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DASCA Senior Data Scientist Sample Questions (Q32-Q37):

NEW QUESTION # 32
Which of the following is NOT a part of Internal Process Optimization?

Answer: C

Explanation:
Internal Process Optimization (IPO) is one of the core applications of data science in business operations. It focuses on improving internal efficiency, reducing costs, and enhancing productivity using data-driven insights.
Typical components of IPO include:
Business Monitoring (Option A): Tracking performance metrics and KPIs in real time.
Business Insights (Option C): Identifying trends, anomalies, and inefficiencies through analytics.
Business Optimization (Option D): Applying data models to optimize workflows, resource utilization, or supply chains.
However:
Business Metamorphosis (Option B): Refers to fundamental transformational change or reinvention of a business model, not process-level optimization. This is more aligned with strategic transformation, not internal process optimization.
Therefore, the correct answer is Option B (Business Metamorphosis).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Business Applications of Data Science: Internal Process Optimization.


NEW QUESTION # 33
Which of the following is NOT a valid section of Big Data Strategy document?

Answer: D

Explanation:
A Big Data Strategy document provides a framework for aligning data initiatives with organizational objectives. It typically includes:
Business strategy (Option A): Ensures that big data initiatives align with overall corporate strategy.
Key decisions (Option B): Identifies the decisions data will help optimize or automate.
Key business initiatives (Option D): Links big data projects with critical organizational initiatives.
Key business entities (Option E): Defines the core entities (customers, products, channels) around which data will be organized.
However:
Business decisions (Option C): This is redundant and not a standard section; "key decisions" covers this aspect already.
Thus, the correct answer is Option C (Business decisions).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Big Data Strategy and Business Alignment.


NEW QUESTION # 34
Which of the following is TRUE for "By" analysis?

Answer: E

Explanation:
"By" analysis is one of the foundational approaches recommended in the DASCA Data Scientist Knowledge Framework for structuring problem-solving in data science. The purpose of "By" analysis is to enable data scientists and business stakeholders to think beyond obvious data correlations and uncover deeper drivers of business outcomes.
At its core, the technique reinforces the discipline ofthinking like a data scientist(Option A). This involves reframing business questions into analytical structures and asking "What drives this metricbywhich factors?" For example, customer churn might be analyzedbydemographics, purchase behavior, or service usage. This structured mindset is critical for ensuring scientific rigor in business problem analysis.
In addition, "By" analysis emphasizes collaboration betweenSubject Matter Experts (SMEs)andData Science teams(Option B). SMEs bring contextual domain knowledge, while data scientists bring analytical and statistical expertise. Together, they brainstorm possible explanatory variables or metrics that could become strong predictors of business performance.
Furthermore, the process provides acollaborative bridgebetween business and technical stakeholders (Option C). It ensures that the exploration of data is not isolated in silos but is grounded in both domain insights and advanced analytical methods. This alignment is crucial for building models that are not only technically sound but also relevant and actionable in real-world business contexts.
Since Options A, B, and C are correct and complementary, the best choice isOption E: All of the above.
Reference:DASCA Data Scientist Knowledge Framework (DSKF) -Data Science Process Fundamentals & Collaborative Analysis Techniques(Official DASCA Study & Exam Preparation Guide).


NEW QUESTION # 35
Which of the following is correct about customer lifetime value (CLTV)?
i. Most organizations determine the current customer lifetime value (CLTV) based on historic sales over past
12 to 18 months
ii. The goal of the CLTV score is to help marketing and store personnel to determine the "value" of a customer

Answer: A

Explanation:
Customer Lifetime Value (CLTV) is a predictive metric estimating the total revenue a business can reasonably expect from a customer during their entire relationship.
Statement i: Correct. Many organizations calculate CLTV using historic transactional data, often looking at sales records over the past 12-18 months to establish baselines.
Statement ii: Correct. The primary purpose of CLTV is to help marketing, sales, and retail teams understand customer value, enabling them to allocate budgets effectively for retention, promotions, and personalized marketing.
Thus, both statements are correct # Option C (Both i and ii).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Business Applications of Data Science: CLTV Metrics and Marketing Analytics.


NEW QUESTION # 36
Which of the following is NOT a cluster management tool?

Answer: D

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 # 37
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