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SDS Related Certifications & SDS Test Dumps Demo

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DASCA Senior Data Scientist Sample Questions (Q73-Q78):

NEW QUESTION # 73
Which of the following is an example of NLP?

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

Answer: A

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 # 74
Which of the following is TRUE for data lake?

A. None of the above
B. The data lake can make both of the Business Intelligence and Data Science environments less agile and more productive
C. The data lake can make both of the Business Intelligence and Data Science environments more agile and more productive
D. The data lake enables organizations to gather, manage, enrich, and analyze many new sources of data, whether structured or unstructured
E. The data lake enables organizations to treat data as an organizational asset to be gathered and nurtured versus a cost to be minimized

Answer: C,D,E

Explanation:
But per MCQ single-choice format # answer: A (though ideally A, B, C are correct).
A data lake is a centralized repository designed to store raw, structured, semi-structured, and unstructured data at scale. It provides:
Agility and productivity (Option A): Data lakes support flexible ingestion and faster access, making BI and data science environments more efficient.
Data integration (Option B): They handle multiple types of data, enabling advanced analytics and machine learning use cases.
Data as an asset (Option C): They shift perspective, treating data as a strategic resource, not just a storage cost.
Option D: Incorrect. Data lakes improve agility, not reduce it.
Option E: Incorrect, since multiple true statements exist.
Thus, the correct choice per DASCA context is Option A (with B and C also being true).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Data Engineering: Data Lakes vs Warehouses.

NEW QUESTION # 75
What is the agenda of discussion at a "stand up" meeting of an Agile team?

A. All of the above
B. What they are planning to do today
C. Any roadblocks they are running into
D. Both A and B
E. What they accomplished the previous day

Answer: A

Explanation:
A daily stand-up meeting (also called a daily Scrum) is a short meeting (usually 15 minutes) that Agile teams hold to synchronize progress. Its agenda is structured around three key questions:
What was accomplished yesterday? (Progress review).
What is planned for today? (Work alignment).
What impediments or roadblocks exist? (Barriers identification).
This process enhances transparency, communication, and accountability, ensuring the team can quickly address obstacles and stay aligned with sprint goals.
Option A: Correct - yesterday's work is discussed.
Option B: Correct - today's planned tasks are outlined.
Option C: Correct - roadblocks are highlighted.
Option D: Incomplete since it misses C.
Option E: Correct - covers all agenda items.
Thus, the correct answer is Option E (All of the above).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Agile Practices in Data Science Projects.

NEW QUESTION # 76
Which of the following is the most important part of Hadoop?

A. Hadoop Distributed File System (HDFS)
B. Both B and C
C. Spark Framework
D. Both A and B
E. MapReduce Framework

Answer: D

Explanation:
The Hadoop ecosystem consists of multiple components, but the two core components that define Hadoop are:
HDFS (Hadoop Distributed File System): Provides fault-tolerant, scalable storage across distributed clusters.
It is the backbone for storing massive datasets in a distributed fashion.
MapReduce Framework: Provides the parallel computing and data processing layer in Hadoop, enabling batch analysis over distributed datasets.
Option A: Correct, HDFS is essential.
Option B: Correct, MapReduce is essential.
Option C: Incorrect, Spark is a newer processing framework, but it is not originally part of Hadoop core.
Option D: Correct answer since both HDFS and MapReduce are considered the fundamental parts of Hadoop.
Option E: Incorrect, because Spark is not a core Hadoop component (though it integrates with Hadoop).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Big Data Ecosystems: Hadoop Architecture & Components.

NEW QUESTION # 77
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 # 78
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