- Identity Check: Which CDSP Is This?
- DSP-210 at a Glance
- Domain Weights on One Page
- Domain 3: Exploratory Data Analysis Essentials
- Domain 2: ETL Facts to Memorize
- Domain 4: Building Models
- The Four Small Domains
- Fees, Booking and Renewal
- What the Sources Don't Settle
- A Domain-Ordered Review Sequence
- Frequently Asked Questions
- CDSP here means Certified Data Science Practitioner from CertNexus, exam code DSP-210, delivered through Pearson VUE.
- The exam has 90 questions: 75 scored and 15 unscored, so you cannot tell which items count.
- Exploratory data analysis is the heaviest domain at 25-36%; ETL and model building follow.
- The passing standard is 72% or 69% depending on the equated form you receive.
Identity Check: Which CDSP Is This?
The acronym CDSP is shared by several unrelated credentials, so confirm you are studying the right one before you memorize anything. This cheat sheet covers Certified Data Science Practitioner, issued by CertNexus, with exam code DSP-210. If your study material mentions other certifying bodies, unrelated fee schedules, or topics outside data science, it belongs to a different credential and will mislead you.
If you are still orienting yourself, the explainers on what CDSP certification is and what CDSP stands for cover the naming question in more depth. This page is the compressed, exam-week version: the facts worth re-reading the night before.
DSP-210 at a Glance
These are the logistics and format facts that belong on the top of your one-page review.
| Item | Fact |
|---|---|
| Certifying body | CertNexus |
| Exam code | DSP-210 |
| Delivery | Pearson VUE test center or OnVUE online proctoring |
| Total questions | 90 (75 scored, 15 unscored) |
| Appointment length | Two hours, including a 5-minute agreement and 5-minute tutorial (about 110 minutes left for questions) |
| Passing standard | 72% or 69%, depending on the equated form |
| Prerequisites | None formal; programming, statistics and data-handling competence recommended |
| Validity | 3 years |
| Launch | DSP-210 launched September 2024; blueprint v1.6 modified February 3, 2025 |
| Predecessor | DSP-110 retired February 28, 2025 |
The roughly 110 minutes of working time across 90 questions averages out to a little over a minute per item. That pace favors candidates who can recognize a standard workflow step quickly and save extra time for scenario-style questions. For a fuller discussion of format and pacing, see our CDSP study guide.
Domain Weights on One Page
CertNexus publishes the weights as ranges, not fixed percentages, so treat them as emphasis guides rather than exact question counts.
| Domain | Name | Weight |
|---|---|---|
| 1 | Defining the need to be addressed through the application of data science | 7-9% |
| 2 | Extracting, Transforming, and Loading Data | 17-25% |
| 3 | Performing exploratory data analysis | 25-36% |
| 4 | Building models | 19-27% |
| 5 | Testing models | 4-7% |
| 6 | Operationalizing the pipeline | 5-8% |
| 7 | Communicating findings | 4-7% |
Domains 2, 3 and 4 carry the bulk of the exam. Even at the low ends of their ranges, those three account for a clear majority of the content, so they deserve the majority of your review hours. The deeper breakdown lives in the complete guide to all seven CDSP domains.
Domain 3: Exploratory Data Analysis Essentials
Performing exploratory data analysis is the largest domain at 25-36%. It rewards candidates who can look at a dataset and decide what to do next, not just recite definitions.
Domain 3: Performing exploratory data analysis
Expect scenario questions that hand you a data situation and ask for the most appropriate next analytical step.
- Summary statistics: when mean versus median is the safer description of center, and what spread measures tell you about a variable
- Distribution shape: recognizing skew, outliers and multimodality, and what each implies for later modeling
- Visual choices: matching chart type to question (distribution, relationship, comparison, composition over categories)
- Missing data: spotting patterns in missingness and choosing between deletion and imputation with a rationale
- Relationships between variables: correlation versus causation, and why a correlation coefficient alone can mislead
- Feature preparation hints surfaced by EDA: scaling needs, skewed variables, redundant or highly correlated predictors
A useful mental model: every EDA question is really asking "what does this evidence justify doing, and what does it not?" Answers that overreach, such as claiming causation from a scatterplot, are classic distractors.
Domain 2: ETL Facts to Memorize
Extracting, Transforming, and Loading Data runs 17-25%. This domain tests whether you can get raw data into a usable, trustworthy state.
Domain 2: Extracting, Transforming, and Loading Data
Think in pipeline stages and in data-quality consequences.
- Extraction from varied sources: files, databases and other structured or semi-structured inputs, and the format quirks each brings
- Transformation: cleaning, type conversion, deduplication, joining and reshaping, plus encoding categorical variables
- Handling inconsistent values, formats and units before analysis begins
- Loading: where cleaned data lands and how it stays reproducible
- Data quality checks: validating row counts, ranges and key integrity after each step
Domain 4: Building Models
Building models carries 19-27%, making it the second or third largest block depending on where the ranges land. Focus on matching problem type to technique and on understanding what each choice trades off.
Domain 4: Building models
Know the problem framing first, then the algorithm family.
- Supervised versus unsupervised learning, and regression versus classification framing
- Training, validation and test splits, and why each exists
- Overfitting and underfitting, with the bias-variance trade-off as the explanation
- Regularization and feature selection as responses to overfitting
- Common algorithm families and when each fits: linear and logistic models, tree-based methods, clustering approaches
- Hyperparameter tuning and cross-validation as ways to select models without touching the final test set
The recommended background of programming, statistics and data-handling competence shows up most here. You do not need to derive algorithms, but you should be able to reason about why a model underperforms and what to change. The difficulty discussion in how hard the CDSP exam is explains where candidates tend to struggle.
The Four Small Domains
Domains 1, 5, 6 and 7 together are small individually, but together they represent a meaningful slice of the exam. They are also where many technically strong candidates lose easy points by skipping them.
Domain 1: Defining the need (7-9%)
- Translate a business problem into a data science problem with a measurable objective
- Decide whether data science is even appropriate, and identify what data and success criteria are needed
- Recognize stakeholder requirements, constraints and risks up front
Domain 5: Testing models (4-7%)
- Pick evaluation metrics that match the problem: accuracy can mislead on imbalanced classes, so know precision, recall and related measures
- Read a confusion matrix and understand error trade-offs
- Use held-out data and validation techniques to estimate real-world performance
Domain 6: Operationalizing the pipeline (5-8%)
- Move a validated model into a repeatable, automated workflow
- Monitor for drift and degradation after deployment
- Think about maintenance, versioning and retraining triggers
Domain 7: Communicating findings (4-7%)
- Tailor the message and visuals to the audience
- Present results, limitations and recommendations honestly, without overstating certainty
- Tie conclusions back to the original business need from Domain 1
Key Takeaway
Domains 1 and 7 bookend the lifecycle: the exam expects you to start from a business need and end by communicating results against it. Questions in these domains often reward the answer that stays closest to the stakeholder's actual goal.
Fees, Booking and Renewal
The cost facts are simple, and worth having at your fingertips.
| Option | Price (USD) |
|---|---|
| Exam voucher | $367.50 |
| Optional digital guide | $103.95 |
| Guide plus voucher bundle | $424.31 |
| Renewal fee (with 90 CECs) | $150 |
Register through Pearson VUE and choose either a test center or OnVUE online proctoring. The exam is closed-book and proctored. There are no formal prerequisites or mandatory training, which makes the voucher the main hard requirement. A full line-by-line view is in the CDSP certification cost breakdown, and eligibility details are covered in CDSP requirements.
Renewal in one sentence
The certification lasts 3 years. Before it expires, you can renew by earning 90 continuing education credits and paying $150, or by passing the current examination. Put your expiry date in a calendar the day you pass.
What the Sources Don't Settle
A responsible cheat sheet flags uncertainty instead of papering over it. A few details are unresolved in the public information, so verify them directly with CertNexus or Pearson VUE before test day.
Also note that the passing standard is stated as 72% or 69% depending on the equated form. That is a cut score, not a pass rate; it tells you how many scored items you need to get right, not how many candidates succeed. If you are trying to understand what the number means in practice, see CDSP passing score. For the pass-rate question specifically, the CDSP pass rate analysis explains why no trustworthy figure is available to quote.
A Domain-Ordered Review Sequence
If you only have a few weeks, sequence your review by weight and by dependency, since later domains build on earlier ones. This is one possible ordering, not an official plan.
ETL and data quality (Domain 2)
- Cleaning, joining, reshaping, encoding
- Order-of-operations and leakage traps
Exploratory data analysis (Domain 3)
- Distributions, outliers, missing data, visual choice
- Correlation versus causation scenarios
Building and testing models (Domains 4 and 5)
- Problem framing, overfitting, validation strategy
- Metrics matched to the problem, confusion matrix reading
Lifecycle bookends and full practice (Domains 1, 6, 7)
- Business framing, deployment, monitoring, communication
- Timed practice sets under the roughly 110-minute constraint
Putting ETL before EDA mirrors how real projects flow and how the exam scenarios are written. After the content pass, shift to timed practice. You can try questions in the style of the real exam on the CDSP practice test site, and the broader CDSP training overview covers preparation resources.
Where the Credential Fits
The certification is aimed at practitioners who work across the whole data science workflow rather than a single narrow tool. That makes it relevant for analysts moving toward modeling roles and for engineers who need to speak credibly about the analytical side. For the career angle, the CDSP jobs overview and the analysis of whether the certification is worth it are good next reads. Pay and market positioning are covered separately in the CDSP salary guide. Practice questions are available any time at the main practice test.
Frequently Asked Questions
The exam has 90 questions in total: 75 are scored and 15 are unscored. The unscored items are not identified, so answer every question as though it counts.
Performing exploratory data analysis (Domain 3) is the largest at 25-36%. Building models (19-27%) and Extracting, Transforming, and Loading Data (17-25%) follow. The weights are published as ranges, so exact question counts vary.
The passing standard is either 72% or 69%, depending on which equated form you receive. This is a cut score and should not be confused with a pass rate.
The exam voucher is $367.50. An optional digital guide is $103.95, and the guide-plus-voucher bundle is $424.31. The certification is valid for 3 years and can be renewed with 90 CECs and $150, or by passing the current examination before it expires.
There are no formal prerequisites or mandatory training, though programming, statistics and data-handling competence are recommended. You can test at a Pearson VUE center or through OnVUE online proctoring; the exam is closed-book and proctored. Check current scheduling details before you book.