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The spokes of the "Hub and Spoke" analytics architecture are the analytic use cases or applications that help the organization to optimize:
A. Deliver a more compelling customer experience
B. All of the above
C. Uncover new monetization opportunities
D. Key business processes
E. Both A and B
Answer: B
Explanation:
In the Hub and Spoke analytics architecture:
The hub is the central data platform (data lake, warehouse, or unified data hub).
The spokes are the analytic use cases or applications that leverage this data to create business value.
These spokes typically help the organization:
Optimize key business processes (Option A).
Deliver improved customer experiences (Option B).
Uncover monetization opportunities (Option C).
Since all three are valid, the correct answer is Option E (All of the above).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Data Engineering Architectures: Hub-and-Spoke Analytics.
NEW QUESTION # 51
Which of the following statements is correct?
A. Apache claimed that Spark is able to run parallel jobs 10 times faster in memory and 100 times faster on disk in comparison to the traditional Hadoop MapReduce
B. Apache claimed that Spark is able to run parallel jobs 50 times faster in memory and 5 times faster on disk in comparison to the traditional Hadoop MapReduce
C. Apache claimed that Spark is able to run parallel jobs 1000 times faster in memory and 100 times faster on disk in comparison to the traditional Hadoop MapReduce
D. Apache claimed that Spark is able to run parallel jobs 100 times faster in memory and 10 times faster on disk in comparison to the traditional Hadoop MapReduce
Answer: D
Explanation:
Apache Spark is a distributed computing framework designed as an improvement over Hadoop's MapReduce.
According to the official Apache Spark documentation:
Spark can run workloads up to 100x faster in memory.
Spark can run workloads up to 10x faster on disk.
This performance gain comes from Spark's use of in-memory computation, DAG execution engine, and optimized query execution, compared to the slower, disk-heavy Hadoop MapReduce framework.
Thus, the correct statement is Option A.
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Big Data Ecosystem: Spark vs Hadoop Performance Comparisons.
NEW QUESTION # 52
Which of the following is NOT a cluster management tool?
A. Apache Mesos
B. Apache Hadoop
C. Zettaset Orchestrator
D. Apache Ambari
Answer: B
Explanation:
Cluster management tools help in orchestrating and monitoring large-scale distributed computing environments.
Zettaset Orchestrator (A): Commercial tool for Hadoop cluster management.
Apache Mesos (B): A cluster manager that abstracts CPU, memory, and storage to enable fault-tolerant distributed systems.
Apache Ambari (C): An open-source tool for provisioning, managing, and monitoring Hadoop clusters.
Apache Hadoop (D): Not a cluster management tool. Hadoop is a framework for distributed storage and processing (HDFS + MapReduce), not a management tool.
Thus, the correct answer is Option D (Apache Hadoop).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Big Data Ecosystem: Hadoop Tools & Cluster Management.
NEW QUESTION # 53
What is TRUE for "rehashing"?
A. Key/value pairs from the original table can be inserted into the new, larger one
B. All of the above
C. Allocate a new, larger hash table in memory
D. It requires a new hash function, which maps values into a larger range of integers
E. Both A and B
Answer: B
Explanation:
Rehashing is a technique used in dynamic hash tables when the load factor (ratio of entries to bucket size) exceeds a certain threshold. It ensures efficient lookup, insertion, and deletion operations.
Option A (Correct): A larger hash table is allocated in memory to accommodate more entries.
Option B (Correct): A new hash function is typically required to map keys into the expanded table range.
Option C (Correct): All key-value pairs from the old table are re-inserted (rehashed) into the new table using the new hash function.
Since all three conditions (A, B, and C) are true, the best choice is Option E (All of the above).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Programming for Data Science: Data Structures & Hashing Techniques
NEW QUESTION # 54
The Big Data Vision Workshop process is ideal for organizations who:
A. Have a wealth of data that they do not know how to monetize
B. Have a desire to leverage the Big Data Vision Workshop to identify where and how to leverage data and analytics to power their business models
C. All of the above
D. Both A and B
E. Have a desire to leverage Big Data to transform their business but do not know where and how to start
Answer: C
Explanation:
The Big Data Vision Workshop is an early-phase framework designed to help organizations shape their data- driven transformation journey. It is particularly beneficial when:
Option A: Organizations want to leverage big data but lack clarity on where to start.
Option B: Organizations already have large volumes of data but struggle to derive monetization strategies from it.
Option C: Organizations want to identify use cases where data and analytics can enhance or even redefine their business models.
Since all three statements apply, the correct answer is Option E (All of the above).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Business Applications of Data Science: Big Data Vision Workshop.
NEW QUESTION # 55
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