D-DS-FN-23 Revolutionary Guide To Exam EMC Dumps [Q94-Q111]

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D-DS-FN-23 Revolutionary Guide To Exam EMC Dumps

D-DS-FN-23 Free Study Guide! with New Update 358 Exam Questions

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

Topic	Details
Topic 1	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 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	Data Analytics Lifecycle: This section of the exam measures the skills of an Entry-Level Data Analyst and explains the purpose and phases of the data analytics lifecycle. It includes understanding key activities and roles involved in the discovery, data preparation, model planning, and model building phases to successfully manage analytics projects.

NEW QUESTION 94
What is a core deliverable at the end of the analytic project?

An implemented database design

A whitepaper describing the project and the implementation

A presentation for project sponsors

The training materials

NEW QUESTION 95
In MADlib what does MAD stand for?

Magnetic, Agile, Deep

Machine Learning, Algorithms for Databases

Mathematical Algorithms for Databases

Modular, Accurate, Dependable

NEW QUESTION 96
You have been assigned to perform a study of the daily revenue effect of a pricing model of online transactions. All the data currently available has been loaded into an analytics database.
This data includes revenue data, pricing data, and online transaction data. You have completed a thorough univariate analysis of all data and have decided that there are three different models you want to test.
Preliminary results show that all models have equally effective results.
What is the next step?

Develop all three models and perform model selection in the next step of the process

Select the model that demonstrates the most sophisticated technique and accuracy

Identify which model the business owner wants and proceed with that model

Prioritize models by complexity and feasibility, and proceed with the most feasible

NEW QUESTION 97
In the Map Reduce framework, what is the purpose of the Reduce function?

It aggregates the results of the Map function and generates processed output

It distributes the input to multiple nodes for processing

It writes the output of the Map function to storage

It breaks the input into smaller components and distributes to other nodes in the cluster

NEW QUESTION 98
Refer to the exhibit.

You have scored your Naive bayesian classifier model on a hold out test data for cross validation and determined the way the samples scored and tabulated them as shown in the exhibit.
What are the the False Positive Rate (FPR) and the False Negative Rate (FNR) of the model?

FPR = 15/262 FNR = 26/288

FPR = 26/288 FNR = 15/262

FPR = 262/15 FNR = 288/26

FPR = 288/26 FNR = 262/15

NEW QUESTION 99
You have plotted the distribution of savings account sizes for a bank.

Based on the distribution shown in the exhibit, how would you proceed?

Data is extremely skewed. Replot the data on a logarithmic scale to get a better understanding of it.

Data is extremely skewed but looks bimodal. Replot the data in the range 2,500 – 10,000 to be certain.

Accounts of sizes greater than 2,500 are rare and are most likely outliers. Eliminate them from future analysis.

Data is extremely skewed. Split the analysis into two cohorts; accounts less than 2,500 and accounts greater than 2,500.

NEW QUESTION 100
Which word or phrase completes the statement? Data-ink ratio is to data visualization as _________.

Confusion matrix is to classifier

Data scientist is to big data

Seasonality is to ARIMA

K-means is to Naive Bayes

NEW QUESTION 101
Refer to the exhibit.

You have created a density plot of purchase amounts from a retail website as shown.
What should you do next?

Recreate the plot using the barplot() function

Use the rug() function to add elements to the plot

Recreate the density plot using a log normal distribution of the purchase amount data

Reduce the sample size of the purchase amount data used to create the plot

NEW QUESTION 102
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?

Goal 2 and 4

Goal 1 and 3

Goals 1, 2, 3, 4

Goals 2, 3, 4

NEW QUESTION 103
Which SQL OLAP grouping extension returns a result for each output row with 1 identifying a summary row and 0 identifying grouped rows?

CUBE

GROUPING

GROUP ID

ROLLUP

The GROUPING extension in SQL OLAP is used to return a result for each output row, where 1 identifies a summary row and 0 identifies grouped rows. This helps distinguish between regular grouped rows and summary rows in the result set.

NEW QUESTION 104
You are using a Logistic Regression model to determine if an applicant’s gender is a factor in determining whether or not they receive a bank loan. When you plot the results, you notice that the regression coefficient is zero.
What can be determined?

Sample size of the data is too small

Applicant’s gender influences the loan decision

Sample size of the data is too large

Applicant’s gender does not influence the loan decision

NEW QUESTION 105
How is HDFS defined?

Large “web table” capable of holding millions of rows and millions of columns

Row-column oriented datastore supporting redundancy and high availability

Reliable, redundant distributed file system

Reliable file system stored on a single extensible storage platform

NEW QUESTION 106
Refer to the Exhibit.

You are going into a meeting where you anticipate your manager will have a question on your dataset.
Specifically, your manager will want to know about customers that are classified as renters with a good credit status.
In order to prepare for the meeting, you create a rule: RENTER => GOOD CREDIT.
What is the confidence of this rule?

18%

41%

63%

73%

NEW QUESTION 107
How does an HBase data structure differ from an ANSI standard relational database management system (RDBMS)?

HBase is challenged by sparse data;
ANSI standard RDBMS handles missing values well by using null fields

HBase supports a single table with no implicit join ability to other tables; ANSI standard RDBMS supports multiple tables with join ability

HBase has strongly defined data columns;
ANSI standard RDBMS is more flexible with column definitions

HBase provides a wider variety of predefined data types for data;
ANSI standard RDBMS provides less variety

NEW QUESTION 108
The average purchase size from your online sales site is $17, 200. The customer experience team believes a certain adjustment of the website will increase sales.
A pilot study on a few hundred customers showed an increase in average purchase size of $1.47, with a significance level of p=0.1. The team runs a larger study, of a few thousand customers.
The second study shows an increased average purchase size of $0.74, with a significance level of 0.03.
What is your assessment of this study?

The change in purchase size is not practically important, and the good p-value of the second study is probably a result of the large study size.

The change in purchase size is small, but may aggregate up to a large increase in profits over the entire customer base.

The difference in the change in purchase size between the two studies is troubling; The team should run another, larger study.

The p-value of the second study shows a statistically significant change in purchase size. The new website is an improvement.

NEW QUESTION 109
Based on the exhibit, what is a likely issue with the data?

Saturated data; indicating potential issues with data definitions

Incomplete data; indicating potential issues with data transmission

Mis-scaled data; indicating potential issues with data entry

No obvious concerns with the data is visible

NEW QUESTION 110
You have been assigned to perform a study of the daily revenue effect of a pricing model of online transactions. When is the analytics lifecycle considered completed?

When written documentation has been produced and the code has been handed off to the DBA/operations.

When a model has been completely developed and the results have shown statistically acceptable results.

When the results of the model have been presented to both the internal analytics team and the business owner of the project.

When a model has been completely developed based on both a sample of the data and the entire set of data available.

NEW QUESTION 111
Refer to the exhibit.

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