MCQs

Disclaimer:

Educational Use Only:

The website links provided are to be used strictly for educational and knowledge-gaining purposes only.

No Misuse:

Do not misuse the links or the information contained therein for any commercial, illegal, or unethical activities.

Respect for Websites and Businesses:

Respect the intellectual property and business interests of the websites and their owners. Do not attempt to alter, copy, or redistribute content without proper authorization.

Acknowledge Value of Knowledge:

Recognize and value the knowledge and resources shared by these websites. Use the information responsibly and ethically.

No Endorsement:

The provision of these links does not imply endorsement of the linked sites, their content, or their products and services. Users are responsible for evaluating the suitability and accuracy of the information.

User Responsibility:

By accessing these links, you agree to use the information at your own risk and to comply with all applicable laws and website terms of use.

Overall MCQs for Reference

What is a primary characteristic of structured data in data science?
a. Unpredictable format
b. Hierarchical data only
c. Clearly organized in rows and columns
d. Stored as images
Answer: c. Clearly organized in rows and columnsanalytixlabs+1
Which of the following is considered unstructured data?
a. CSV file
b. Relational database table
c. Text from social media posts
d. SQL query output
Answer: c. Text from social media postsincludehelp+1
What is tidy data?
a. Data formatted only for Excel
b. Data where each variable forms a column, each observation forms a row, and each type of observational unit forms a table
c. Data without missing values
d. Multi-indexed data with mixed types
Answer: b. Data where each variable forms a column, each observation forms a row, and each type of observational unit forms a tablesanfoundry+1
Which is NOT a type of data collection method for primary data?
a. Observation
b. Survey
c. Government database
d. Experiment
Answer: c. Government databasetestbook+1
Raw data in data science typically refers to:
a. Data that has already been cleaned
b. Processed information
c. Original, unprocessed data collected from source
d. Summarized statistics
Answer: c. Original, unprocessed data collected from sourceanalytixlabs+1
Which database is best suited for storing large-scale unstructured data?
a. Relational database
b. NoSQL database
c. Flat file
d. Spreadsheet
Answer: b. NoSQL databasepwskills+1
Meta-data describes:
a. The value of each data point
b. The structure, format, or meaning of other data
c. Only numerical data
d. Visualization of data only
Answer: b. The structure, format, or meaning of other datasanfoundry+1
Which of the following is an example of categorical data?
a. Weight of a person
b. Temperature reading
c. Gender (male, female, other)
d. GDP value
Answer: c. Gender (male, female, other)includehelp+1
Data redundancy means:
a. Data loss
b. Having duplicate data entries
c. Primary data collection
d. Data anonymization
Answer: b. Having duplicate data entriessanfoundry+1
What does data wrangling involve?
a. Collecting primary data
b. Formatting, cleaning, and transforming raw data
c. Running supervised machine learning models
d. Developing statistical plots
Answer: b. Formatting, cleaning, and transforming raw datainterviewbit+1
Null values in a dataset indicate:
a. Duplicate records
b. Missing or undefined data
c. Outliers
d. Data normalization
Answer: b. Missing or undefined datagyansetu+1
Which format is often used for exporting structured tabular data?
a. .jpg
b. .csv
c. .mp4
d. .exe
Answer: b. .csvinterviewbit+1
Outliers are data points that:
a. Cluster with the majority
b. Are missing values
c. Deviate significantly from other observations
d. Are always errors
Answer: c. Deviate significantly from other observationsgyansetu+1
What is data normalization?
a. Merging two different datasets
b. Rescaling numeric data into a standard range
c. Identifying categorical variables
d. Introducing missing values intentionally
Answer: b. Rescaling numeric data into a standard rangeinterviewbit+1
In data content, which is an example of temporal data?
a. Age group
b. Transaction timestamp
c. Gender
d. Currency
Answer: b. Transaction timestampanalytixlabs+1
Which of the following best describes big data’s “variety” characteristic?
a. Large size only
b. Different types and formats of data sources
c. Only structured databases
d. Only numeric data
Answer: b. Different types and formats of data sourcessanfoundry+1
Which of these is a data quality issue?
a. Consistent data types
b. Data completeness
c. Inconsistent or inaccurate data entries
d. Data normalization
Answer: c. Inconsistent or inaccurate data entriesanalytixlabs+1
Which method is used for detecting missing values in a DataFrame using Python’s pandas?
a. fillna()
b. isnull()
c. sort_values()
d. groupby()
Answer: b. isnull()interviewbit+1
Secondary data is best defined as:
a. Data collected and analyzed by the researcher
b. Raw data collected directly from the field
c. Data previously collected and processed by others
d. Data generated by experiments only
Answer: c. Data previously collected and processed by otherstestbook+1
What is data sampling?
a. Replacing all missing values with zero
b. Selecting a subset of data from a larger dataset for analysis
c. Merging two datasets
d. Sorting data alphabetically
Answer: b. Selecting a subset of data from a larger dataset for analysissanfoundry+1
Which is a common source of bias in data collection?
a. Random sampling methods
b. Non-representative samples
c. Data visualization
d. Data imputation
Answer: b. Non-representative samplestestbook+1
Which file type is often used for sharing semi-structured data?
a. .csv
b. .json
c. .zip
d. .exe
Answer: b. .jsoninterviewbit+1
Which of the following is considered time-series data?
a. Survey results without dates
b. Weather measurements taken hourly
c. Gender of survey participants
d. Movie genres
Answer: b. Weather measurements taken hourlyanalytixlabs+1
Which library is commonly used for data manipulation in Python?
a. TensorFlow
b. Keras
c. pandas
d. matplotlib
Answer: c. pandasinterviewbit+1
Data augmentation is typically used in:
a. Data visualization
b. Increasing dataset size with new, similar samples
c. Normalizing missing values
d. Encoding categorical variables
Answer: b. Increasing dataset size with new, similar samplesanalytixlabs+1
What is an identifier variable?
a. A variable that indicates the timestamp of entry
b. A variable that uniquely identifies each data record
c. A categorical variable
d. The primary data type of a column
Answer: b. A variable that uniquely identifies each data recordsanfoundry+1
Data consistency refers to:
a. The presence of outliers
b. Uniformity of data across datasets and systems
c. Existence of missing values
d. Use of different schemas
Answer: b. Uniformity of data across datasets and systemssanfoundry+1
Which technique is used to fill missing numerical data with median or mean value?
a. Data aggregation
b. Data imputation
c. Data encryption
d. Data normalization
Answer: b. Data imputationinterviewbit+1
Which of these is NOT a type of data encoding technique?
a. One-hot encoding
b. Binary encoding
c. Symmetric encoding
d. Label encoding
Answer: c. Symmetric encodinganalytixlabs+1
Data anonymization is important for:
a. Improving data redundancy
b. Protecting privacy of individuals in data sets
c. Increasing categorical feature count
d. Random sampling
Answer: b. Protecting privacy of individuals in data setsinterviewbit+1
Which statement best describes qualitative data?
a. Only numerical data
b. Data expressed as descriptions or categories
c. Data always presented in graphs
d. Data requiring normalization
Answer: b. Data expressed as descriptions or categoriesincludehelp+1
The process of merging data from different sources is called:
a. Data aggregation
b. Data visualization
c. Data normalization
d. Data simulation
Answer: a. Data aggregationanalytixlabs+1
What does the “veracity” of big data refer to?
a. Data speed
b. Data accuracy and reliability
c. Data type
d. Data formatting
Answer: b. Data accuracy and reliabilitysanfoundry+1
Which is a benefit of using relational databases for data content?
a. Storing images only
b. Efficient storage and organization of structured data
c. Only for semi-structured data
d. Only for big data
Answer: b. Efficient storage and organization of structured datapwskills+1
Which package is used in R for data manipulation tasks?
a. ggplot2
b. dplyr
c. shiny
d. caret
Answer: b. dplyrinterviewbit+1
The process of detecting duplicate entries in datasets is called:
a. Data imputation
b. Data deduplication
c. Data transformation
d. Data typing
Answer: b. Data deduplicationsanfoundry+1
Which data type is used for storing true/false values?
a. Numeric
b. Text
c. Boolean
d. Float
Answer: c. Booleananalytixlabs+1
Which open-source platform is commonly used for big data processing?
a. Excel
b. Azure Data Studio
c. Apache Hadoop
d. PowerBI
Answer: c. Apache Hadooppwskills+1
In data content, what is a feature?
a. Another term for dataset
b. A machine learning algorithm
c. An individual measurable property in a dataset
d. A normalization technique
Answer: c. An individual measurable property in a datasetsanfoundry+1
Which method is used to convert categorical variables into numerical in data preprocessing?
a. Data encryption
b. Label encoding
c. Data normalization
d. Data augmentation
Answer: b. Label encodinginterviewbit+1
Variable “Date of Birth” in a dataset is an example of:
a. Categorical data
b. Boolean data
c. Temporal data
d. Identifier variable
Answer: c. Temporal dataanalytixlabs+1
Which of these is a quantitative variable?
a. Nationality
b. Product rating (1 to 5 stars)
c. Gender
d. Phone number
Answer: b. Product rating (1 to 5 stars)includehelp+1
Which is a widely used language for data wrangling in data science?
a. C++
b. Python
c. Java
d. Assembly
Answer: b. Pythoninterviewbit+1
What kind of data is typically used for customer segmentation?
a. Only temporal data
b. Only unstructured data
c. Both structured and unstructured data
d. Only image data
Answer: c. Both structured and unstructured datasanfoundry+1
A data model mainly serves to:
a. Randomly sample data
b. Describe how data is represented and structured
c. Visualize data automatically
d. Store only categorical data
Answer: b. Describe how data is represented and structuredanalytixlabs+1
In a data science project, ‘data dictionary’ refers to:
a. The reference guide to all features and variables in the dataset
b. Python’s built-in dict type
c. Database schema
d. Pandas function
Answer: a. The reference guide to all features and variables in the datasetsanfoundry+1
Why is data exploration important?
a. For designing neural networks
b. To understand distributions, patterns, and detect anomalies in data
c. To only clean the data
d. For running SQL queries
Answer: b. To understand distributions, patterns, and detect anomalies in datainterviewbit+1
In the context of data content, “granularity” refers to:
a. Data encryption
b. Level of detail or fineness of data
c. Data storage medium
d. Outlier detection
Answer: b. Level of detail or fineness of dataanalytixlabs+1
Which is a disadvantage of high-dimensional data?
a. Improved accuracy
b. Easier visualization
c. Curse of dimensionality, making analysis harder
d. Simpler models
Answer: c. Curse of dimensionality, making analysis hardersanfoundry+1
For text analytics in data science, which technique is commonly used?
a. Fourier transform
b. TF-IDF (Term Frequency–Inverse Document Frequency)
c. Linear regression
d. K-means clustering only
Answer: b. TF-IDF (Term Frequency–Inverse Document Frequency)interviewbit+1

Blog

Systemethic Technologies

Leave a Reply Cancel reply

Useful links

Other Terms & Policies

Contact Info