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CERTNEXUS DSP-210 STUDY
GUIDE

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CertNexus Certified Data Science Practitioner (CDSP) 1
Introduction to DSP-210 CertNexus Certified
Data Science Practitioner (CDSP) Exam
The CertNexus DSP-210 Exam is challenging and thorough preparation is essential for
success. This exam study guide is designed to help you prepare for the CDSP certification
exam. It contains a detailed list of the topics covered on the Professional exam, as well
as a detailed list of preparation resources. This study guide for the CertNexus Certified
Data Science Practitioner (CDSP) will help guide you through the study process for your
certification.
DSP-210 CertNexus Certified Data Science Practitioner
(CDSP) Exam Summary
● Exam Name: CertNexus Certified Data Science Practitioner (CDSP)
● Exam Code: DSP-210
● Exam Price: $367.50 (USD)
● Duration: 120 mins
● Number of Questions: 90
● Passing Score: 72%
● Reference Books: DSP training
● Schedule Exam: Pearson VUE

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CertNexus Certified Data Science Practitioner (CDSP) 2
● Sample Questions: CertNexus CDSP Sample Questions
● Recommended Practice: CertNexus DSP-210 Certification Practice Exam
Exam Syllabus: DSP-210 CertNexus Certified Data Science
Practitioner (CDSP)
Topic Details
Defining the need to be addressed through the application of data science (7-9%)
Identify the project scope
- Identify project specifications, including
objectives (metrics/KPIs) and stakeholder
requirements
- Identify mandatory deliverables,
optional deliverables
- Determine project timeline
- Identify project limitations (time,
technical, resource, data, risks)
Understand challenges
- Understand terminology
• Milestone
• POC (Proof of concept)
• MVP (Minimal Viable Product)
- Become aware of data privacy, security,
and governance policies
• GDPR
• HIPPA
• California Privacy Act
- Obtain permission/access to
stakeholder data
- Ensure appropriate voluntary disclosure
and informed consent controls in place
Classify a question into a known data science
problem
- Identify references relevant to the data
science problem
• Optimization problem
• Forecasting problem
• Regression problem
• Classification problem
• Segmentation/Clustering problem
- Identify data sources and type
• Structured/unstructured
• Image

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CertNexus Certified Data Science Practitioner (CDSP) 3
Topic Details
• Text
• Numerical
• Categorical
- Select modeling type
• Regression
• Classification
• Forecasting
• Clustering
• Optimization
• Recommender systems
Extracting, Transforming, and Loading Data (17-25%)
Gather data sets
- Read Data
• Write a query for a SQL database
• Write a query for a NoSQL
database
• Read data from/write data to cloud
storage solutions
1. AWS S3
2. Google Storage Buckets
3. Azure Data Lake
- Become aware of first-, second-, and
third-party data sources
• Understand data collection
methods
• Understand data sharing
agreements, where applicable
- Explore third-party data availability
• Demographic data
• Bloomberg
- Collect open-source data
• Use APIs to collect data
• Scrape the web
- Generate data assets
• Dummy or test data
• Randomized data
• Anonymized data
• AI-generated synthetic data

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CertNexus Certified Data Science Practitioner (CDSP) 4
Topic Details
Clean data sets
- Identify and eliminate irregularities in
data (e.g., edge cases, outliers)
• Nulls
• Duplicates
• Corrupt values
- Parse the data
- Check for corrupted data
- Correct the data format
- Deduplicate data
- Apply risk and bias mitigation
techniques
• Understand common forms of ML
bias
1. Sampling bias
2. Measurement bias
3. Exclusion bias
4. Observer bias
5. Prejudicial bias
6. Confirmation bias
7. Bandwagoning
- Identify the sources of bias
• Sources of bias include data
collection, data labeling, data
transformation, data imputation,
data selection, and data training
methods
• Use exploratory data analysis to
visualize and summarize the data,
and detect outliers and anomalies
• Assess data quality by measuring
and evaluating the completeness,
correctness, consistency, and
currency of data
• Use data auditing techniques to
track and document the
provenance, ownership, and
usage of data, and applied data
cleaning steps

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CertNexus Certified Data Science Practitioner (CDSP) 5
Topic Details
- Mitigate the impact of bias
• Apply mitigation strategies such as
data augmentation, sampling,
normalization, encoding, validation
- Evaluate the outcomes of bias
• Use methods such as confusion
matrix, ROC curve, AUC score,
and fairness metrics
- Monitor and improve the data cleaning
process
• Establish or adhere to data
governance rules, standards, and
policies for data and the data
cleaning process
Merge and load data sets
- Join data from different sources
• Make sure a common key exists in
all datasets
• Unique identifiers
- Load data
• Load into DB
• Load into dataframe
• Export the cleaned dataset
• Load into visualization tool
- Make an endpoint or API
Apply problem-specific transformations to
data sets
- Apply word vectorization or word
tokenization
• Word2vec
• TF-IDF
• Glove
- Generate latent representations for
image data
Performing exploratory data analysis (25-36%)
Examine data
- Generate summary statistics
- Examine feature types
- Visualize distributions
- Identify outliers
- Find correlations
- Identify target feature(s)

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CertNexus Certified Data Science Practitioner (CDSP) 6
Topic Details
Preprocess data
- Identify missing values
- Make decisions about missing values
(e.g., imputing method, record removal)
- Normalize, standardize, or scale data
Carry out feature engineering
- Apply encoding to categorical data
• One-hot encoding
• Target encoding
• Label encoding or Ordinal
encoding
• Dummy encoding
• Effect encoding
• Binary encoding
• Base-N encoding
• Hash encoding
- Split features
• Text manipulation
1. Split
2. Trim
3. Reverse
• Manipulate data
• Split names
• Extract year from title
- Convert dates to useful features
- Apply feature reduction methods
• PCA
• t-SNE
• Random forest
• Backward feature elimination
• Forward feature selection
• Factor analysis
• Missing value ratio
• Low-variance filter
• High-correlation filter
• SVD
• False discovery rate
• Feature importance methods
Building models (19-27%)

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CertNexus Certified Data Science Practitioner (CDSP) 7
Topic Details
Prepare data sets for modeling
- Decide proportion of data set to use for
training, testing, and (if applicable)
validation
- Split data to train, test, and (if
applicable) validation sets, mitigating
data leakage risk
Train models
- Define models to try
• Regression
1. Linear regression
2. Random forest
3. XGBoost
- Classification
• Logistic regression
• Random forest classification
• XGBoost classifier
• naïve Bayes
- Forecasting
• ARIMA
- Clustering
• k-means
• Density-based methods
• Hierarchical clustering
- Train model or pre-train or adapt
transformers
- Tune hyper-parameters, if applicable
• Cross-validation
• Grid search
• Gradient decent
• Bayesian optimization
Evaluate models
- Define evaluation metric
- Compare model outputs
• Confusion matrix
• Learning curve
- Select best-performing model
- Store model for operational use
• MLflow
• Kubeflow
Testing models (4-7%)

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CertNexus Certified Data Science Practitioner (CDSP) 8
Topic Details
Test hypotheses
- Design A/B tests
• Experimental design
1. Design use cases
2. Test creation
3. Statistics
- Define success criteria for test
- Evaluate test results
Operationalizing the pipeline (5-8%)
Deploy pipelines
- Build streamlined pipeline (using dbt,
Fivertran, or similar tools)
- Implement confidentiality, integrity, and
access control measures
- Put model into production
• AWS SageMaker
• Azure ML
• Docker
• Kubernetes
- Ensure model works operationally
- Monitor pipeline for performance of
model over time
• MLflow
• Kubeflow
• Datadog
- Consider enterprise data strategy and
data management architecture to
facilitate the end-to-end integration of
data pipelines and environments
• Data warehouse and ETL process
• Data lake and ETL processes
• Data mesh, micro-services, and
APIs
• Data fabric, data virtualization, and
low-code automation platforms
Communication findings (4-7%)
Report findings
- Implement model in a basic web
application for demonstration (POC
implementation)
• Web frameworks (Flask, Django)

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CertNexus Certified Data Science Practitioner (CDSP) 9
Topic Details
• Basic HTML
• CSS
- Derive insights from findings
- Identify features that drive outcomes
(e.g., explainability, interpretability,
variable importance plot)
- Show model results
- Generate lift or gain chart
- Ensure transparency and explainability
of model
• Use explainable methods (e.g.,
intrinsic and post hoc)
1. Visualization
2. Feature importance analysis
3. Attention mechanisms
4. Avoiding black-box techniques
in model design
5. Explainable AI (XAI)
frameworks and tools
- SHAP
- LIME
- ELI5
- What-If Tool
- AIX360
- Skater
- Et al
- Document the model lifecycle
• ML design and workflow
• Code comments
• Data dictionary
• Model cards
• Impact assessments
- Engage with diverse perspectives
• Stakeholder analysis
• User testing
• Feedback loops
- Participatory design

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CertNexus Certified Data Science Practitioner (CDSP) 10
Topic Details
Democratize data
- Make data more accessible to a wider
range of stakeholders
- Make data more understandable and
actionable for nontechnical individuals
• Implement self-service
data/analytics platforms
- Create a culture of data literacy
• Educate employees on how to use
data effectively
• Offer support and guidance on
data-related issues
• Promote transparency and
collaboration around data
CertNexus DSP-210 Certification Sample Questions and
Answers
To make you familiar with CertNexus Certified Data Science Practitioner (CDSP) (DSP-
210) certification exam structure, we have prepared this sample question set. We
suggest you to try our Sample Questions for CDSP DSP-210 Certification to test your
understanding of CertNexus DSP-210process with real CertNexus certification exam
environment.
DSP-210 CertNexus Certified Data Science Practitioner (CDSP)
Sample Questions:
01. Why is it important to define hypotheses before analyzing data?
a) To reduce model training time
b) To prevent overfitting in the model
c) To maximize prediction accuracy
d) To minimize confirmation bias and data dredging
Answer: d
02. Which tools or techniques assist in identifying data quality issues?
(Choose two)
a) Data profiling tools
b) Data dictionaries

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CertNexus Certified Data Science Practitioner (CDSP) 11
c) Regular expressions
d) Decision trees
Answer: a, c
03.What challenges are commonly faced when working with real-time streaming
data?
(Choose two)
a) High latency during retrieval
b) Schema evolution in SQL tables
c) Handling data arrival out of order
d) Inability to batch process
Answer: c, d
04. Which best practices support responsible data democratization in large
organizations?
(Choose two)
a) Creating data dictionaries and metadata catalogs
b) Enabling audit logs to track data access
c) Allowing unrestricted data modification
d) Using machine learning to anonymize business reports
Answer: a, b
05. When presenting findings to a mixed audience of technical and non-technical
stakeholders, which approach is most effective?
a) Focus entirely on code-level insights
b) Include separate tracks for technical and business interpretations
c) Use only advanced metrics such as F1 or AUC
d) Skip visuals in favor of equations
Answer: b
06. What is a major risk of generating too many polynomial interaction features?
a) Reduced data normalization
b) Increased interpretability
c) Model underfitting
d) Overfitting due to increased complexity
Answer: d
07. Why is cross-validation often preferred over a single train/test split?
a) It provides a more robust estimate of model performance
b) It reduces the training time significantly

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CertNexus Certified Data Science Practitioner (CDSP) 12
c) It eliminates the need for test data
d) It only works with unsupervised algorithms
Answer: a
08. In A/B testing, which metric is most directly used to decide whether to accept
or reject the null hypothesis?
a) Conversion rate
b) Confidence interval
c) P-value
d) Recall
Answer: c
09. Which data preprocessing steps are critical before training most machine
learning models?
(Choose two)
a) Removing duplicate records
b) Applying SMOTE before EDA
c) Converting text fields into numeric values
d) Increasing the learning rate
Answer: a, c
10. Why is monitoring a deployed model important after production rollout?
a) To improve model visualization
b) To track UI engagement metrics
c) To detect performance drift and data quality issues
d) To optimize batch processing speed
Answer: c