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DASCA Senior Data Scientist Sample Questions (Q54-Q59):

NEW QUESTION # 54
Which of the following can be classified as factor analysis in machine learning?

• A. Exploratory factor analysis
• B. Confirmatory factor analysis
• C. Both A and B
• D. None of the above

Answer: C

Explanation:
Factor analysis is a dimensionality reduction technique used to uncover latent variables (factors) that explain observed patterns of correlations in data. It is widely used in psychometrics, social sciences, and machine learning.
Exploratory Factor Analysis (EFA, Option A): Used when the underlying factor structure is unknown, aiming to discover potential latent variables.
Confirmatory Factor Analysis (CFA, Option B): Used when there is a hypothesis about factor structure, and the goal is to confirm it statistically.
Both are valid approaches to factor analysis, hence the correct answer is Option C (Both A and B).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Dimensionality Reduction & Factor Analysis in Machine Learning.

 

NEW QUESTION # 55
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 100 times faster in memory and 10 times faster on disk in comparison to the traditional Hadoop MapReduce
• C. 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
• D. 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

Answer: B

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 # 56
Which of the following is an example of NLP?

• A. Flagging e-mails as spam
• B. Using Twitter to assess public sentiment
• C. Finding which text documents are about similar topics
• D. Both A and C
• E. All of the above

Answer: E

Explanation:
Natural Language Processing (NLP) is the field of machine learning that deals with human language understanding and generation. Applications include:
Option A (Spam detection): NLP techniques classify emails based on text patterns and context.
Option B (Sentiment analysis on Twitter): NLP models analyze textual data to extract emotions, opinions, and trends.
Option C (Topic modeling): NLP clustering and probabilistic models (e.g., LDA) classify documents by semantic similarity.
Since all are valid NLP applications, the correct answer is Option E (All of the above).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Applications of Machine Learning: NLP in Real- World Use Cases.

 

NEW QUESTION # 57
The aim of developing Scribe was to offer reliability with using:

• A. None of the above
• B. Complex protocols
• C. High disk space usage
• D. Extensive protocols

Answer: A

Explanation:
Scribe is a server developed by Facebook for aggregating log data streamed in real time from many servers.
Its primary aim was reliability and simplicity in collecting logs at scale.
Unlike other systems, Scribe was designed to avoid complex or extensive protocols and to ensure lightweight log transfer.
It also avoids unnecessary high disk usage, relying instead on efficient pipelines.
Thus, the correct answer is Option D (None of the above).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Data Engineering Tools: Log Collection Systems (Scribe, Kafka, Flume).

 

NEW QUESTION # 58
Which of the following is NOT a cluster management tool?

• A. Apache Ambari
• B. Apache Mesos
• C. Apache Hadoop
• D. Zettaset Orchestrator

Answer: C

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 # 59
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