CertNexus Certified Data Science Practitioner (CDSP) Practice Exam

These questions are part of the practice quiz. Start practicing

• Which cost function calculates the average difference between estimated and actual values without factoring in their signs?
• What does elastic net regression combine in its approach?
• Which term refers to the situation where the distribution of data has two peaks?
• Which of the following is true about WCSS in clustering?
• What is the expected output when applying standardization to a dataset?
• What is a key characteristic of leptokurtic distributions?
• Which of the following visualizations is used to show central tendency and variation in data distribution?
• What is the primary function of a bar chart?
• What role does a dataset play in the business goals of a project?
• In data analysis, what term describes the use of numerical values to summarize data patterns?
• Which term refers to the measure of variability that captures the range of the middle half of data values?
• What is the primary purpose of data visualizations?
• Which term is used to indicate a data point's placement within a cluster in relation to others?
• A value that falls outside the expected range of data can best be described as what?
• Which of the following refers to a diagram that represents a tree-like hierarchy?
• What aspect of a sample set does the sample mean represent?
• What type of plot is characterized by connecting data points in order with a series of lines?
• What is a sample set?
• Which of the following are examples of pipeline monitoring solutions?
• What term describes massive quantities of data that cannot be easily translated into actionable intelligence using traditional methods?
• Which characteristic is involved in a learning curve?
• What characteristic defines a unimodal distribution?
• What is the primary characteristic of a model that suffers from overfitting?
• Which type of join returns all records from the first dataset and only the matching records from the second dataset?
• In k-fold cross-validation, how is the data used?
• Which chart type is used to represent the proportional measurement of categorical values with horizontal or vertical bars?
• Which AI discipline enables machines to improve estimative capabilities without explicit instructions?
• What does the ROC curve represent in model evaluation?
• What does the null hypothesis assume in statistical testing?
• What term is used to describe a model that is deemed useful for its intended task?
• What is an example of data that is not classified as big data?
• What clustering method adjusts the number of clusters dynamically by merging nearby points?
• What is the term for the calculation involving the average, mode, and standard deviation that indicates skewness?
• What is the measure of how often the positive identifications made by a learning model are true positives?
• What is a regular expression used for?
• What type of plot is used to show the distribution of a numerical value through probability density?
• What is the key benefit of using DevOps practices in data science projects?
• What are hyperparameters in the context of machine learning?
• In a confusion matrix, what do true positives represent?
• Which of the following terms best describes the diversity of outcomes that a model can produce due to variability in the data?
• In the context of machine learning, the concept of "noise" can best be described as:
• In data science, what is often the goal of using a stochastic model?
• What characteristic of a distribution indicates that values are concentrated toward one of the extremes?
• What is the formula used to calculate kurtosis?
• Which term describes parameters that are typically set before the training of a machine learning model begins?
• In sensitivity analysis, which aspect is primarily evaluated?
• Which of the following best describes supervised learning?
• What cross-validation method is defined by leaving one participant out to minimize performance issues?
• Which clustering method is noted for its shortcomings with circular or spiral data?
• What does the coefficient of determination (R^2) indicate?
• Which statistical parameter is NOT part of the common four used to measure distributions?
• What does HIPAA stand for in relation to healthcare regulations?
• What is the role of a threshold in a binary classification model?
• Which term refers to the probability distribution that has a bell-shaped curve?
• What process can help in making raw data more understandable and usable?
• What does the p-value represent in hypothesis testing?
• What technique helps prevent overfitting in machine learning by constraining model parameters?
• What does a 'decision boundary' separate in a dataset?
• What transformation method raises each data example to a power of some lambda value to reduce skewness?
• Which of the following best describes continuous variables?
• Which type of data holds number values that express magnitude?
• Which of the following is a method used in hypothesis testing?
• What process involves adjusting hyperparameters used by an algorithm to improve model performance?
• What metric is often used to assess the performance of classification models alongside F1 score?
• Which technique involves scaling features so that the lowest value is 0 and the highest is 1?
• Which approach aims to identify and control the variables in an experiment?
• What type of data representation involves ordering observations according to changes over time?
• How does a confidence interval typically perform in relation to the true population mean?
• What are the internal parameters derived from a model during the training process known as?
• What does the term "overfitting" refer to in machine learning?
• What defines a specific implementation of an algorithm that generates predictions based on training data?
• Which of the following best describes 'regularization'?
• What is the process called that simplifies a dataset by removing redundant or irrelevant features?
• What does feature engineering primarily aim to enhance in a machine learning model?
• What is PCI DSS best known for?
• What does leave-one-out validation involve?
• What type of data can be placed in an order?
• What term refers to a variable that a data science practitioner seeks to learn more about?
• What type of plot visually represents data values using varying shades of color on a matrix?
• Which of the following lists includes different types of data storage solutions?
• What does recall measure in a machine learning model?
• Which function refers to how independent variables relate to the dependent variables to best meet expectations?
• Which type of distribution demonstrates the probability of outcomes for a random variable?
• What bias arises when training data excludes participants who have dropped out over time?
• Which term is commonly associated with removing common words that may not add significant meaning in text analysis?
• What is a primary function of ridge regression?
• What does a z-score indicate?
• What is the technique called that improves a model’s ability to make estimations by generating features?
• In the context of data science, what does 'data preparation' specifically refer to?
• What is meant by collinearity in regression analysis?
• How is variance calculated for a sample set?
• What is the formula for calculating accuracy in a classification model?
• What is the term for a decision boundary in support vector machines that has parallel and equidistant lines on either side?
• Which technique involves scaling features so that the mean value is 0 and the standard deviation is 1?
• What technique improves a model's ability to generalize to new data by partitioning the data?
• Which metric measures the distance between a data point and its cluster centroid?
• What is the name of the process that converts a continuous variable into a discrete variable?
• What measures how often a learning model incorrectly classifies positive outcomes?
• What k-fold cross-validation method uses all data points in the dataset as folds?
• What is the defining feature of parametric algorithms?
• What do we call irrelevant or irregular data values that obscure meaningful patterns in other relevant data?
• What term describes incorrect or missing values in a dataset?
• What aspect of data is preserved in stratified k-fold cross-validation?
• What is the term for the minimum number of samples required to be a leaf node in a decision tree?
• What is the primary focus of Bessel's correction in statistical calculations?
• What is a measure that indicates the strength of dependence between two variables, producing a value between +1 and -1?
• What term describes a representation of the relationship between input and output variables in a machine learning model?
• What method involves optimizing hyperparameters through random sampling of parameter combinations?
• Which concept refers to the trend that, as more data is added to a model, the model's performance reaches an optimal point beyond which additional data has a negligible effect?
• What U.S. law was enacted in 1996 to regulate healthcare practices?
• What classification problem involves assigning multiple labels to a single data example?
• What approach would you use to estimate missing values in a dataset?
• What is the name of the cross-validation method that splits a dataset into training and test sets?
• Which type of algorithms typically has a fixed number of parameters?
• In machine learning, what is the significance of the term 'errors'?
• In machine learning, what type of classification problem allows data examples to be classified into one of three or more classes?
• Which process is essential for ensuring data quality before analysis?
• What type of regression analysis provides a classification probability between 0 and 1?
• What type of data is defined as being in a format that facilitates searching, filtering, or extracting?
• In machine learning, what is the variable that you are attempting to predict in a training set called?
• What is the relation between ARIMA and time series analysis?
• What law states that when a measure becomes a target, it ceases to be a good measure?
• Which hyperparameter tuning method randomly selects combinations of hyperparameters?
• Which methodology involves identifying, analyzing, and controlling variables in an experimental setup?
• Which model only considers a single variable in its prediction?
• In an experiment, what is the term for a variable that can affect the dependent variable?
• What is the classification algorithm that utilizes Bayes' theorem to compute classification probabilities called?
• Which type of variable is characterized by having countable, limited values and finite gaps between them?
• Which analysis focuses on understanding and modeling the distribution and relationships of data points?
• What is lemmatization primarily used for in text processing?
• Which clustering algorithm starts with each data example in its own cluster?
• Which statistical test compares the effects of categorical variables?
• Which of the following is a technique for selecting a subset of features in a model?
• What does the area under the ROC curve represent?
• Data that can take any value within a range is known as which type of data?
• What defines non-parametric algorithms?
• Which algorithm is commonly used to address multi-class classification problems?
• What regulation governs the export of EU citizens' personal data?
• What correction is applied when performing variance calculations on a sample by subtracting 1 from the total number of values?
• What term describes the measure of decision-making processes in a model applied to specific data examples?
• What term describes a type of classification in SVMs where all examples are on the correct side of the margin?
• Which type of algorithms are characterized by generating a potentially infinite number of model parameters?
• What is the process of identifying an issue that should be addressed and putting it in understandable and actionable terms called?
• Which type of data expresses categories without a meaningful order?
• What optimization method uses past samples to influence where future sampling occurs in order to find the next optimal sample space?
• Which ensemble learning method aggregates multiple decision tree models together and selects the optimal classifier or predictor?
• What type of distribution is characterized by having two humps?
• What type of graphical representation shows data points in relation to their geographical location?
• What best describes the primary goal of data science?
• What analytical method assesses how well a data point fits within a cluster relative to others?
• What does a z-score represent in statistics?
• What method would primarily be used for reducing the dimensionality of a categorical dataset?
• What is the purpose of the Area Under ROC Curve (AUC) metric?
• Which type of join returns all records from both datasets, matching where possible?
• What property of a dataset is displayed when there is a high density of values clustered at one end of the distribution?
• What term describes a distribution that follows the shape of a normal curve?
• What does the abbreviation AI stand for in data science?
• What term describes a model that performs well on any new datasets it might encounter?
• In data science, what method is used when a data example can only be classified as a 1 or 0?
• Which regression method is often used for modeling binary outcomes?
• What is the purpose of stratified k-fold cross-validation?
• Data binning helps in managing which aspect of the dataset?
• Which metric provides the weighted average of precision and recall?
• What is the term for a distribution that has more than one peak?
• What is the term for a type of data analysis that quantitatively summarizes patterns and relationships in a dataset?
• In the context of text analysis, what is the primary purpose of a bag-of-words model?
• What is a function that represents the distribution of a random variable as a symmetrical bell-shaped graph?
• What does 'data munging' or 'data wrangling' primarily involve?
• What type of classification approach uses support vector machines to maximize the margin distance?
• What is the main objective of dimensionality reduction in data science?
• Which term describes a distribution characterized by a flat peak and light tails?
• Which concept is used to quantify the error between estimated values and actual labeled values?
• What process is crucial for making raw data interpretable and analyzable by machine learning algorithms?
• What is the process of combining and preparing data from multiple sources called?
• What is the term used to describe a project's uncontrolled growth beyond its original objectives?
• What is the term used for the phenomenon where a machine learning model's performance deteriorates over time due to changes in the patterns of data?
• What tool is used to visualize the results of a classification problem?
• What type of distribution illustrates the frequency of outcomes for a specific random variable sample?
• Which clustering algorithm starts with all data examples in a single cluster and splits them?
• Which analysis method is primarily focused on predicting future outcomes based on current data?
• Which of the following describes project deliverables in a project scope?
• What type of regression analysis deals with an independent and a dependent variable in a linear relationship?
• Which term refers to the transformation and loading process of data into a destination?
• Which term is used to describe categorical values visualized for comparison purposes in a dataset?
• What is the primary goal of tokenization in text processing?
• In project management, what term describes a detailed outline of all aspects of a project, including constraints?
• Which of the following is NOT a feature of ANOVA?
• What does the mean squared error (MSE) function primarily measure in a machine learning model?
• What measure indicates the linear correlation between two variables commonly called x and y?
• Which term describes a mathematical system that generates assumptions about data using statistical methods?
• What regularization method uses the l2 norm for its regularization term?
• Which of the following is NOT a regularization technique?
• What do attributes (or features) contain in a model?
• Which statistical test is used to compare the means of two distributions when the population standard deviation is known?
• What does R² indicate in statistical modeling?
• What is the measure that represents the square root of variance?
• Which of the following techniques is used to address class imbalance in datasets?
• What is the name of the process used to fill in missing data values using statistical calculations?
• What is the measure of how many positive instances a model identifies compared to all relevant instances called?
• What type of error cannot be reduced further when fitting a machine learning model due to model framing?
• What aspect of model evaluation is concerned with the proportion of actual positives correctly identified?
• What does AUC stand for in the context of model evaluation?
• In data science, what is the purpose of data preprocessing?
• Which approach combines the estimates of multiple models in machine learning?
• What is the main purpose of latent class analysis?
• Which set of statistical parameters is used to measure a distribution?
• What type of test compares two different values of the same variable to determine the most effective one?
• What does LCA stand for in data analysis?
• In leave-p-out validation, how many data points are used for testing?
• What is the term for the process of cleaning and organizing raw data into a usable format?
• Which of the following is commonly used as a metric for constructing a decision tree?
• Which statistical test is used to compare the means of two distributions when the population standard deviation is unknown?
• What is the primary purpose of the Gini index in decision trees?
• The CART model is primarily used for which of the following?
• What does the term 'data wrangling' typically refer to in data science?
• What does data cleaning refer to?
• What is a confidence interval?
• Which plot visually represents the range measurements such as Median, Q1, Q3, Minimum, and Maximum?
• Which type of analysis involves summarizing patterns and relationships in data using statistical measures and visualizations?
• Which of the following is not a characteristic of data visualizations?
• What hyperparameter determines how deep a decision tree can grow?
• What is the method for systematically designing experiments to evaluate the influence of variables?
• In clustering, what is the term for the point where the mean distance between data examples and their centroid stabilizes?
• Which of the following sampling techniques could lead to an underrepresentation of minority classes?
• What is the hyperparameter optimization method that evaluates multiple parameter combinations?
• When a model cannot capture the underlying trends in the data, it is said to be:
• Which of the following is a common method for handling missing data?
• Which regression technique forces the coefficients of the last relevant feature to zero using the l1 norm?
• Which metric is used to evaluate the accuracy of regression predictions?
• In data science, what is the significance of 'data preprocessing'?
• Which of the following structures is characterized by conditional statements and their conclusions?
• Which technique is used to assess the performance of a classification model?
• What formula is used to calculate the variance of a population?
• The absence of reported observations in the training data may lead to which type of bias?
• What process involves placing the values of continuous variables into specific, discrete intervals?
• What type of variable is often the focus of a regression analysis?
• What is the term for a sequential set of processing that automates the data science process by feeding the output of one process into the input of the next process?
• Which statistical measures summarize the "middle" portion of a sample dataset?
• What system uses k-nearest neighbor for classification of data examples?
• Which method visually compares the change in a model's performance against the number of data examples used?
• What is an area plot in data visualization?
• What is the challenge of 'data wrangling' primarily about?
• What issue arises when a model is too simplistic, resulting in an inability to derive relevant insights from new data?
• What property indicates that a process cannot perfectly estimate individual events but can demonstrate a general pattern?
• What issue occurs when a model is too complex and matches the training data too closely?
• Which term describes a distribution with a specific shape characterized by a normal peak and normal tails?
• Which measure reflects the separation between clusters in a dataset?
• Which variable in an experiment is typically manipulated to observe its effect on the dependent variable?
• What is the splitting metric used in decision trees that assesses the purity of nodes?
• What type of kurtosis has a value equal to 3?
• What statistical measure provides an indication of how closely the data points cluster around the mean?
• What does RMSE stand for in the context of evaluating model performance?
• Which term best describes the alterations made to data in order for it to support analytics?
• What type of data can be easily searched and filtered, while other elements are not?
• In the context of data science, what is the purpose of a cost function?
• In gradient descent, what is referred to as the learning rate?
• What type of data consists of numerical values that stand for magnitude?
• What is the term for the process of simplifying a decision tree by removing nodes, branches, and leaves that offer little value?
• Which method minimizes a cost function by gradually tuning model parameters?
• Which term refers to qualitative data with a limited number of values?
• What iterative ensemble learning method builds multiple decision trees to reduce errors?
• What type of machine learning is characterized by using multiple layers of information to make complex decisions?
• What does TPR stand for in the evaluation of machine learning models?
• What is the process of making predictions about future events based on past event analysis called?
• What statistical test is used to compare the means of multiple distributions?
• Which function outputs a value between 0 and 1, forming an S shape in logistic regression?
• What is used to assess the skill and performance of a machine learning model?
• Which method is used for evaluating model performance in regression tasks?
• Which hyperparameter specifies how many samples are required to split a decision node?
• Which statistical concept relates to determining how many standard deviations a value is from the mean?
• What term is defined as the average of all numbers in a data set?
• In machine learning, what does "iterative" refer to in the context of gradient descent?
• What term describes the frequency with which a machine learning model correctly identifies all actual negative instances?
• Which testing method allows researchers to determine if there are statistical differences between group means?
• Which term refers to the practice of giving different weights or importance to different components within a model?
• Which term describes the difference between the smallest and largest values in a dataset?
• What term describes the merging of development and operations in a tech context?
• What is the term for bias introduced when the training dataset is not representative of the target population?
• Which process converts data from one type to a coded value of a different type?
• Which term encompasses methods like linear regression, decision trees, and k-means clustering?
• What is the term for the process of closely examining data to reveal new insights?
• What is a characteristic of multi-label classification?
• What is the term for variables that change indirectly in an experiment?
• What process involves taking data as input and representing it in a certain structure or syntax?
• What is the role of the cost function in machine learning?
• What is the technique of condensing a language vocabulary into smaller dimensional vectors called?
• Data that holds categorical values is referred to as what type of data?
• What is defined as a matrix of all zeros except for the main diagonal consisting of all 1s?
• What can be inferred if kurtosis is less than 3?
• What is the primary goal of a regression analysis?
• What is represented on a lift chart?
• What does the term 'dimensions' refer to in the context of a model?
• What term refers to the extent to which data varies across all values in a dataset?
• Which type of kurtosis is characterized by a narrow peak and heavy tails?
• What is a 'dataset' in the context of data science?
• What does IQR stand for in statistical analysis?
• What term describes a mathematical relationship between two variables?
• What is defined as a value that deviates significantly from the main distribution of values?
• What is the key characteristic of Ridge regularization?
• What is a limitation of linear equations in data modeling?
• Which type of machine learning provides known label values as input for future predictions?
• What is the concept of model drift?
• What type of plot represents a probability distribution using bins?
• What technique samples the training dataset for each individual tree while allowing data examples to appear in multiple models?
• Which of the following statements is true about model parameters?
• What type of data is described as unstructured?
• What type of error occurs when the chosen model is overly complex and captures noise instead of the underlying trend?
• Gradient boosting is primarily used for which type of modeling?
• In the context of machine learning, what does the term "k" in k-fold cross-validation refer to?
• What key benefit does cross-validation provide in model evaluation?
• What kind of learning uses data that is difficult to search, filter, or extract?
• Which of the following best describes platykurtic distributions?
• What algorithm is commonly used to classify data examples based on similarities within the feature space?
• What type of graph is used to illustrate the relationship between two quantitative variables?