SDS関連資料、SDS模擬体験我々JPTestKingサイトはすべてのDASCA SDS試験に準備する受験生の最も信頼できる強いバッキングです。DASCA SDS試験のための一切な需要を満足して努力します。購入した後、我々はあなたがSDS試験にうまく合格するまで細心のヘルプをずっと与えます。一年間の無料更新と試験に合格しなくて全額返金も我々の誠のアフタサーブすでございます。 DASCA Senior Data Scientist 認定 SDS 試験問題 (Q80-Q85):質問 # 80
Which of the following is an SLAs specification in case of Internet Service Provider (ISP)?
A. Mean Time To Recovery (MTTR)
B. Turnaround Time (TAT)
C. All of the above
D. Mean Time Between Failures (MTBF)
正解：C
解説：
Service Level Agreements (SLAs) define performance commitments between service providers (like ISPs) and customers. Key SLA metrics include:
MTBF (Option A): Measures reliability by defining the expected average time between service failures.
MTTR (Option B): Measures availability by defining how quickly service can be restored after a failure.
TAT (Option C): Measures responsiveness in resolving customer requests or incidents.
All three are standard SLA performance specifications. Hence, the correct answer is Option D (All of the above).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Data Engineering and IT Practices: SLAs and Performance Metrics.
質問 # 81
Which of the following is FALSE for Social Network Analysis (SNA)?
A. Social Network Analysis (SNA) is an example of graph analysis
B. SNA is used to investigate social structures and relationships across social networks
C. Social Network Analysis (SNA) is an example of trend analysis
D. None of the above
E. SNA characterizes networked structures in terms of nodes and the ties or edges that connect them
正解：C
解説：
Social Network Analysis (SNA) is a powerful analytical method that applies graph theory to study relationships among entities (people, organizations, computers, etc.).
Option A: Correct. SNA is indeed an example of graph analysis because it models entities as nodes and their relationships as edges/ties.
Option B: FALSE. SNA is not an example of trend analysis. Trend analysis focuses on temporal patterns (time series), while SNA is structural and relational.
Option C: Correct. SNA investigates structures such as communities, influencers, and information diffusion in networks.
Option D: Correct. The characterization of nodes and edges is central to SNA.
Option E: Incorrect, since we've identified Option B as false.
Thus, the false statement is Option B.
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Analytics: Graph Analysis & Social Network Analysis.
質問 # 82
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. Have a desire to leverage Big Data to transform their business but do not know where and how to start
D. All of the above
E. Both A and B
正解：D
解説：
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.
質問 # 83
A workflow refers to a:
A. Indirected acyclic graph
B. Directed acyclic graph
C. Indirected cyclic graph
D. Directed cyclic graph
正解：B
解説：
In data pipelines and process orchestration, a workflow is represented as a Directed Acyclic Graph (DAG):
Directed: Each edge has a direction, representing task dependencies.
Acyclic: No cycles exist; tasks must follow a sequence without looping back.
Graph: Represents tasks as nodes and dependencies as edges.
This structure is common in tools like Apache Airflow, Spark DAGs, and Hadoop MapReduce job schedulers.
Option A & B: Incorrect, as workflows cannot have cycles (would cause infinite loops).
Option D: Incorrect, because workflows are directed, not indirected.
Thus, the correct answer is Option C (Directed Acyclic Graph).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Data Engineering Architectures: Workflow Management with DAGs.
質問 # 84
What is the agenda of discussion at a "stand up" meeting of an Agile team?
A. What they are planning to do today
B. What they accomplished the previous day
C. All of the above
D. Any roadblocks they are running into
E. Both A and B
正解：C
解説：
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.