Distributed Systems and Streaming Data
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Questions and Answers

Which combination of Big Data tools is most appropriate for processing real-time call logs to detect network anomalies?

  • Apache Kafka and Apache HBase (correct)
  • Apache Hadoop and Apache Pig
  • Apache Spark and Apache Hive
  • Apache Storm and Apache Pig

    • Why is Apache Hadoop not suitable for real-time processing of call logs?

  • It is designed for batch processing. (correct)
  • It is not distributed.
  • It cannot handle high volumes of data.
  • It lacks a storage solution.

    • Apache Kafka is primarily used as a substitute for which type of solution?

  • Data visualization
  • Data analysis
  • Log aggregation (correct)
  • Batch processing

    • Which of the following statements about Apache Spark and Apache Hive is true?

    <p>Spark can handle real-time processing, while Hive is oriented towards batch processing.</p> Signup and view all the answers

    What is the primary function of Apache Flume?

    <p>Data ingestion</p> Signup and view all the answers

    Which tool is NOT suited for real-time analytics when combined with Apache Storm?

    <p>Apache Pig</p> Signup and view all the answers

    Why is log aggregation an important use case for Apache Kafka?

    <p>It efficiently gathers log data from various sources.</p> Signup and view all the answers

    Which of the following represents the smallest unit of data processed by Apache Spark Streaming?

    <p>Micro-batch</p> Signup and view all the answers

    Which of the following tools is best suited for real-time stream processing?

    <p>Apache Storm</p> Signup and view all the answers

    What characteristic defines the behavior of Bloom filters?

    <p>May produce false positives but no false negatives</p> Signup and view all the answers

    What does the CAP theorem emphasize in the design of distributed systems?

    <p>Trade-offs between consistency, availability, and partition tolerance</p> Signup and view all the answers

    Which of the following is NOT a primary use of Bloom filters?

    <p>Sorting large datasets</p> Signup and view all the answers

    In the context of Spark Streaming, what is a 'window' primarily used for?

    <p>To group data for processing over a defined time interval</p> Signup and view all the answers

    Which statement about record processing in Spark Streaming is accurate?

    <p>Records are grouped into micro-batches for processing.</p> Signup and view all the answers

    Why might one use multiple hash functions in Bloom filters?

    <p>To decrease the chance of false positives</p> Signup and view all the answers

    Which of the following describes a limitation of the CAP theorem?

    <p>It states that only one of the three properties can ever be fully achieved.</p> Signup and view all the answers

    What does the CAP theorem primarily address in distributed systems?

    <p>Trade-offs between consistency, availability, and partition tolerance</p> Signup and view all the answers

    Which of the following statements is true regarding the guarantees of the CAP theorem?

    <p>A distributed system must guarantee partition tolerance.</p> Signup and view all the answers

    In Cassandra, which consistency level ensures a write operation is acknowledged after writing to all replicas?

    <p>ALL</p> Signup and view all the answers

    What does the CAP theorem suggest about maintaining data accuracy?

    <p>It does not prioritize data accuracy, focusing instead on consistency and availability.</p> Signup and view all the answers

    Which option is not a focus of the CAP theorem?

    <p>Performance</p> Signup and view all the answers

    What is a common misunderstanding about the CAP theorem?

    <p>It guarantees all three properties simultaneously.</p> Signup and view all the answers

    Why is availability not guaranteed if partition tolerance is required, according to the CAP theorem?

    <p>Because partition events disrupt the entire network consistency.</p> Signup and view all the answers

    For a distributed system, what does NOT enhance fault tolerance?

    <p>Adopting the CAP theorem principles</p> Signup and view all the answers

    What is the primary purpose of bootstrapping in the Random Forest algorithm?

    <p>To produce replicas of the dataset by random sampling with replacement</p> Signup and view all the answers

    How do individual decision trees in a Random Forest model gain diversity?

    <p>By training each tree on different bootstrapped subsets of data</p> Signup and view all the answers

    In the context of building a decision tree model using MapReduce, what is the correct approach?

    <p>Distributing data and computations across multiple nodes for parallel processing</p> Signup and view all the answers

    What is a common misconception about the bootstrapping process?

    <p>Bootstrapping samples the dataset without replacement</p> Signup and view all the answers

    Why is using a single-node system not ideal for building decision tree models in big data scenarios?

    <p>They cannot process large datasets efficiently</p> Signup and view all the answers

    What is the role of automatic data partitioning in the MapReduce framework for decision trees?

    <p>It enhances the efficiency of data analysis by sorting data during processing</p> Signup and view all the answers

    Which of the following statements about bootstrapping and decision trees is accurate?

    <p>Bootstrapping enhances variability by sampling with replacement</p> Signup and view all the answers

    What happens when the decision tree model is constructed without using the MapReduce framework?

    <p>Scalability issues may arise with larger datasets.</p> Signup and view all the answers

    What is the primary aim of bagging in machine learning?

    <p>To reduce variance in predictions</p> Signup and view all the answers

    Which statement accurately describes bagging?

    <p>It combines predictions from multiple models through averaging.</p> Signup and view all the answers

    How does bagging primarily improve model performance?

    <p>By generating multiple data subsets for training</p> Signup and view all the answers

    What is a common misconception regarding the effect of bagging on bias?

    <p>Bagging is designed specifically for reducing bias.</p> Signup and view all the answers

    Which of the following best describes the benefit of using regression trees in a big data environment with MapReduce?

    <p>They automatically manage large datasets using distributed processing.</p> Signup and view all the answers

    What is an incorrect statement about the use of decision trees in big data?

    <p>They are only suited for classification tasks.</p> Signup and view all the answers

    In what way is bagging not focused on decision trees?

    <p>It is solely intended for feature selection.</p> Signup and view all the answers

    Which aspect of bagging differentiates it from boosting?

    <p>Bagging creates subsets of data independently for each model.</p> Signup and view all the answers

    Study Notes

    Spark Streaming

    • Spark Streaming is designed for processing streaming data, not static datasets.
    • The smallest unit of data processed in Spark Streaming is a micro-batch.
    • A micro-batch is a collection of records.

    Bloom Filters

    • Bloom Filters are used for approximate membership testing, not sorting.
    • They are known for producing false positives but not false negatives.
    • They typically use multiple hash functions, not cryptographic hashing functions.

    CAP Theorem

    • The CAP theorem states that distributed systems can only guarantee two out of Consistency, Availability, and Partition tolerance in the presence of network partitions.
    • Partition tolerance is the most important guarantee for distributed systems because network partitions are inevitable.

    Apache Cassandra

    • Apache Cassandra's ALL consistency level requires all replicas to acknowledge a write operation before returning a response, ensuring the highest level of data consistency.

    Real-Time Data Processing Use Cases

    • A combination of Apache Kafka and Apache HBase is suitable for real-time data processing, like processing call logs to detect network anomalies.
    • Kafka is a distributed event streaming platform, used for handling real-time data streams.
    • HBase is a NoSQL database used for real-time read/write access to large datasets.

    Apache Kafka

    • Apache Kafka is commonly used as a substitute for log aggregation solutions.

    Random Forest

    • Random Forest models are built using bootstrapping.
    • Bootstrapping involves creating multiple subsets of the original dataset by randomly sampling with replacement, introducing variability among individual decision trees.

    Decision Trees in MapReduce

    • Decision trees can be built using MapReduce by distributing the data and computations across multiple nodes for parallel processing.

    Bagging

    • Bagging is a general method for averaging predictions used to improve the stability and accuracy of machine learning algorithms.
    • It is not a technique exclusively designed for decision trees.
    • Bagging reduces variance but not necessarily bias.

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    Description

    This quiz covers key concepts related to Spark Streaming, Bloom Filters, the CAP theorem, and Apache Cassandra. Test your understanding of how these components interact in distributed systems and real-time data processing. Explore the fundamentals of data consistency and processing frameworks through practical questions.

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