D-DS-FN-23 Dumps (V9.02) Are the Most Current Materials for the Dell Data Science Foundations 2023 Exam Preparation: D-DS-FN-23 Free Dumps (Part 3, Q81-Q120) Online

1. You have been assigned to run a logistic regression model for each of 100 countries, and all the data is currently stored in a PostgreSQL database.

Which tool/library would you use to produce these models with the least effort?

2. A data scientist plans to classify the sentiment polarity of 10, 000 product reviews collected from the Internet.

What is the most appropriate model to use? Suppose labeled training data is available.

3. What does R code nv <- v[v < 1000] do?

4. You have run a Linear Regression model on the data shown in the graphic.

Which value is a reasonable guess for R-squared?

5. You have created a scatterplot of two continuous variables for 2000 records. You want to add a line to the scatterplot to check linearity of the data.

Which function would best address this need?

6. Why do the Naïve Bayesian classifier implementations use the log of probability value rather than the pure probability value?

7. Consider the following SQL query:

SELECT product_id FROM supplier_A

UNION

SELECT product_id FROM supplier_B;

What is the expected result?

8. In data visualization, which type of chart is recommended to represent frequency data?

9. Which word or phrase completes the statement; “Excessive emphasis color is to Bar chart as __________________.”?

10. You submit a MapReduce job to a Hadoop cluster. Although the job was successfully submitted, you notice that it is not completing.

What should be done?

11. Trend, seasonal, and cyclical are components of a time series.

What is another component?

12. Refer to the exhibit.

After analyzing a dataset, you report findings to your team:

1. Variables A and C are significantly and positively impacting the dependent variable.

2. Variable B is significantly and negatively impacting the dependent variable.

3. Variable D is not significantly impacting the dependent variable.

After seeing your findings, the majority of your team agreed that variable B should be positively impacting the dependent variable.

What is a possible reason the coefficient for variable B was negative and not positive?

13. Refer to the exhibit.

You have run a linear regression model against your data, and have plotted true outcome versus predicted outcome. The R-squared of your model is 0.75.

What is your assessment of the model?

14. If distributed Item-based Collaborative Filtering is an algorithm supported by Mahout, what is the use case category of the algorithm?

15. Your risk analysis team has access to new customer financial data. You want to use this data to improve your prediction of credit default. Previously, the team was using only credit bureau scores, loan size, and customer income to assess risk of default.

What is the null hypothesis that should be used to evaluate the model?

16. Which assumption makes the Naïve Bayesian classifier different from the general Bayesian model?

17. Refer to the exhibit.

You have plotted the distribution of savings account sizes for your bank.

How would you proceed, based on this distribution?

18. You have the following corpus of texts:

“The cat hit the dog.”

“The dog bit the mail carrier.”

“The mail carrier chased the truck.”

“The truck hit the wall while avoiding the dog that chased the cat.”

“The cat climbed the wall.”

If the tf-idf metric is used to score relevance for search and retrieval, which term has the highest discriminatory power?

19. What is required in a presentation for business analysts?

20. You are using the Apriori algorithm to determine the likelihood that a person who owns a home has a good credit score. You have determined that the confidence for the rules used in the algorithm is > 75%. You calculate lift = 1.011 for the rule, "People with good credit are homeowners".

What can you determine from the lift calculation?

21. You are provided with a dataset with the following attributes:

1. Customer ID

2. Gender flag

3. Amount spent in last four quarters

4. Age

5. Number of purchases made in last four quarters

6. Store credit card flag

7. Elite program member flag

Based on the attributes provided, your task is to find an alternate set of rules that helps to define which customers are more or less likely to become “Elite program members”.

Which analytical method would produce the desired rule set?

22. 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?

23. Review the following code:

SELECT pn, vn, sum(prc*qty) FROM sale

GROUP BY CUBE(pn, vn) ORDER BY 1, 2, 3;

Which combination of subtotals do you expect to be returned by the query?

24. What is a characteristic of an analytic sandbox?

25. On analyzing the results of a K-means clustering output, you noticed that splits on variables you expected to see were not observed.

What actions should be taken?

26. What describes the data repository represented by the 'A' in MAD?

27. Refer to the exhibit.

What is the approximate R-squared value for a linear regression model fitted to the data associated with this scatterplot?

28. Based on the graphic, what should be done to begin addressing chart junk?

29. Refer to the graphic.

How would you run the MADlib kmeans function?

30. What should be subtracted to remove a simple linear trend from a time series?

31. Which visualization technique should be avoided?

32. 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 tabluated them as shown in the exhibit.

What are the Precision and Recall rate of the model?

33. What provides the means for matching and manipulating text strings in SQL?

34. Refer to the exhibit.

You are assigned to do an end of the year sales analysis of 1, 000 different products, based on the transaction table.

Which column in the end of year report requires the use of a window function?

35. You fit a Logistic Regression model to your training data and notice that the variable X has an infinite magnitude coefficient.

What does this indicate?

36. What would be considered "Big Data"?

37. You have been assigned to run a linear regression model for each of 5, 000 distinct districts, and all the data is currently stored in a PostgreSQL database.

Which tool/library would you use to produce these models with the least effort?

38. In addition to quantitative and technical skills, what is a key aspect of the profile of a data scientist?

39. An IT department deployed a spam filter to reduce the amount of junk e-mail received by its employees. After six months, they notice that the spam filter is less effective than when initially deployed.

They examine the system running the spam filter and it appears to be operating normally.

What action would improve the effectiveness of the spam filter?

40. Which component of a final presentation focuses on how to deploy the model?