CertNexus CDSP (DSP-210) Certification Sample Questions
The 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.
These sample questions are simple and basic questions that represent likeness to the real CertNexus DSP-210 exam questions. To assess your readiness and performance with real time scenario based questions, we suggest you prepare with our Premium CertNexus CDSP Certification Practice Exam. When you solve real time scenario based questions practically, you come across many difficulties that give you an opportunity to improve.
CertNexus DSP-210 Sample Questions:
01. Before building a binary classifier for equipment failure, an analyst tabulates the target column and finds 1,850 failures among 210,000 records.
Why is establishing this proportion an essential part of examining the data?
a) It confirms how many records are available for training and for evaluating the model
b) It indicates which features will be most predictive of failure
c) It determines whether accuracy will be meaningful and whether the split needs stratifying
d) It establishes whether the target column contains any missing or out-of-range values
02. Several numeric features must be fed to a neural network whose input layer expects values within a fixed bounded interval. The features are on very different scales, and none contains extreme values.
Which rescaling method fits this requirement?
a) Min-max normalization, which maps each feature onto a fixed bounded interval
b) Standardization, which centers each feature at mean 0 with unit variance
c) Rank transformation, replacing each value with its position in the sorted column
d) A log transformation applied to each feature
03. In a credit dataset the column recording prior bankruptcy details is blank for most applicants, and analysis shows the blank rows have a markedly different default rate from the populated ones. The team intends to impute the column.
What should accompany the imputation?
a) A second imputation using a different method, averaged with the first
b) A binary indicator column marking which rows were originally missing
c) A note in the data dictionary recording the imputation method and date
d) A weighting scheme that reduces the influence of the imputed rows during training
04. Two variables in a sensor dataset rise together consistently, but the relationship curves — each additional unit of the first is associated with a progressively smaller increase in the second. Pearson correlation reports a moderate value that the analyst believes understates the association.
Which measure better captures the relationship?
a) Pearson correlation computed after removing the extreme observations
b) The coefficient of determination obtained from a simple linear regression of one variable on the other
c) Spearman rank correlation measuring monotonic rather than linear association
d) Covariance between the two variables in their original units
05. A researcher has 30 questionnaire items believed to reflect a few underlying attitudes that cannot be measured directly. A colleague suggests principal component analysis; the researcher chooses factor analysis instead.
What distinguishes the two for this purpose?
a) Factor analysis requires standardized inputs, while principal component analysis does not
b) Factor analysis retains all of the original items, while principal component analysis discards the weakest of them
c) Factor analysis produces uncorrelated outputs, while principal component analysis does not
d) Factor analysis models the items as arising from latent constructs, while components are combinations of the items
06. Geolocation records of reported incidents form elongated groupings that follow road corridors, together with scattered isolated points across the map. The team does not know in advance how many groupings exist and wants the isolated points left unassigned.
Which clustering approach fits these requirements?
a) k-means clustering with the number of clusters chosen in advance by an elbow plot
b) Density-based clustering that finds arbitrary shapes and labels sparse points as noise
c) Principal component analysis applied to the coordinates before grouping
d) Agglomerative hierarchical clustering cut at a chosen dendrogram height
07. After several evaluation rounds a team must store the selected model so that a result can be reproduced and audited months later.
Beyond the serialized model file itself, what is most important to record alongside it?
a) The names of the team members who approved the model for release
b) A copy of the source data files stored in the same directory as the model artifact
c) The hardware specification of the machine, including its accelerator type, used for training
d) The training data version, the hyperparameters and the metrics from that run
08. Over a two-month project a team has checked test-set performance after each of roughly forty modeling changes, keeping the variants that scored better. The final test score is strong.
Why is that score no longer a dependable estimate of future performance?
a) Forty modeling changes is too many for a project of this size to evaluate coherently
b) The test partition was too small to distinguish between forty variants
c) Repeated selection against the test set has fitted the modeling choices to it
d) Test scores become unreliable once the underlying data is more than a month old
09. An analyst is combining two sources for a churn study. The subscription table holds one row per customer per month, while the support table holds one row per individual ticket, and a customer may raise several tickets in a month. Joining them directly on the customer identifier produces far more rows than the subscription table contains.
What should be done before the join?
a) Deduplicate the support table, keeping only each customer's first ticket
b) Aggregate the support table to one row per customer per month, then join on both keys
c) Add the ticket identifier to the join condition so each pairing is unique
d) Use a left join from the subscription table so that unmatched support tickets are dropped
10. Tuning a model requires roughly 40 minutes per configuration, and the team can afford about 30 evaluations in total. The search space has six continuous hyperparameters.
Which search strategy makes the best use of that budget?
a) Bayesian optimization, which uses earlier evaluations to choose the next configuration
b) Manual tuning, adjusting one hyperparameter at a time while holding the others fixed
c) Exhaustive grid search over a coarse grid across all six dimensions
d) Random search over the six hyperparameter dimensions
Answers:
|
Question: 01 Answer: c |
Question: 02 Answer: a |
Question: 03 Answer: b |
Question: 04 Answer: c |
Question: 05 Answer: d |
|
Question: 06 Answer: b |
Question: 07 Answer: d |
Question: 08 Answer: c |
Question: 09 Answer: b |
Question: 10 Answer: a |
Note: For any error in CertNexus Certified Data Science Practitioner (CDSP) (DSP-210) certification exam sample questions, please update us by writing an email on feedback@edusum.com.
