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- A-Philip Zimbardo
- B-Stanley Milgram
- C-Solomon Asch
- D-John B. Watson
- Posted By: MCQSEXAM
- Psychology / Psychology
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- A-Trust vs. Mistrust
- B-Autonomy vs. Shame and Doubt
- C-Initiative vs. Guilt
- D-Identity vs. Role Confusion
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- Psychology / Psychology
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- A-Physiological needs
- B-Safety needs
- C-Love and belongingness needs
- D-Esteem needs
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- Psychology / Psychology
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- A-Serotonin
- B-Dopamine
- C-Acetylcholine
- D-GABA
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- Psychology / Psychology
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- A-Cultural Psychology
- B-Cross-Cultural Psychology
- C-Socio-Cultural Psychology
- D-Ethnopsychology
- Posted By: MCQSEXAM
- Psychology / Psychology
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- A-Sigmund Freud
- B-B.F. Skinner
- C-Carl Rogers
- D-William James
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- Psychology / Psychology
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- A-Biological Psychology
- B-Cognitive Psychology
- C-Social Psychology
- D-Developmental Psychology
- Posted By: MCQSEXAM
- Psychology / Psychology
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- A-Data Gateway
- B-Power Query Editor
- C-Scheduled Refresh
- D-Row-level Security
- Posted By: MCQSEXAM
- Data Science / Power BI
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- A-Using consistent color schemes and fonts
- B-Including all available visuals on a single dashboard
- C-Arranging visuals logically for easy comprehension
- D-Limiting the number of visuals to avoid clutter
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- Data Science / Power BI
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- A-To import data from external sources
- B-To provide context or explanations for visualizations
- C-To define relationships between tables
- D-To create calculated columns
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- Data Science / Power BI
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- A-Create calculated columns
- B-Interact with visuals to apply filters
- C-Modify data sources
- D-Export data to external sources
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- Data Science / Power BI
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- A-To create custom visualizations
- B-To define relationships between tables
- C-To organize and display visuals on a dashboard
- D-To import data from external sources
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- Data Science / Power BI
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- A-Power Query Editor
- B-Power BI Service
- C-Dashboard Canvas
- D-Report View
- Posted By: MCQSEXAM
- Data Science / Power BI
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- A-To import data from external sources
- B-To create calculated columns
- C-To provide an overview of key metrics and insights
- D-To design custom visualizations
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- Data Science / Power BI
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- A-To create custom visualizations
- B-To design consistent branding across reports
- C-To optimize data storage
- D-To import data from external sources
- Posted By: MCQSEXAM
- Data Science / Power BI
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- A-Drill-downs
- B-Slicers
- C-Tooltips
- D-Data Insights
- Posted By: MCQSEXAM
- Data Science / Power BI
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- A-To create calculated columns
- B-To filter data based on specific criteria
- C-To dynamically format visuals based on data values
- D-To define relationships between tables
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- Data Science / Power BI
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- A-Animated Charts
- B-Play Axis
- C-Data Insights
- D-Report Themes
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- Data Science / Power BI
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- A-To zoom in on specific data points for detailed analysis
- B-To filter data based on user interactions
- C-To create hierarchies within visualizations
- D-To export data to external sources
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- Data Science / Power BI
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- A-Using a wide range of colors in a single visualization
- B-Overloading visualizations with too much data
- C-Ensuring consistency in formatting across visualizations
- D-Using small font sizes for text elements
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- Data Science / Power BI
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- A-To provide additional information on hover
- B-To navigate between different report pages
- C-To filter data based on user interactions
- D-To create calculated columns
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- Data Science / Power BI
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- A-Power Query Editor
- B-Report View
- C-Custom Visuals Marketplace
- D-Data Model View
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- Data Science / Power BI
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- A-To filter data based on specific criteria
- B-To create calculated columns
- C-To define relationships between tables
- D-To import data from external sources
- Posted By: MCQSEXAM
- Data Science / Power BI
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- A-Scatter Plot
- B-Radar Chart
- C-Tree Map
- D-Gauge
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- Data Science / Power BI
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- A-To restrict data access based on user roles
- B-To create hierarchies within tables
- C-To enable filtering in both directions between related tables
- D-To optimize data storage
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- Data Science / Power BI
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- A-Active
- B-Inactive
- C-Both
- D-Single
- Posted By: MCQSEXAM
- Data Science / Power BI
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- A-To import data from external sources
- B-To create tables based on existing tables using DAX expressions
- C-To visualize data in charts and graphs
- D-To optimize data storage
- Posted By: MCQSEXAM
- Data Science / Power BI
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- A-Query Editor
- B-Data Model View
- C-Report View
- D-Relationship View
- Posted By: MCQSEXAM
- Data Science / Power BI
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- A-They are pre-calculated and stored in the data model.
- B-They can be used in DAX expressions to perform calculations on aggregated data.
- C-They are created using SQL queries.
- D-They can only be created in Power Query Editor.
- Posted By: MCQSEXAM
- Data Science / Power BI
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- A-To organize data into logical groups
- B-To enforce data validation rules
- C-To optimize data storage
- D-To import data from external sources
- Posted By: MCQSEXAM
- Data Science / Power BI
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- A-To add new columns to a table based on existing columns
- B-To filter data based on specific criteria
- C-To visualize data in charts and graphs
- D-To import data from external sources
- Posted By: MCQSEXAM
- Data Science / Power BI
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- A-One-to-One
- B-One-to-Many
- C-Many-to-One
- D-Many-to-All
- Posted By: MCQSEXAM
- Data Science / Power BI
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- A-To merge tables into a single table
- B-To define how tables are visually displayed in reports
- C-To optimize data storage
- D-To enable data analysis across related tables
- Posted By: MCQSEXAM
- Data Science / Power BI
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- A-To automate data refresh
- B-To create calculated columns
- C-To collaborate and share reports
- D-To import data from external sources
- Posted By: MCQSEXAM
- Data Science / Power BI
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- A-Power BI Desktop
- B-Power Query Editor
- C-Power BI Service
- D-Power BI Mobile
- Posted By: MCQSEXAM
- Data Science / Power BI
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- A-To import new data sources
- B-To clean existing data
- C-To automate data updates
- D-To create calculated columns
- Posted By: MCQSEXAM
- Data Science / Power BI
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- A-Filtering rows
- B-Renaming columns
- C-Running SQL queries
- D-Removing duplicates
- Posted By: MCQSEXAM
- Data Science / Power BI
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- A-Row-level security
- B-Column-level security
- C-Dataset permissions
- D-Report sharing
- Posted By: MCQSEXAM
- Data Science / Power BI
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- A-Web Importer
- B-Power Query Editor
- C-Data Gateway
- D-Azure Integration
- Posted By: MCQSEXAM
- Data Science / Power BI
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- A-Creating visuals
- B-Preparing data for analysis
- C-Defining relationships between tables
- D-Importing data from external sources
- Posted By: MCQSEXAM
- Data Science / Power BI
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- A-SQL
- B-Python
- C-DAX
- D-JavaScript
- Posted By: MCQSEXAM
- Data Science / Power BI
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- A-Power Query Editor
- B-Data Modeling View
- C-Report View
- D-Data Analysis Expressions (DAX)
- Posted By: MCQSEXAM
- Data Science / Power BI
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- A-Excel
- B-SQL Server
- C-Oracle
- D-PowerPoint
- Posted By: MCQSEXAM
- Data Science / Power BI
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- A-Value equality
- B-Reference equality
- C-Data type equality
- D-Memory location equality
- Posted By: MCQSEXAM
- Data Science / Python
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- A-[1, 2]
- B-{'a': 1, 'b': 2}
- C-(1, 2)
- D-Error
- Posted By: MCQSEXAM
- Data Science / Python
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- A-remove()
- B-pop()
- C-discard()
- D-delete()
- Posted By: MCQSEXAM
- Data Science / Python
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- A-to_int()
- B-int()
- C-parse_int()
- D-convert_to_int()
- Posted By: MCQSEXAM
- Data Science / Python
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- A-Tuples can be modified after creation.
- B-Tuples use square brackets [ ] for declaration.
- C-Tuples can only contain immutable elements.
- D-Tuples can only contain integers.
- Posted By: MCQSEXAM
- Data Science / Python
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- A-(1, 2, 3, 4)
- B-(1, 2) (3, 4)
- C-(4, 3, 2, 1)
- D-Error
- Posted By: MCQSEXAM
- Data Science / Python
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- A-trim()
- B-strip()
- C-clean()
- D-remove()
- Posted By: MCQSEXAM
- Data Science / Python
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- A-reverse()
- B-invert()
- C-flip()
- D-revolve()
- Posted By: MCQSEXAM
- Data Science / Python
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- A-[0, 1, 2, 3, 4]
- B-[1, 2, 3, 4, 5]
- C-[1, 2, 3, 4]
- D-[0, 1, 2, 3]
- Posted By: MCQSEXAM
- Data Science / Python
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- A-bool(0)
- B-bool("")
- C-bool([])
- D-bool(None)
- Posted By: MCQSEXAM
- Data Science / Python
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- A-"Python"
- B-"nohtyP"
- C-"nohtyP"
- D-"ythoPn"
- Posted By: MCQSEXAM
- Data Science / Python
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- A-frac()
- B-modf()
- C-fraction()
- D-split()
- Posted By: MCQSEXAM
- Data Science / Python
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- A-True
- B-False
- C-Error
- D-Undefined
- Posted By: MCQSEXAM
- Data Science / Python
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- A-Integer division always rounds down the result.
- B-Integer division always rounds up the result.
- C-Integer division returns a float.
- D-Integer division is not supported in Python 3.x.
- Posted By: MCQSEXAM
- Data Science / Python
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- A-[1, 2]
- B-{'a': 1, 'b': 2}
- C-(1, 2)
- D-Error
- Posted By: MCQSEXAM
- Data Science / Python
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- A-update()
- B-merge()
- C-append()
- D-extend()
- Posted By: MCQSEXAM
- Data Science / Python
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- A-{1, 2}
- B-{3}
- C-{3, 4, 5}
- D-Error
- Posted By: MCQSEXAM
- Data Science / Python
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- A-remove()
- B-clear()
- C-pop()
- D-discard()
- Posted By: MCQSEXAM
- Data Science / Python
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- A-('a', 'b', 'c')
- B-('a', 'b', 'c', 'd')
- C-('a', 'b', 'd', 'c')
- D-Error
- Posted By: MCQSEXAM
- Data Science / Python
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- A-count()
- B-index()
- C-get()
- D-len()
- Posted By: MCQSEXAM
- Data Science / Python
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- A-['a', 'b', 'c', 'a', 'b', 'c']
- B-[['a', 'b', 'c'], ['a', 'b', 'c']]
- C-['aa', 'bb', 'cc']
- D-Error
- Posted By: MCQSEXAM
- Data Science / Python
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- A-pop()
- B-remove()
- C-del()
- D-clear()
- Posted By: MCQSEXAM
- Data Science / Python
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- A-Strings can contain both letters and numbers.
- B-Strings are mutable.
- C-Strings can only contain ASCII characters.
- D-Strings can be represented using single quotes but not double quotes.
- Posted By: MCQSEXAM
- Data Science / Python
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- A-Booleans are represented as 1 and 2.
- B-Booleans can only be assigned with the True keyword.
- C-Booleans are a subclass of integers.
- D-Booleans cannot be used in conditional statements.
- Posted By: MCQSEXAM
- Data Science / Python
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- A-"hello"
- B-"HELLO"
- C-"Hello"
- D-Error
- Posted By: MCQSEXAM
- Data Science / Python
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- A-Integers can have leading zeros.
- B-Integers can have a decimal point.
- C-Integers can be represented in binary format.
- D-Integers can contain letters.
- Posted By: MCQSEXAM
- Data Science / Python
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- A-Floats cannot represent negative numbers.
- B-Floats have infinite precision.
- C-Floats are always displayed with a fixed number of decimal places.
- D-Floats can be used to represent fractions accurately.
- Posted By: MCQSEXAM
- Data Science / Python
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- A-keys()
- B-values()
- C-items()
- D-get()
- Posted By: MCQSEXAM
- Data Science / Python
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- A-Tuples can be modified after creation.
- B-Tuples use curly braces {} for declaration.
- C-Tuples support duplicate elements.
- D-Tuples can only contain integers.
- Posted By: MCQSEXAM
- Data Science / Python
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- A-push()
- B-add()
- C-append()
- D-insert()
- Posted By: MCQSEXAM
- Data Science / Python
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- A-pop()
- B-remove()
- C-del()
- D-clear()
- Posted By: MCQSEXAM
- Data Science / Python
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- A-index()
- B-find()
- C-search()
- D-locate()
- Posted By: MCQSEXAM
- Data Science / Python
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- A-"python"
- B-"nohtyp"
- C-"nythop"
- D-Error
- Posted By: MCQSEXAM
- Data Science / Python
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- A-2e3
- B-0b101
- C-'3.14'
- D-[1, 2, 3]
- Posted By: MCQSEXAM
- Data Science / Python
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- A-remove()
- B-pop()
- C-delete()
- D-discard()
- Posted By: MCQSEXAM
- Data Science / Python
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- A-person['age']
- B-person.get('age')
- C-person.value('age')
- D-person[age]
- Posted By: MCQSEXAM
- Data Science / Python
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- A-{1, 2, 3, 4, 5}
- B-{1, 2}
- C-{3}
- D-Error
- Posted By: MCQSEXAM
- Data Science / Python
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- A-Yes
- B-No
- C-Only if it contains integers
- D-Only if it contains strings
- Posted By: MCQSEXAM
- Data Science / Python
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- A-Mutable
- B-Indexed
- C-Unordered
- D-Duplicates allowed
- Posted By: MCQSEXAM
- Data Science / Python
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- A-append()
- B-insert()
- C-extend()
- D-remove()
- Posted By: MCQSEXAM
- Data Science / Python
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