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EMC D-DS-FN-23 Exam Syllabus Topics:
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NEW QUESTION # 209
In a Logistic Regression, the coefficient for "age" equals -3.
What is the correct interpretation of the Logistic Regression coefficient, holding all other variables constant?
- A. For every 1 unit increase in age, the dependent variable is multiplied by -3
- B. When age increases by 1 unit, the odds of response are multiplied by e(-3) or 0.05
- C. When age decreases by 3 units, the odds of response are multiplied by e(-3) or 0.05
- D. For every 1 unit increase in age, the dependent variable is reduced by 3 units
Answer: B
NEW QUESTION # 210
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. Business User
- C. Project Manager
- D. Project Sponsor
Answer: A
NEW QUESTION # 211
Which ROC curve represents a perfect model fit?
A)
B)
C)
D)
- A. Exhibit D
- B. Exhibit C
- C. Exhibit A
- D. Exhibit B
Answer: C
NEW QUESTION # 212
Which word or phrase completes the statement? Business Intelligence is to monitoring trends as Data Science is to ________ trends.
- A. Discarding
- B. Optimizing
- C. Predicting
- D. Driving
Answer: C
NEW QUESTION # 213
What is one modeling or descriptive statistical function in MADlib that is typically not provided in a standard relational database?
- A. Variance
- B. Linear regression
- C. Expected value
- D. Quantiles
Answer: B
NEW QUESTION # 214
You have the data from a popular e-commerce website. You are exploring the time spent (in seconds) on the website by 100,000 customers across 14 different product categories.
What visualization can be used to represent the relationship between time spent and product category?
- A. Hexbin plot
- B. Rug plot
- C. Box and whisker plot
- D. Scatter plot
Answer: C
Explanation:
A box and whisker plot is ideal for visualizing the relationship between time spent and product category, especially when you have multiple categories. It shows the distribution of time spent in each product category, including the median, quartiles, and any potential outliers.
NEW QUESTION # 215
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?
- A. Variable B needs a quadratic transformation due to its relationship to the dependent variable
- B. Variable B is interacting with another variable due to correlated inputs
- C. Variable B needs a logarithmic transformation due to its relationship to the dependent variable
- D. The information gain from variable B is already provided by another variable
Answer: B
NEW QUESTION # 216
Refer to the exhibit.
Click on the calculator icon in the upper left corner. An analyst is searching a corpus of documents for the topic "solid state disk".
In the Exhibit, Table A provides the inverse document frequency for each term across the corpus. Table B provides each term's frequency in four documents selected from corpus.
Which of the four documents is most relevant to the analyst's search?
- A. Document B
- B. Document A
- C. Document C
- D. Document D
Answer: C
NEW QUESTION # 217
Why do the Naïve Bayesian classifier implementations use the log of probability value rather than the pure probability value?
- A. To ensure the conditional independence of attribute values
- B. To obtain a more accurate estimate of the probabilities without the need for a Laplace smoothing
- C. To invalidate the variables that are continuous
- D. To avoid numerical underflow errors in high dimensional problems
Answer: D
NEW QUESTION # 218
In linear regression, what indicates that an estimated coefficient is significantly different than zero?
- A. A small p-value
- B. The estimated coefficient is greater than 3
- C. R-squared near 0
- D. R-squared near 1
Answer: A
NEW QUESTION # 219
Which word or phrase completes the statement; "A theater actor is to 'artistic and expressive' as a data scientist is to."?
- A. Independent and intelligent
- B. Logical and steadfast
- C. Introverted and technical
- D. Communicative and collaborative
Answer: D
NEW QUESTION # 220
You have created a Logistic Regression model to predict customer churn for your company. The company's Marketing department wants to use your model to identify at-risk customers and offer incentives to keep them from leaving.
Using two different thresholds for the model provides the two confusion matrices shown in the graphic.
Marketing understands the relative costs of missing at-risk customers versus offering incentives to customers who are not at risk. Therefore, you need their advice on how to set the appropriate threshold on the churn model.
You are meeting with the Marketing team. In the meeting, you plan to state: "Raising the threshold from 0.5 to 0.75 reduces the number of unnecessary incentives that can be offered, at the cost of missing more of the customers who churned." What is the most appropriate visual to reinforce this statement?
- A.
- B.
- C.
- D.
Answer: D
NEW QUESTION # 221
In naïve Bayes implementations, how is the possibility of zero probabilities resulting from unobserved attribute/classifier pairs properly handled?
- A. Add a small quantity to each of the observed counts
- B. Add a large quantity to each of the observed counts
- C. Double the observed counts
- D. Square the observed counts
Answer: A
NEW QUESTION # 222
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?
- A. The extreme-valued outliers may negatively affect the model's performance. Remove them to see if the R-squared improves over typical data.
- B. The observations seem to come from two different populations, but this model fits them both equally well.
- C. The R-squared may be biased upwards by the extreme-valued outcomes. Remove them and refit to get a better idea of the model's quality over typical data.
- D. The R-squared is good. The model should perform well.
Answer: C
NEW QUESTION # 223
Refer to the exhibit.
You are asked to write a report on how specific variables impact your client's sales using a data set provided to you by the client. The data includes 15 variables that the client views as directly related to sales, and you are restricted to these variables only.
After a preliminary analysis of the data, the following findings were made:
1. Multicollinearity is not an issue among the variables
2. Only three variables-A, B, and C-have significant correlation with sales You build a linear regression model on the dependent variable of sales with the independent variables of A, B, and C.
The results of the regression are seen in the exhibit.
Which interpretation is supported by the analysis?
- A. Variables A, B, and C are significantly impacting sales and are effectively estimating sales
- B. Due to the R2 of 0.10, the model is not valid - a different analytical model should be attempted
- C. Due to the R2 of 0.10, the model is not valid - the linear regression should be rerun with all 15 variables forced into the model to increase the R2
- D. Variables A, B, and C are significantly impacting sales, but are not effectively estimating sales
Answer: D
NEW QUESTION # 224
Refer to the exhibit.
In the exhibit, the x-axis represents the derived probability of a borrower defaulting on a loan. Also in the exhibit, the pink represents borrowers that are known to have not defaulted on their loan, and the blue represents borrowers that are known to have defaulted on their loan.
Which analytical method could produce the probabilities needed to build this exhibit?
- A. Logistic Regression
- B. Discriminant Analysis
- C. Association Rules
- D. Linear Regression
Answer: A
NEW QUESTION # 225
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. EDW
- B. OLTP
- C. ELT
- D. ETL
Answer: C
NEW QUESTION # 226
What type of data is represented in the exhibit?
- A. Semi-structured
- B. Quasi-structured
- C. Structured
- D. Unstructured
Answer: C
NEW QUESTION # 227 
Refer to the exhibit.
To predict whether or not a customer will renew their annual property insurance policy, an insurance company built and operationalized a naive Bayes classification model. In the model, there are two class labels, renewal and non-renewal, that are assigned to each customer based on their attributes.
A subset of the key attributes, their values, and corresponding conditional probabilities are provided in the exhibit.
A customer has the following attributes:
# Age is greater than 65 years
# Owns their own home
# Renewal month is August
If 20% of customers do not renew the police every year, what is the score for a renewal in the naive Bayesian model for the customer described above?
- A. 0.0022
- B. 0.0216
- C. 0 0027
- D. 0.0270
Answer: B
Explanation:
The formula for Naive Bayes is:
For the renewal class, we are given:
# P(Class = Renewal) = 0.8 (since 80% renew the policy)
# P(Age > 65 years | Renewal) = 0.3
# P(Housing = Own | Renewal) = 0.9
# P(Renewal Month = August | Renewal) = 0.1
P(Renewal) = P(Renewal) × P(Age > 65 years | Renewal) × P(Housing = Own | Renewal) × P (Renewal Month = August | Renewal) P(Renewal) = 0.8 × 0.3 × 0.9 × 0.1 = 0.0216
NEW QUESTION # 228
Which chart type is intended to display time series data?
- A. [Histogram
- B. Pie chart
- C. Line chart
- D. Bar chart
Answer: C
Explanation:
A line chart is specifically designed to display time series data. It shows data points in a sequential order, making it easy to observe trends over time.
NEW QUESTION # 229
You have run the association rules algorithm on your data set, and the two rules {banana, apple} =>
{grape} and {apple, orange}=> {grape} have been found to be relevant.
What else must be true?
- A. {grape, apple, orange} must be a frequent itemset.
- B. {banana, apple, grape, orange} must be a frequent itemset.
- C. {banana, apple} => {orange} must be a relevant rule.
- D. {grape} => {banana, apple} must be a relevant rule.
Answer: A
NEW QUESTION # 230
What does a leaf node represent in a decision tree?
- A. Decision point on a variable
- B. Assigned class label
- C. Outcome of a test on a variable
- D. Root of the decision tree
Answer: B
NEW QUESTION # 231
After running a density plot you realize that the data has a long tail to the right. What can you do to make the dataset more normally distributed?
- A. Use a scatter plot to obtain a better picture
- B. Use a histogram to obtain a better picture
- C. Apply a square transformation
- D. Apply a logarithmic transformation
Answer: D
Explanation:
A logarithmic transformation is commonly used to reduce right skewness (long tail to the right) and make data more normally distributed.
NEW QUESTION # 232
Consider the following itemsets:
(hat, scarf, coat)
(hat, scarf, coat, gloves)
(hat, scarf, gloves)
(hat, gloves)
(scarf, coat, gloves)
If the minimum support is 50%, what represents the complete list of frequent 2-itemsets?
- A. (hat, scarf), (scarf, coat), (coat, gloves)
- B. (hat, scarf), (hat, gloves), (scarf, gloves), (scarf, coat)
- C. (scarf, gloves), (scarf, coat) (hat, gloves)
- D. (hat, scarf), (hat, gloves)
Answer: B
NEW QUESTION # 233
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