Kurt Walsh Kurt Walsh
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SDS資料,SDS真題
作為IT認證的一項重要考試,DASCA SDS認證資格可以給你帶來巨大的好處,所有請把握這次可以成功的機會。為了能順利通過考試,持有完全版的DASCA SDS題庫資料是必要的,你就能輕松通過想要的認證考試。此外,Fast2test提供的所有考古題都是最新的,其中PDF版本的SDS題庫支持打打印,方便攜帶,現在就來添加我們最新的SDS考古題,了解更多的考試資訊吧!
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SDS認證考試資料匯總
SDS是DASCA認證考試,所以通過SDS是踏上DASCA 認證的第一步。也因此SDS認證考試變得越來越火熱,參加SDS考試的人也越來越多,但是SDS認證考試的通過率並不是很高。當你選擇SDS考試時有沒有選擇相關的考試課程?
最新的 DASCA Data Scientist SDS 免費考試真題 (Q75-Q80):
問題 #75
Semi-structured data does NOT include:
- A. Scientific data
- B. Database system
- C. Schema-full data
- D. File systems
答案:C
解題說明:
Semi-structured data falls between structured data (e.g., relational databases with fixed schema) and unstructured data (e.g., free text, audio, video). It typically includes irregular or flexible schema information, such as XML, JSON, email data, or log files.
Option A (Database systems): Correct, databases may hold semi-structured content (e.g., JSON or XML columns).
Option B (File systems): Correct, file-based storage (logs, JSON, Avro, CSV) often contains semi-structured data.
Option C (Scientific data): Correct, many scientific applications generate semi-structured data formats (sensor readings, genomic sequences, etc.).
Option D (Schema-full data): Correct Answer. Schema-full (strict schema-defined relational tables) represent structured data, not semi-structured.
Thus, semi-structured data does NOT include schema-full data.
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Big Data Fundamentals: Data Types & Sources.
問題 #76
Which of the following is TRUE about monetization exercise?
- A. The monetization exercise works by first understanding the product usage patterns and customer usage behaviors associated with a particular product and service
- B. The monetization exercise process then seeks to identify complementary or secondary recommendations that can be packaged and delivered along with that product or service
- C. All of the above
- D. Both B and C
- E. The monetization exercise provides an opportunity to uncover new product and/or service opportunities
答案:C
解題說明:
The monetization exercise is a critical phase in Big Data Business Model Maturity where organizations turn insights into new revenue opportunities.
Option A: Correct. Monetization often uncovers new product or service opportunities by leveraging analytics.
Option B: Correct. The first step is analyzing product usage patterns and customer behaviors to identify hidden value.
Option C: Correct. Insights help in building secondary recommendations or complementary products/services (cross-sell, upsell opportunities).
Thus, all three statements are valid, making the correct answer Option E (All of the above).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Business Applications of Data Science: Data Monetization Strategies.
問題 #77
Example of amortized performance is:
- A. Python dictionaries
- B. Hadoop dictionaries
- C. MapReduce dictionaries
- D. All of the above
- E. HDFS dictionaries
答案:A
解題說明:
Amortized performance refers to averaging the cost of operations over a sequence of actions, ensuring that while some operations may be costly, the overall average time per operation remains efficient.
Python Dictionaries (Option B): Implemented using hash tables. Insertions, deletions, and lookups typically run in O(1) average time, but occasionally require rehashing (costly). The high cost of rehashing is spread over many operations, giving amortized constant-time performance.
Option A (Hadoop dictionaries): Not standard terminology.
Option C (HDFS dictionaries): HDFS doesn't use dictionary structures in this sense.
Option D (MapReduce dictionaries): MapReduce uses key-value pairs, but amortized dictionary performance is not its focus.
Thus, the correct answer is Option B (Python dictionaries).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Programming for Data Science: Hash Tables & Amortized Analysis.
問題 #78
Which of the following is a DevOps Practice?
- A. Continuous integration
- B. All of the above
- C. Continuous delivery
- D. Continuous build
答案:B
解題說明:
DevOps is a collaborative practice that integrates software development (Dev) and IT operations (Ops) to shorten development cycles and deliver applications reliably. Common DevOps practices include:
Continuous Build (Option A): Automating compilation and packaging of source code to ensure consistent builds.
Continuous Integration (Option B): Developers frequently merge code into a shared repository, which is automatically tested to catch integration issues early.
Continuous Delivery (Option C): Automating software release pipelines so applications can be deployed to production quickly and reliably.
Since all of these are essential DevOps practices, the correct answer is Option D (All of the above).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Business Applications of Data Science: DevOps Practices in Data Science Projects.
問題 #79
Which of the following standardizes scores similar to a percentile rank but preserves equal interval properties of a Z-score?
- A. High Curve Equivalent (HCE)
- B. Normal Curve Equivalent (NCE)
- C. Medium Curve Equivalent (MCE)
- D. None of the above
- E. Trend analysis
答案:B
解題說明:
Normal Curve Equivalent (NCE) scores are standardized scores designed to:
Range between 1 and 99.
Be comparable to percentile ranks but with the advantage of equal-interval properties like Z-scores.
This makes NCE scores useful in educational assessments, survey analysis, and statistical modeling.
Option A (Trend analysis): Incorrect. Not related to score standardization.
Option B (Correct): NCE fits the definition perfectly.
Option C (HCE) & D (MCE): Not recognized standard measures in statistics.
Option E: Incorrect, since Option B is valid.
Thus, the correct answer is Option B: Normal Curve Equivalent (NCE).
Reference:
DASCA Data Scientist Knowledge Framework (DSKF) - Statistical Methods in Data Science: Z-scores, Percentiles, and NCE.
問題 #80
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