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NEW QUESTION # 150
Which characteristic applies mainly to Data Science as opposed to Business Intelligence?
- A. Advanced analytical methods
- B. Focus on structured data
- C. Robust reporting
- D. Data dashboards
Answer: A
NEW QUESTION # 151
Which word or phrase completes the statement; "A theater actor is to 'artistic and expressive' as a data scientist is to."?
- A. Logical and steadfast
- B. Introverted and technical
- C. Communicative and collaborative
- D. Independent and intelligent
Answer: C
NEW QUESTION # 152
In MADlib what does MAD stand for?
- A. Magnetic, Agile, Deep
- B. Machine Learning, Algorithms for Databases
- C. Modular, Accurate, Dependable
- D. Mathematical Algorithms for Databases
Answer: A
NEW QUESTION # 153
Refer to the exhibit.
You are using K-means clustering to classify customer behavior for a large retailer. You need to determine the optimum number of customer groups. You plot the within-sum-of- squares (wss) data as shown in the exhibit.
How many customer groups should you specify?
- A. 0
- B. 1
- C. 2
- D. 3
Answer: C
NEW QUESTION # 154
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?
- A. Goals 2, 3, 4
- B. Goal 1 and 3
- C. Goals 1, 2, 3, 4
- D. Goal 2 and 4
Answer: D
NEW QUESTION # 155
What is holdout data?
- A. a subset of the provided data set selected at random and used to validate the model
- B. a subset of the provided data set selected at random and used to initially construct the model
- C. a subset of the provided data set that is removed by the data scientist because it contains data errors
- D. a subset of the provided data set that is removed by the data scientist because it contains outliers
Answer: A
NEW QUESTION # 156
Consider the example of an analysis for fraud detection on credit card usage. You will need to ensure higher-risk transactions that may indicate fraudulent credit card activity are retained in your data for analysis, and not dropped as outliers during pre-processing.
What will be your approach for loading data into the analytical sandbox for this analysis?
- A. ELT
- B. OLTP
- C. EDW
- D. ETL
Answer: A
NEW QUESTION # 157
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. K-means clustering
- B. Association rules
- C. Logistic regression
- D. Naïve Bayes classifier
Answer: A
NEW QUESTION # 158
What are the characteristics of Big Data?
- A. Data volume, business importance, and data structure variety.
- B. Data volume, processing complexity, and data structure variety.
- C. Data volume, processing complexity, and business importance.
- D. Data type, processing complexity, and data structure variety.
Answer: B
NEW QUESTION # 159
You have plotted the distribution of savings account sizes for a bank.
Based on the distribution shown in the exhibit, how would you proceed?
- A. Accounts of sizes greater than 2,500 are rare and are most likely outliers. Eliminate them from future analysis.
- B. Data is extremely skewed but looks bimodal. Replot the data in the range 2,500 - 10,000 to be certain.
- C. Data is extremely skewed. Split the analysis into two cohorts; accounts less than 2,500 and accounts greater than 2,500.
- D. Data is extremely skewed. Replot the data on a logarithmic scale to get a better understanding of it.
Answer: D
NEW QUESTION # 160
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?
- A. Bit
- B. Chased
- C. Truck
- D. Dog
Answer: A
NEW QUESTION # 161
The web analytics team uses Hadoop to process access logs. They now want to correlate this data with structured user data residing in a production single-instance JDBC database. They collaborate with the production team to import the data into Hadoop.
Which tool should they use?
- A. Scribe
- B. Sqoop
- C. Chukwa
- D. Pig
Answer: B
NEW QUESTION # 162
You have scored your Naïve Bayesian Classifier model on "hold out" test data for cross validation. You have determined the way the samples scored and have tabulated them as shown in the exhibit.
What are the Precision and Recall rates of the model?
- A. Precision = 277/262 Recall = 288/262
- B. Precision = 262/277 Recall = 262/288
- C. Precision = 288/262 Recall = 277/262
- D. Precision =262/288 Recall = 262/277
Answer: B
NEW QUESTION # 163
You are testing two new weight-gain formulas for puppies. The test gives the results: Control group: 1% weight gain Formula A. 3% weight gain Formula B. 4% weight gain A one-way ANOVA returns a p-value = 0.027
What can you conclude?
- A. Formula A and Formula B are both effective at promoting weight gain.
- B. Formula A and Formula B are about equally effective at promoting weight gain.
- C. Formula B is more effective at promoting weight gain than Formula A.
- D. Either Formula A or Formula B is effective at promoting weight gain.
Answer: D
NEW QUESTION # 164
What is the purpose of the process step "parsing" in text analysis?
- A. performs the search and/or retrieval in finding a specific topic or an entity in a document
- B. computes the TF-IDF values for all keywords and indices
- C. executes the clustering and classification to organize the contents
- D. imposes a structure on the unstructured/semi-structured text for downstream analysis
Answer: D
NEW QUESTION # 165
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 observations seem to come from two different populations, but this model fits them both equally well.
- B. The R-squared is good. The model should perform 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 extreme-valued outliers may negatively affect the model's performance. Remove them to see if the R-squared improves over typical data.
Answer: C
NEW QUESTION # 166
What is the output format from the Map function of MapReduce?
- A. Compressed index
- B. Unique key record and separate records of all possible values
- C. Key-value pairs
- D. Binary representation of keys concatenated with structured data
Answer: C
NEW QUESTION # 167
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