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NEW QUESTION # 195
In which lifecycle stage are initial hypotheses formed?
- A. Model building
- B. Discovery
- C. Data preparation
- D. Model planning
Answer: B
NEW QUESTION # 196
Which data asset is an example of semi-structured data?
- A. Webserver log
- B. News article
- C. Database table
- D. XML data file
Answer: D
NEW QUESTION # 197
Refer to the exhibit.
You are using k-means clustering to discover groupings within a data set. You plot within- sum-of-squares (wss) of multiple cluster sizes.
Based on the exhibit, how many clusters should you use in your analysis?
- A. 0
- B. 1
- C. 2
- D. 3
Answer: C
NEW QUESTION # 198
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?
- A. Leverage of the rules is low
- B. The rule is coincidental
- C. Support for the association is low
- D. The rule is true
Answer: B
NEW QUESTION # 199
What is a benefit of Spark in-memory data processing as opposed to using MapReduce?
- A. Allows parallel processing, which MapReduce does not support
- B. Removes the need to use disks at all, which reduces cost
- C. Supports processing unstructured data, which MapReduce does not allow
- D. Avoids writing intermediate data to disk, which speeds up processing
Answer: D
Explanation:
Spark's in-memory data processing avoids the need to write intermediate data to disk, which significantly speeds up processing compared to MapReduce, which writes intermediate data to disk after each operation.
This makes Spark much faster for iterative tasks.
NEW QUESTION # 200
In which phase of the analytic lifecycle would you expect to spend most of the project time?
- A. Discovery
- B. Communicate Results
- C. Data preparation
- D. Operationalize
Answer: C
NEW QUESTION # 201
You are assigned the task of creating customer profiles for your company. In your database, you have
25 key input variables that come together to define 2,500 customers. You decide to run a K-means cluster analysis on the 25 input variables based on k=4 to build your profiles.
Your analysis resulted in four cluster populations:
Cluster A=1,000 customers
Cluster B=560 customers
Cluster C=925 customers
Cluster D=15 customers
What should be attempted first to more evenly distribute the customer population across clusters?
- A. Remove the 15 customers in Cluster D from the population
- B. Increase K from 4 to 5
- C. Reduce K from 4 to 3
- D. Remove some of the input variables from the analysis
Answer: C
NEW QUESTION # 202
What are categorized as cluster and workflow management tools for Hadoop?
- A. Drill, Hive, and HBase
- B. Spark, Tez, and Cassandra
- C. Ambari, Oozie, and Zookeeper
- D. Flume, Sqoop, and Storm
Answer: C
Explanation:
Ambari, Oozie, and Zookeeper are tools used for cluster and workflow management in Hadoop. Ambari manages and monitors clusters, Oozie handles workflow scheduling, and Zookeeper coordinates distributed processes.
NEW QUESTION # 203
You are performing a market basket analysis using the Apriori algorithm.
Which measure is a ratio describing the how many more times two items are present together than would be expected if those two items are statistically independent?
- A. Confidence
- B. Lift
- C. Leverage
- D. Support
Answer: B
NEW QUESTION # 204
Which of the following is an example of quasi-structured data?
- A. Customer record table
- B. Clickstream data
- C. OLTP
- D. OLAP
Answer: B
NEW QUESTION # 205
What is the output format from the Map function of MapReduce?
- A. Binary representation of keys concatenated with structured data
- B. Unique key record and separate records of all possible values
- C. Compressed index
- D. Key-value pairs
Answer: D
NEW QUESTION # 206
Which SQL set operator returns rows that exist in the first SELECT statement answer set but not in the second SELECT statement?
- A. EXCEPT
- B. UNION ALL
- C. UNION
- D. INTERSECT
Answer: A
Explanation:
The EXCEPT operator returns rows that exist in the result set of the first SELECT statement but not in the second. It removes duplicates and only shows the difference between the two datasets.
NEW QUESTION # 207
Which word or phrase completes the statement? A Data Scientist would consider that a RDBMS is to a Table as R is to a ________.
- A. Data frame
- B. Matrix
- C. Array
- D. List
Answer: A
NEW QUESTION # 208
In addition to the business question and descriptions of available data sets, what else would an analytic plan include?
- A. Existing solutions to the business question
- B. Access credentials to database and/or Hadoop cluster
- C. Initial hypotheses
- D. Relevant statistical tests
Answer: C
NEW QUESTION # 209
Consider these itemsets:
(hat, scarf, coat)
(hat, scarf, coat, gloves)
(hat, scarf, gloves)
(hat, gloves)
(scarf, coat, gloves)
What is the confidence of the rule (gloves -> hat)?
- A. 66%
- B. 60%
- C. 75%
- D. 80%
Answer: C
NEW QUESTION # 210
When would you use GROUP BY ROLLUP clause in your OLAP query?
- A. where only the subtotals are to be included in the output
- B. where only specific subtotals and grand totals for a combination of variables are to be included in the output
- C. where all subtotals and grand totals are to be included in the output
- D. where only the grand totals are to be included in the output
Answer: C
NEW QUESTION # 211
For which class of problem is Map Reduce most suitable?
- A. Simple marginalization tasks
- B. Embarrassingly parallel
- C. Minimal result data
- D. Non-overlapping queries
Answer: B
NEW QUESTION # 212
A study was run to identify general dietary patterns among the residents of a small town. Twelve thousand people were surveyed and the data was subject to K-means clustering.
In one of the iterations, there were six clusters formed with 38, 1560, 1799, 2560, 2893, and 3150 respondents.
What should be the next step in identifying optimal clusters?
- A. Multiply each variable by its standard deviation
- B. Remove 38 respondents because the 5 clusters seem to be well distributed
- C. Determine the optimal number of clusters by plotting the Within Sum of Squares (WSS) values as a function of K
- D. Add more categorical variables to the dataset to maximize the Within Sum of Squares (WSS) value for K=6
Answer: C
NEW QUESTION # 213
Consider a database with the following four transactions:
Transaction 1: {cheese, bread, milk}
Transaction 2: {soda, bread, milk}
Transaction 3: {cheese, bread}
Transaction 4: {cheese, soda, juice}
Assuming a minimum support of 50%, which itemsets would be generated in the second step of the Apriori algorithm?
- A. {cheese, bread}, {cheese, bread, milk}
- B. {cheese, bread}, {cheese, milk}, {bread, milk}
- C. {cheese}, {bread}, {milk}, {soda}
- D. {cheese, bread}, {bread, milk}
Answer: D
NEW QUESTION # 214
How are window functions different from regular aggregate functions?
- A. Rows retain their separate identities and the window function can only access the current row.
- B. Rows are grouped into an output row and the window function can only access the current row.
- C. Rows are grouped into an output row and the window function can access more than the current row.
- D. Rows retain their separate identities and the window function can access more than the current row.
Answer: D
NEW QUESTION # 215 
Refer to the exhibit, which shows pairwise counts for items purchased together.
Consider the following association rule: Milk -> Eggs
What is value of the lift?
- A. 0
- B. 0.264
- C. 70.81
- D. 1.18
Answer: D
Explanation:
P(Milk#Eggs) is the probability of both Milk and Eggs being bought together, which is the count for Milk and Eggs (110).
P(Milk)) is the probability of Milk being bought, which is the total for Milk (233) divided by the grand total (374).
P(Eggs) is the probability of Eggs being bought, which is the total for Eggs (150) divided by the grand total (374).
The value of the lift for the association rule Milk -> Eggs is approximately 1.18.
NEW QUESTION # 216
In the Map Reduce framework, what is the purpose of the Map Function?
- A. It breaks the input into smaller components and distributes to other nodes in the cluster
- B. It sorts the results of the Reduce function
- C. It collects the output of the Reduce function
- D. It processes the input and generates key-value pairs
Answer: D
NEW QUESTION # 217
You have an automotive database containing numeric characteristics such as engine size, horsepower, and top speed.
Which technique could you use to group similar cars together?
- A. Naïve Bayes classifier
- B. K-means clustering
- C. Association rules
- D. Logistic regression
Answer: B
NEW QUESTION # 218
A data scientist is given an R data frame, "empdata", with the columns Age, Salary, Occupation, Education, and Gender. The data scientist would like to examine only the Salary and Occupation columns for ages greater than 40.
Which command extracts the appropriate rows and columns from the data frame?
- A. empdata[c("Salary", "Occupation"), empdata$Age > 40]
- B. empdata[Age > 40, ("Salary", "Occupation")]
- C. empdata[, c("Salary", "Occupation")]$Age > 40
- D. empdata[empdata$Age > 40, c("Salary", "Occupation")]
Answer: D
NEW QUESTION # 219
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