Q121
Q121 How does data aggregation aid in data visualization?
By reducing data volume
By encrypting data
By removing duplicates
By creating joins
Q122
Q122 What is the primary challenge of visualizing Big Data?
Data accuracy
Data latency
Scalability
Data integration
Q123
Q123 Which Python library is commonly used for creating visualizations in data reporting?
NumPy
Matplotlib
Pandas
Hadoop
Q124
Q124 How do you create a basic bar chart in Python using Matplotlib?
plt.line()
plt.plot()
plt.bar()
plt.show()
Q125
Q125 Which D3.js function is used to append new elements to the SVG container for visualization?
d3.select()
d3.append()
d3.enter()
d3.create()
Q126
Q126 A chart in Tableau is not displaying all data points correctly. What could be the possible cause?
Incorrect data type
Data filters
Large dataset
Data normalization
Q127
Q127 A report is taking too long to generate in a data visualization tool. What could be the likely reason?
Small dataset
Lack of data aggregation
Too many visualizations
Network issues
Q128
Q128 What is the primary goal of real-time data processing in Big Data?
Data storage
Insight communication
Low-latency data processing
Data replication
Q129
Q129 Which of the following is a common challenge in real-time data processing?
High storage cost
Scalability
Data accuracy
Network latency
Q130
Q130 How does Apache Flink ensure fault tolerance in real-time data processing?
By using real-time backups
By using data replication
By using distributed snapshots
By compressing data
Q131
Q131 What is the main benefit of windowing in real-time stream processing?
It reduces network overhead
It aggregates real-time data
It stores data permanently
It increases data velocity
Q132
Q132 Which command is used to start a Kafka stream in Apache Kafka?
kafka-producer
kafka-run-stream
kafka-console-producer
kafka-topics
Q133
Q133 How do you define a windowed operation in Apache Flink for real-time data?
stream.window(TumblingWindow)
stream.window(SlidingWindow)
stream.window(TimeWindow)
stream.window(CountWindow)
Q134
Q134 Which command is used to monitor Kafka consumer lag in real-time data processing?
kafka-lag-monitor
kafka-consumer-groups --describe
kafka-run-class
kafka-stream-monitor
Q135
Q135 A real-time data pipeline is experiencing high latency. What could be the possible cause?
Small dataset
Incorrect windowing
Network congestion
Data replication
Q136
Q136 A Flink stream is failing due to memory overflow. What could be the most likely cause?
Too few records
Small data size
High data volume
Low throughput
Q137
Q137 Which emerging technology is most commonly associated with Big Data analytics?
Blockchain
Edge computing
Quantum computing
Artificial Intelligence
Q138
Q138 How does edge computing enhance Big Data processing?
By centralizing data
By reducing network traffic
By storing data in the cloud
By using real-time processing
Q139
Q139 What role does blockchain play in Big Data security?
It provides data encryption
It decentralizes data management
It ensures data replication
It stores data in the cloud
Q140
Q140 How do you integrate a machine learning model in a Big Data pipeline using Apache Spark?
Use SQL API
Use MLlib
Use Hive
Use Pig
Q141
Q141 Which command in TensorFlow is used to run distributed machine learning jobs across multiple nodes?
tf.start()
tf.run()
tf.distribute.MirroredStrategy()
tf.cluster()
Q142
Q142 A Big Data pipeline with AI integration is producing inconsistent results. What could be the likely cause?
Overfitting of the AI model
Low data volume
High replication factor
Slow query processing
Q143
Q143 A quantum computing-based Big Data system is failing to process large datasets. What could be the cause?
Incorrect qubit configuration
Low network bandwidth
Incorrect encryption
Lack of edge computing
Q144
Q144 In a case study on Big Data in healthcare, what was the primary benefit of using Big Data analytics?
Improved data storage
Predictive healthcare
Cost reduction
Patient data security
Q145
Q145 How did Walmart leverage Big Data to enhance customer experience?
By analyzing social media data
By optimizing product prices
By implementing predictive analytics for inventory
By enhancing network security
Q146
Q146 In the Netflix case study, how did Big Data improve content recommendations?
By using user surveys
By analyzing historical viewing data
By monitoring social media trends
By sending notifications
Q147
Q147 Which Spark function would be most appropriate for analyzing user behavior data in a retail case study?
reduceByKey()
filter()
map()
count()
Q148
Q148 How would you query customer purchase history stored in a MongoDB collection in a retail case study?
db.collection.find({})
db.collection.query({})
db.collection.select({})
db.collection.get({})
Q149
Q149 In a financial Big Data case study, a query on customer transactions is running slowly. What could be the issue?
Insufficient indexes
Over-indexing
Data replication
Incorrect partitioning
Q150
Q150 In a telecom case study, a real-time streaming pipeline is failing due to data bottlenecks. What could be the cause?
Incorrect data partitioning
High query complexity
Data encryption
Low data replication
