Data Scientist and Big Data Analytics Foundations (D-DS-FN-23) Certification Exam Sample Questions

D-DS-FN-23 Dumps Free, D-DS-FN-23 PDF Download, Data Scientist and Big Data Analytics Foundations Dumps Free, Data Scientist and Big Data Analytics Foundations PDF Download, D-DS-FN-23 Free DownloadHere are the sample questions which will help you be familiar with Dell Technologies Data Scientist and Big Data Analytics Foundations 2023 (D-DS-FN-23) exam style and structure. We encourage you to try our Demo Data Scientist and Big Data Analytics Foundations Certification Practice Exam to measure your understanding of exam structure in an environment which simulates the Data Scientist and Big Data Analytics Foundations 2023 Certification test environment.

To make your preparation more easy for Data Scientist and Big Data Analytics Foundations 2023 (D-DS-FN-23) exam, we strongly recommend you to use our Premium Data Scientist and Big Data Analytics Foundations Certification Practice Exam. According to our survey with certified candidates, you can easily score 85% in your actual Dell Technologies Certification exam if you can score 100% in our premium Data Scientist and Big Data Analytics Foundations Certification practice exams.

Dell Technologies D-DS-FN-23 Sample Questions:

01. In the Map Reduce framework, what is the purpose of the Reduce function?
a) It aggregates the results of the Map function and generates processed output
b) It distributes the input to multiple nodes for processing
c) It writes the output of the Map function to storage
d) It breaks the input into smaller components and distributes to other nodes in the cluster
 
02. What is an example of a null hypothesis?
a) that a newly created model provides a prediction of a null sample mean
b) that a newly created model provides a prediction of a null population 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 that will be well fit to the null distribution
 
03. You submit a Map Reduce job to a Hadoop cluster. However, you notice that although the job was successfully submitted, it is not completing.
What should be done to identify the issue?
a) Ensure DataNode is running
b) Ensure NameNode is running
c) Ensure JobTracker is running
d) Ensure TaskTracker is running
 
04. How are window functions different from regular aggregate functions?
a) Rows retain their separate identities and the window function can access more than the current row.
b) Rows are grouped into an output row and the window function can access more than the current row.
c) Rows retain their separate identities and the window function can only access the current row.
d) Rows are grouped into an output row and the window function can only access the current row.
 
05. Your colleague, who is new to Hadoop, approaches you with a question. They want to know how best to access their data. This colleague has a strong background in data flow languages and programming.
Which query interface would you recommend?
a) Hive
b) Pig
c) HBase
d) Howl
 
06. Before you build an ARMA model, how can you tell if your time series is weakly stationary?
a) The mean of the series is close to 0.
b) There appears to be a constant variance around a constant mean.
c) The series is normally distributed.
d) There appears to be no apparent trend component.
 
07. 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 TaskTracker is running.
b) Ensure that the JobTracker is running
c) Ensure that the NameNode is running
d) Ensure that a DataNode is running
 
08. How does Pig’s use of a schema differ from that of a traditional RDBMS?
a) Pig's schema requires that the data is physically present when the schema is defined
b) Pig's schema supports a single data type
c) Pig's schema is optional
d) Pig's schema is required for ETL
 
09. What is the primary function of the NameNode in Hadoop?
a) Keeps track of which MapReduce jobs have been assigned to each TaskTracker
b) Monitors the state of each JobTracker node and signals an event if unavailable
c) Runs some number of mapping tasks against its assigned data
d) Acts as a regulator/resolver among clients and DataNodes
 
10. For which class of problem is Map Reduce most suitable?
a) Embarrassingly parallel
b) Minimal result data
c) Simple marginalization tasks
d) Non-overlapping queries

Answers:

Question: 1 Answer: a Question: 2 Answer: c
Question: 3 Answer: d Question: 4 Answer: a
Question: 5 Answer: b Question: 6 Answer: b
Question: 7 Answer: a Question: 8 Answer: c
Question: 9 Answer: d Question: 10 Answer: a

Note: Please write us on feedback@analyticsexam.com if you find any data entry error in these Data Scientist and Big Data Analytics Foundations 2023 (D-DS-FN-23) sample questions.

Rating: 4.9 / 5 (120 votes)