Free EMC D-DS-FN-23 Study Guides Exam Questions & Answer [Q77-Q102]

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Free EMC D-DS-FN-23 Study Guides Exam Questions and Answer

D-DS-FN-23 Exam Dumps, D-DS-FN-23 Practice Test Questions

NEW QUESTION # 77
Refer to the exhibit.

You are using k-means clustering to discover groupings within a data set. You plot within- sum-of-squares (wss) of multiple cluster sizes.
Based on the exhibit, how many clusters should you use in your analysis?

  • A. 0
  • B. 1
  • C. 2
  • D. 3

Answer: A


NEW QUESTION # 78
Under which circumstance do you need to implement N-fold cross-validation after creating a regression model?

  • A. There is not enough data to create a test set.
  • B. The data is unformatted.
  • C. There are categorical variables in the model.
  • D. There are missing values in the data.

Answer: A


NEW QUESTION # 79
Data has been collected on visitors' viewing habits at a bank's website.
Which technique is used to identify pages commonly viewed during the same visit to the website?

  • A. Classification
  • B. Regression
  • C. Association Rules
  • D. Clustering

Answer: C


NEW QUESTION # 80
Which process in text analysis can be used to reduce dimensionality?

  • A. Parsing
  • B. Stemming
  • C. Digitizing
  • D. Sorting

Answer: B


NEW QUESTION # 81
What is a key consideration when preparing a presentation intended for analysts?

  • A. Focus on clean simple-to-understand visuals
  • B. Emphasize the business benefits of implementing the model
  • C. Provide talking points to promote or evangelize the project
  • D. Describe how to implement the model

Answer: A

Explanation:
Analysts value clarity and interpretability in data presentations. Clean, simple-to-understand visuals help them accurately assess the data, model outputs, and insights without unnecessary complexity.


NEW QUESTION # 82
In addition to less data movement and the ability to use larger datasets in calculations, what is a benefit of analytical calculations in a database?

  • A. more efficient handling of categorical values
  • B. quicker time to insight
  • C. improved connections between disparate data sources
  • D. full use of data aggregation functionality

Answer: B


NEW QUESTION # 83
Which key role for a successful analytic project can provide business domain expertise with a deep understanding of the data and key performance indicators?

  • A. Business Intelligence Analyst
  • B. Project Manager
  • C. Business User
  • D. Project Sponsor

Answer: A


NEW QUESTION # 84
Which word or phrase completes the statement; "A theater actor is to 'artistic and expressive' as a data scientist is to."?

  • A. Logical and steadfast
  • B. Communicative and collaborative
  • C. Independent and intelligent
  • D. Introverted and technical

Answer: B


NEW QUESTION # 85
Consider this SQL statement: SELECT product, avg(prod_cost) FROM product_detail GROUP BY product.
The GROUP BY clause implies what type of function?

  • A. Aggregate function
  • B. Window function
  • C. User defined function
  • D. System function

Answer: A

Explanation:
The GROUP BY clause in the SQL statement implies the use of an aggregate function. In this case, avg (prod_cost) is an aggregate function that calculates the average of the product costs for each group of products.


NEW QUESTION # 86
For which class of problem is Map Reduce most suitable?

  • A. Embarrassingly parallel
  • B. Minimal result data
  • C. Non-overlapping queries
  • D. Simple marginalization tasks

Answer: A


NEW QUESTION # 87
You have created a Linear Regression model to predict total sales based on variables M, N, P and Q as shown in the graphic. You originally expected all variables to have positive coefficients.

Which action would you take?

  • A. Accept only positive variables and investigate potential correlation with the dependent variable
  • B. Accept none of the variables and investigate correlations between all variables
  • C. Accept only statistically significant variables and investigate correlated independent variables
  • D. Accept all variables and begin model validation steps against holdout data

Answer: B


NEW QUESTION # 88
Which R data structure allows elements to have different data types?

  • A. Matrix
  • B. Array
  • C. Vector
  • D. List

Answer: D


NEW QUESTION # 89
You have been assigned to run a Logistic Regression model for 100 countries each. All data is currently stored in a PostgreSQL database.
Which tool/library should be used to produce these models with the least effort?

  • A. Mahout
  • B. HBase
  • C. MADlib
  • D. RStudio

Answer: C


NEW QUESTION # 90
When would you prefer a Naive Bayes model to a logistic regression model for classification?

  • A. When you need to estimate the probability of an outcome, not just which class it is in.
  • B. When some of the input variables might be correlated.
  • C. When you are using several categorical input variables with over 1000 possible values each.
  • D. When all the input variables are numerical.

Answer: C


NEW QUESTION # 91
Which participant in a data analytics project is typically responsible for assessing the validity of the model?

  • A. Project sponsor
  • B. Project manager
  • C. Business user
  • D. Data scientist

Answer: D


NEW QUESTION # 92
What is an example of a null hypothesis?

  • A. that a newly created model provides a prediction that will be well fit to the null distribution
  • B. that a newly created model provides a prediction of a null sample mean
  • C. that a newly created model does not provide better predictions than the currently existing model
  • D. that a newly created model provides a prediction of a null population mean

Answer: C


NEW QUESTION # 93
You submit a MapReduce job to a Hadoop cluster and notice that although the job was successfully submitted, it is not completing.
What should you do?

  • A. Ensure that the NameNode is running
  • B. Ensure that the JobTracker is running
  • C. Ensure that the TaskTracker is running.
  • D. Ensure that a DataNode is running

Answer: C


NEW QUESTION # 94
While having a discussion with your colleague, this person mentions that they want to perform K-means clustering on text file data stored in HDFS.
Which tool would you recommend to this colleague?

  • A. Sqoop
  • B. HBase
  • C. Mahout
  • D. Scribe

Answer: C


NEW QUESTION # 95
A data scientist is preparing a presentation for a meeting with the project's business sponsors. The distribution of per-sale revenue is an important finding from the analysis. The graphics illustrate four ways to plot the per-sale revenue distribution.."

Which graphic is most appropriate for the sponsor presentation?

  • A. Figure A
  • B. Figure C
  • C. Figure B
  • D. Figure D

Answer: C


NEW QUESTION # 96
The web analytics team uses Hadoop to process access logs. They now want to correlate this data with structured user data residing in their massively parallel database.
Which tool should they use to export the structured data from Hadoop?

  • A. Pig
  • B. Chukwa
  • C. Scribe
  • D. Sqoop

Answer: D


NEW QUESTION # 97
Refer to the exhibit.

Click on the calculator icon in the upper left corner. You are going into a meeting where you know your manager will have a question on your dataset -- specifically relating to customers that are classified as renters with good credit status.
In order to prepare for the meeting, you create a rule: RENTER => GOOD CREDIT.
What is the confidence of the rule?

  • A. 73%
  • B. 18%
  • C. 63%
  • D. 41%

Answer: C


NEW QUESTION # 98
Based on the exhibit, what is a likely issue with the data?

  • A. Incomplete data; indicating potential issues with data transmission
  • B. No obvious concerns with the data is visible
  • C. Mis-scaled data; indicating potential issues with data entry
  • D. Saturated data; indicating potential issues with data definitions

Answer: D


NEW QUESTION # 99
You are provided four different datasets. Initial analysis on these datasets show that they have identical mean, variance and correlation values.
What should your next step in the analysis be?

  • A. Visualize the data to further explore the characteristics of each data set
  • B. Select one of the four datasets and begin planning and building a model
  • C. Recalculate the descriptive statistics since they are unlikely to be identical for each dataset
  • D. Combine the data from all four of the datasets and begin planning and bulding a model

Answer: A


NEW QUESTION # 100
You have created a Linear Regression model to predict total sales based on variables M, N, P and Q as shown in the graphic. You originally expected all variables to have positive coefficients.

Which action would you take?

  • A. Accept only positive variables and investigate potential correlation with the dependent variable
  • B. Accept none of the variables and investigate correlations between all variables
  • C. Accept only statistically significant variables and investigate correlated independent variables
  • D. Accept all variables and begin model validation steps against holdout data

Answer: B


NEW QUESTION # 101
Refer to the exhibit.

Which type of data issue would you suspect based on the exhibit?

  • A. "Saturated" data, indicating potential issues with data definitions
  • B. Incomplete data, indicating potential issues with data transmission
  • C. The exhibit does not raise any obvious concerns with the data.
  • D. Mis-scaled data, indicating potential issues with data entry

Answer: A


NEW QUESTION # 102
......


EMC D-DS-FN-23 Exam Syllabus Topics:

TopicDetails
Topic 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.
Topic 2
  • Big Data, Analytics, and the Data Scientist Role: This section of the exam measures the skills of a Data Science Enthusiast and covers the basic concepts of Big Data, including its defining characteristics and the business drivers behind its rise. It also introduces the role of the Data Scientist, highlighting the critical skills needed in the data science field.
Topic 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.
Topic 4
  • 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.

 

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