CertNexus CDSP (DSP-210) Certification Sample Questions

CDSP Dumps, DSP-210 Dumps, DSP-210 PDF, CDSP VCE, CertNexus DSP-210 VCE, CertNexus CDSP PDFThe purpose of this Sample Question Set is to provide you with information about the CertNexus Data Science Practitioner exam. These sample questions will make you very familiar with both the type and the difficulty level of the questions on the DSP-210 certification test. To get familiar with real exam environment, we suggest you try our Sample CertNexus CDSP Certification Practice Exam. This sample practice exam gives you the feeling of reality and is a clue to the questions asked in the actual CertNexus Certified Data Science Practitioner (CDSP) certification exam.

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CertNexus DSP-210 Sample Questions:

01. 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
 
02. Which tools or techniques assist in identifying data quality issues?
(Choose two)
a) Data profiling tools
b) Data dictionaries
c) Regular expressions
d) Decision trees
 
03. 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
 
04. 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
 
05. 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
 
06. 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
 
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
c) It eliminates the need for test data
d) It only works with unsupervised algorithms
 
08. 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
 
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
 
10. 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

Answers:

Question: 01
Answer: c		 Question: 02
Answer: a, c		 Question: 03
Answer: a, b		 Question: 04
Answer: d		 Question: 05
Answer: c
Question: 06
Answer: b		 Question: 07
Answer: a		 Question: 08
Answer: c, d		 Question: 09
Answer: a, c		 Question: 10
Answer: d

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