Sample Questions and Answers
- Which of the following is most commonly used to analyze unstructured data in the form of text?
A) Decision Trees
B) Natural Language Processing (NLP)
C) Linear Regression
D) K-means Clustering
Answer: B - What is the primary goal of predictive analytics in the context of unstructured data?
A) To summarize historical data
B) To create visual dashboards
C) To identify patterns and forecast future trends
D) To store unstructured data in a structured format
Answer: C - Which of these methods is specifically used for sentiment analysis in unstructured text data?
A) Data Mining
B) Text Mining
C) Regression Analysis
D) Logistic Regression
Answer: B - What is one common technique used for transforming unstructured text into structured data for analysis?
A) Clustering
B) Tokenization
C) Dimensionality Reduction
D) Cross-validation
Answer: B - Which of the following unstructured data types can be analyzed using image recognition techniques?
A) Social media posts
B) Audio files
C) Customer feedback surveys
D) Photographs
Answer: D - In which industry is unstructured data like social media posts and customer reviews commonly analyzed for sentiment analysis?
A) Healthcare
B) Retail
C) Education
D) Manufacturing
Answer: B - Which of the following machine learning techniques is most commonly applied to unstructured data to classify text into categories?
A) Linear Regression
B) Support Vector Machines
C) K-means Clustering
D) Principal Component Analysis
Answer: B - Which of these tools is widely used for analyzing and extracting meaning from unstructured text data in business settings?
A) Python and R
B) Excel
C) PowerPoint
D) MATLAB
Answer: A - What is the main challenge when analyzing unstructured data for business decision-making?
A) Lack of data storage solutions
B) Difficulty in structuring the data
C) Over-reliance on numerical data
D) Limited hardware capabilities
Answer: B - Which technique is commonly used to extract keywords and key phrases from unstructured text data?
A) Decision Trees
B) Text Mining
C) Data Normalization
D) Neural Networks
Answer: B - Which of the following is an example of unstructured data in the context of business analytics?
A) Excel spreadsheets
B) Product sales data
C) Email content
D) Financial reports
Answer: C - What is the purpose of clustering techniques when applied to unstructured data?
A) To predict future values
B) To group similar items together
C) To reduce dimensionality
D) To test hypotheses
Answer: B - Which of the following unstructured data sources is frequently analyzed to gain insights into customer behavior?
A) Structured databases
B) Sensor data
C) Audio recordings
D) Transaction logs
Answer: C - Which type of unstructured data would benefit most from the use of sentiment analysis?
A) Time-series data
B) Customer reviews and social media posts
C) Weather data
D) Sales transaction data
Answer: B - Which machine learning model is commonly used to extract topics from large collections of unstructured text?
A) Naive Bayes
B) Latent Dirichlet Allocation (LDA)
C) Random Forest
D) Support Vector Machine
Answer: B - When analyzing unstructured data, which of the following tools can be used to create visualizations such as word clouds or topic maps?
A) Tableau
B) SAS
C) Microsoft Word
D) MATLAB
Answer: A - Which of the following is a technique used to handle missing data in unstructured datasets?
A) Imputation
B) Outlier detection
C) Forecasting
D) Segmentation
Answer: A - What role does feature extraction play in analyzing unstructured data?
A) It reduces the volume of data
B) It transforms raw data into usable features
C) It eliminates irrelevant data
D) It stores the data in a structured format
Answer: B - Which of the following is an example of unstructured data in the healthcare industry?
A) Patient health records
B) Prescription information
C) Doctor’s handwritten notes
D) Medical billing data
Answer: C - What is the purpose of applying deep learning models to unstructured data?
A) To identify and extract patterns from large, complex datasets
B) To store data in relational databases
C) To generate random data for analysis
D) To create structured datasets
Answer: A - Which of the following would NOT typically be considered unstructured data?
A) Text from online reviews
B) Video content from surveillance cameras
C) Transaction records
D) Social media posts
Answer: C - Which unstructured data type is often analyzed using speech recognition tools in the context of customer service?
A) Text documents
B) Audio recordings
C) Video files
D) Images
Answer: B - In the context of unstructured data analysis, what does the process of “data wrangling” refer to?
A) Cleaning and transforming raw data into a usable format
B) Summarizing data using descriptive statistics
C) Building predictive models for decision-making
D) Visualizing the results of data analysis
Answer: A - Which of the following methods is commonly used to analyze patterns in unstructured video data?
A) Time-series analysis
B) Image recognition
C) Regression analysis
D) Monte Carlo simulations
Answer: B - What is one key advantage of analyzing unstructured data in business analytics?
A) It eliminates the need for predictive models
B) It provides insights into customer sentiments and behaviors
C) It requires no pre-processing before analysis
D) It is cheaper to store compared to structured data
Answer: B - Which of the following is a common tool used for text classification in unstructured data?
A) Random Forest
B) Naive Bayes
C) Linear Regression
D) Hierarchical Clustering
Answer: B - Which of the following best describes “data mining” in the context of unstructured data?
A) It involves creating artificial datasets from existing ones
B) It is the process of finding hidden patterns in large datasets
C) It organizes data into structured formats
D) It reduces the complexity of data visualization
Answer: B - In business analytics, what is a “data lake” typically used for in handling unstructured data?
A) Storing raw data in its native format for future processing
B) Performing real-time analytics on structured data
C) Transforming unstructured data into structured databases
D) Performing basic data cleaning tasks
Answer: A - Which of the following is an example of an unstructured data analysis application used in the retail industry?
A) Sales forecast modeling
B) Customer sentiment analysis from social media
C) Inventory management
D) Profit margin calculations
Answer: B - What is a major advantage of using unstructured data analytics in business decision-making?
A) Unstructured data is always easy to analyze
B) It offers deeper insights into customer preferences and trends
C) It guarantees accurate results with minimal data
D) It avoids the need for data pre-processing
Answer: B
- Which of the following describes the process of “text mining”?
A) Extracting meaningful patterns and knowledge from textual data
B) Analyzing numerical data from spreadsheets
C) Predicting future sales based on historical data
D) Summarizing unstructured data into reports
Answer: A - In which business scenario would audio analytics from customer service calls be most useful?
A) Tracking employee productivity
B) Enhancing customer experience and feedback analysis
C) Managing financial transactions
D) Predicting product demand
Answer: B - Which technique is commonly used to transform audio data into text for analysis?
A) Sentiment analysis
B) Speech-to-text conversion
C) Image recognition
D) Predictive modeling
Answer: B - Which machine learning algorithm is most suitable for clustering unstructured data into distinct groups based on similarities?
A) K-means Clustering
B) Decision Trees
C) Linear Regression
D) Support Vector Machines
Answer: A - What is the purpose of sentiment analysis in unstructured data?
A) To predict future market trends
B) To analyze the frequency of keywords
C) To determine the emotional tone behind words
D) To categorize text into predefined topics
Answer: C - Which of the following would NOT be a common challenge when dealing with unstructured data?
A) Difficulty in organizing data in structured formats
B) Lack of relevant tools for analysis
C) Predicting trends from structured data
D) High computational requirements for processing large datasets
Answer: C - What is “topic modeling” used for in the context of unstructured data?
A) To summarize large datasets into concise information
B) To classify text into specific categories or topics
C) To generate random text data
D) To convert unstructured data into structured formats
Answer: B
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