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新版SDS題庫 - SDS認證資料
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最新的 DASCA Data Scientist SDS 免費考試真題 (Q17-Q22):
問題 #17
Which of the following is an example of NLP?
- A. Finding which text documents are about similar topics
- B. Both A and C
- C. All of the above
- D. Using Twitter to assess public sentiment
- E. Flagging e-mails as spam
答案：C
解題說明：
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.
問題 #18
Machine learning can be used in:
- A. Fraud detection
- B. Real-time ads on web pages and mobile devices
- C. Pattern and image recognition
- D. Web search results
- E. All of the above
答案：E
解題說明：
Machine Learning has broad applications across industries and technologies:
Fraud Detection (Option A): Detecting anomalies in financial transactions, credit card usage, and cybersecurity threats.
Web Search Results (Option B): Ranking algorithms (e.g., Google's PageRank enhanced by ML techniques) improve relevance of search queries.
Real-time Ads (Option C): Online ad systems use reinforcement learning and recommendation models to target ads dynamically.
Pattern & Image Recognition (Option D): ML (especially deep learning) powers facial recognition, handwriting recognition, medical imaging, etc.
Since ML is used in all these applications, the correct answer is Option E (All of the above).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Applications of Machine Learning Across Domains.
問題 #19
Spark programs can be written in:
- A. Python
- B. Java
- C. None of the above
- D. All of the above
- E. Scala
答案：D
解題說明：
Apache Spark supports multiple programming languages for developing distributed applications:
Java (Option A): Supported through Spark's JVM-based APIs.
Scala (Option B): Spark is natively written in Scala, and Scala APIs provide full functionality.
Python (Option C): Supported via PySpark, enabling Python developers to leverage Spark.
Additionally, Spark also supports R and SQL-like queries, making it versatile for data scientists and engineers.
Thus, the correct answer is Option D (All of the above).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Programming Tools: Spark APIs for Java, Scala, Python, and R.
問題 #20
Which of the following is FALSE for Social Network Analysis (SNA)?
- A. Social Network Analysis (SNA) is an example of graph analysis
- B. SNA characterizes networked structures in terms of nodes and the ties or edges that connect them
- C. None of the above
- D. Social Network Analysis (SNA) is an example of trend analysis
- E. SNA is used to investigate social structures and relationships across social networks
答案：D
解題說明：
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.
問題 #21
Which of the following can visualize variations in the base data, which can be used to identify outliers in the data for further investigation?
- A. Trend Analysis
- B. None of the above
- C. Box Plots
- D. Scatter Plot
- E. Histogram
答案：C
解題說明：
Box plots (or Whisker plots) are statistical graphics that represent data distribution through:
Minimum, First Quartile (Q1), Median, Third Quartile (Q3), and Maximum.
Outliers are plotted as individual points beyond the whiskers.
This makes them particularly powerful for:
Identifying outliers in data.
Comparing distributions across categories.
Understanding variability in data.
Option A (Trend Analysis): Shows temporal patterns, not individual outliers.
Option C (Histogram): Shows frequency distribution but does not explicitly highlight outliers.
Option D (Scatter Plot): Shows relationships between variables but doesn't focus on statistical outliers in one distribution.
Thus, the correct answer is Option B (Box Plots).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Data Visualization Tools: Box Plots for Outlier Detection.
問題 #22
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