実際的なD-DS-FN-23的中率試験-試験の準備方法-正確的なD-DS-FN-23復習過去問

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EMC D-DS-FN-23 認定試験の出題範囲：

トピック	出題範囲
トピック 1	Advanced Analytics - Theory, Application, and Interpretation of Results for Eight Methods: This section of the exam measures the skills of an Entry-Level Data Analyst and covers foundational knowledge in various advanced analytics methods. Topics include the theory, application, and interpretation of K-means clustering, association rules, linear and logistic regression, naïve Bayesian classifiers, decision trees, time series analysis, and text analytics.
トピック 2	Initial Analysis of the Data: This section of the exam measures the skills of a Data Science Enthusiast and focuses on the first steps in analyzing data. It explains how basic R commands are used for exploration, discusses important statistical measures and visualizations, and describes hypothesis testing techniques for evaluating models.
トピック 3	Advanced Analytics for Big Data - Technology and Tools: This section of the exam measures the skills of a Data Science Enthusiast and addresses the technological challenges associated with Big Data. It introduces tools and technologies such as MapReduce, Hadoop, the Hadoop ecosystem, in-database analytics, SQL essentials, and advanced SQL techniques like window functions and MADlib.
トピック 4	Operationalizing an Analytics Project and Data Visualization Techniques: This section of the exam measures the skills of an Entry-Level Data Analyst and explains best practices for communicating findings and operationalizing analytics projects. It covers effective methods for presenting projects to various audiences and emphasizes the importance of planning and creating impactful data visualizations.

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EMC Dell Data Science Foundations 認定 D-DS-FN-23 試験問題 (Q229-Q234):

質問 # 229
You are studying the behavior of a population, and you are provided with multidimensional data at the individual level. You have identified four specific individuals who are valuable to your study, and would like to find all users who are most similar to each individual.
Which algorithm is the most appropriate for this study?

A. Linear regression
B. K-means clustering
C. Decision trees
D. Association rules

正解：B

質問 # 230
What describes a true property of Logistic Regression method?

A. It handles missing values well.
B. It works well with discrete variables that have many distinct values.
C. It is robust with redundant variables and correlated variables.
D. It works well with variables that affect the outcome in a discontinuous way.

正解：C

質問 # 231
A call center for a large electronics company handles an average of 35, 000 support calls a day. The head of the call center would like to optimize the staffing of the call center during the rollout of a new product due to recent customer complaints of long wait times.
You have been asked to create a model to optimize call center costs and customer wait times.
The goals for this project include:
1. Relative to the release of a product, how does the call volume change over time?
2. How to best optimize staffing based on the call volume for the newly released product, relative to old products.
3. Historically, what time of day does the call center need to be most heavily staffed?
4. Determine the frequency of calls by both product type and customer language.
Which goals are suitable to be completed with MapReduce?

A. Goals 2, 3, 4
B. Goal 1 and 3
C. Goal 2 and 4
D. Goals 1, 2, 3, 4

正解：C

質問 # 232
Which word or phrase completes the statement? Data-ink ratio is to data visualization as _________.

A. Data scientist is to big data
B. Confusion matrix is to classifier
C. K-means is to Naive Bayes
D. Seasonality is to ARIMA

正解：B

質問 # 233
What is a benefit of Spark in-memory data processing as opposed to using MapReduce?

A. Supports processing unstructured data, which MapReduce does not allow
B. Avoids writing intermediate data to disk, which speeds up processing
C. Removes the need to use disks at all, which reduces cost
D. Allows parallel processing, which MapReduce does not support

正解：B

解説：
Spark's in-memory data processing avoids the need to write intermediate data to disk, which significantly speeds up processing compared to MapReduce, which writes intermediate data to disk after each operation.
This makes Spark much faster for iterative tasks.

質問 # 234
......

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D-DS-FN-23復習過去問: https://www.certshiken.com/D-DS-FN-23-shiken.html

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Alan Gray Alan Gray

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実際的なD-DS-FN-23的中率試験-試験の準備方法-正確的なD-DS-FN-23復習過去問

EMC D-DS-FN-23 認定試験の出題範囲：

D-DS-FN-23復習過去問 & D-DS-FN-23サンプル問題集

EMC Dell Data Science Foundations 認定 D-DS-FN-23 試験問題 (Q229-Q234):

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