multiple-choice, Scenario-based questions, Multiple-select
Available Languages:English
Exam Price:USD $200 (plus applicable taxes)
Passing Score:70%
Recommended Training:Databricks Documentation
Databricks Academy & Free Training
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

Databricks Databricks-Certified-Professional-Data-Scientist Exam Syllabus Topics:

SectionWeightObjectives
Model Development & Algorithms30%- Unsupervised methods: clustering, PCA, anomaly detection
- Tree-based models: decision trees, random forest, gradient boosting
- Spark MLlib and distributed algorithms
- Regression models: linear, logistic, regularized
Data Preparation & Feature Engineering25%- Feature construction, scaling, encoding, and selection
- Using Databricks for data processing
- Data cleaning, transformation, and validation
Model Management & Deployment20%- Model registry and versioning
- Experiment tracking and reproducibility with MLflow
- Model deployment and serving on Databricks
- Monitoring and interpretability
Machine Learning Fundamentals25%- Statistical concepts and bias-variance tradeoff
- Types of machine learning: supervised, unsupervised, reinforcement
- In-sample vs out-of-sample evaluation

Frequently Asked Questions: Databricks Databricks-Certified-Professional-Data-Scientist

120 minutes for 59-60 questions — tighter than it looks once hard items start eating minutes. Train the pace now: assign each question a time budget, flag the stubborn ones, and run full timed sessions in the PrepAwayTest engine so test day feels like practice, not pressure.

Yes:

Courses build the base; measurement finishes the job. Once training ends, the Databricks-Certified-Professional-Data-Scientist practice questions from PrepAwayTest show whether the knowledge actually holds under exam conditions.

4 domains make up the Databricks Certified Professional Data Scientist blueprint, led by Model Management & Deployment (20%), Data Preparation & Feature Engineering (25%), Machine Learning Fundamentals (25%). The weights show where the vendor concentrates its questions — that's where your hours should concentrate too. The complete outline above details every subtopic.

Delivery is automatic: successful payment emails the product to your mailbox within a minute — install on unlimited computers, and if 2 hours pass with nothing (check spam first), contact us. If you fail: sit the corresponding Databricks-Certified-Professional-Data-Scientist exam within 60 days of purchase, then submit a scanned enrollment slip and official Score Report PDF within 2 days of the exam — full refund, processed within 7 days. Excluded: exams taken within 3 days of purchase, name mismatches between candidate and payer, and free or expired products. Alternatively, exchange for two equal-value exam products free and keep your updates.

The Databricks-Certified-Professional-Data-Scientist exam is Databricks's official certification exam for the Databricks Certified Professional Data Scientist credential, positioned at the Professional level. It has never been considered easy — which is precisely why it impresses employers. It also relates to Databricks Certified Machine Learning Professional, Databricks Certified Data Engineer Professional.

Through the vendor's official registration channels:

The exam is available Online proctored via Kryterion or authorized test center — pick the delivery method that suits you at booking.

The enrollment fee is USD $200 (plus applicable taxes) — not cheap — and the passing score is 70%. Since the fee applies in full to every attempt, each retake doubles down on the cost. The smarter sequence: drill the 140 practice questions from PrepAwayTest first, book the exam second.

Yes — hesitating is normal, and the free demo exists for exactly that. Download it, get a feel for the Databricks Certified Professional Data Scientist questions, and decide with confidence. Purchases include 365 days of free updates, with new versions announced by email; after expiry, renew at 50% off.

Recommended: Proficiency in Python/Scala, Apache Spark, Databricks Lakehouse Platform, and practical data science experience; no mandatory prerequisites Rules like these do change — confirm the current criteria on the official exam page (official Databricks-Certified-Professional-Data-Scientist exam page) before registering.

Databricks Certified Professional Data Scientist Sample Questions:

Select the correct option from the below

  • A. If you're not trying to predict a target value, then you need to look into unsupervised learning
  • B. If you're trying to predict or forecast a target value^ then you need to look into supervised learning.
  • C. If the target value can take on a number of values, say any value from 0.00 to 100.00, or -999 to 999: or
    +_to -_, then you need to look unsupervised learning
  • D. If you've chosen supervised learning, with discrete target value like Yes/No. 1/2/3, A/B/C: or Red/Yellow/Black, then look into classification.
  • E. Are you trying to fit your data into some discrete groups? If so and that's all you need, you should look into clustering.
Reveal Solution  Discussion  0

Correct Answer: A,B,D,E  🗳️

Explanation: Only visible for PrepAwayTest members. You can sign-up / login (it's free).

Which of the following technique can be used to the design of recommender systems?

  • A. Collaborative filtering
  • B. Naive Bayes classifier
  • C. Power iteration
  • D. 2 and 3
  • E. 1 and 3
Reveal Solution  Discussion  0

Correct Answer: A  🗳️

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Which analytical method is considered unsupervised?

may have a trend component that is quadratic in nature. Which pattern of data will indicate that the trend in the time series data is quadratic in nature?

  • A. Naive Bayesian classifier
  • B. Decision tree
  • C. Linear regression
  • D. K-means clustering
Reveal Solution  Discussion  0

Correct Answer: D  🗳️

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In which lifecycle stage are test and training data sets created?

  • A. Model planning
  • B. Data preparation
  • C. Model building
  • D. Discovery
Reveal Solution  Discussion  0

Correct Answer: C  🗳️

Explanation: Only visible for PrepAwayTest members. You can sign-up / login (it's free).

You have collected the 100's of parameters about the 1000's of websites e.g. daily hits, average time on the websites, number of unique visitors, number of returning visitors etc. Now you have find the most important parameters which can best describe a website, so which of the following technique you will use

  • A. Linear Regression
  • B. PCA (Principal component analysis)
  • C. Clustering
  • D. Logistic Regression
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

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