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[Hardware] Exam D-DS-FN-23 Tutorials & Latest D-DS-FN-23 Exam Materials

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【Hardware】 Exam D-DS-FN-23 Tutorials & Latest D-DS-FN-23 Exam Materials

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EMC D-DS-FN-23 Exam Syllabus Topics:
TopicDetails
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
  • 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.
Topic 3
  • 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.
Topic 4
  • 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.

EMC Dell Data Science Foundations Sample Questions (Q170-Q175):NEW QUESTION # 170
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?
  • A. FPR = 288/26 FNR = 262/15
  • B. FPR = 262/15 FNR = 288/26
  • C. FPR = 26/288 FNR = 15/262
  • D. FPR = 15/262 FNR = 26/288
Answer: D

NEW QUESTION # 171
Why do the Naïve Bayesian classifier implementations use the log of probability value rather than the pure probability value?
  • A. To avoid numerical underflow errors in high dimensional problems
  • B. To invalidate the variables that are continuous
  • C. To obtain a more accurate estimate of the probabilities without the need for a Laplace smoothing
  • D. To ensure the conditional independence of attribute values
Answer: A

NEW QUESTION # 172
Which clause is required by all window functions?
  • A. RANGE()
  • B. ORDER BY
  • C. PARTITION BY
  • D. OVER()
Answer: D

NEW QUESTION # 173
Assume you are performing an analysis to determine fraud detection on credit card usage. You will need to ensure higher-risk transactions. These may indicate that fraudulent credit card activity is retained in your data for analysis and not dropped as outliers during pre- processing.
What is the approach for loading data into the analytical sandbox for this analysis?
  • A. ETL
  • B. ELT
  • C. OLTP
  • D. EDW
Answer: B

NEW QUESTION # 174
In association rules, given items X and Y, what does lift measure?
  • A. How many times more often X and Y occur together than expected if they were statistically independent, expressed as a ratio
  • B. Percentage of transactions with Xthat also contain Y
  • C. Difference in the probability ofX and Y appearing together compared with expectations as if they were statistically independent
  • D. Percentage of transactions that contain an itemset with X
Answer: A
Explanation:
Lift measures how many times more often X and Y occur together than would be expected if they were statistically independent. It is expressed as a ratio, and a lift greater than 1 indicates a positive association between X and Y.

NEW QUESTION # 175
......
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