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NEW QUESTION 1
A data analyst has been asked to create an ad-hoc sales report for the Chief Executive Officer (CEO). Which of the following should be included in the report?
A.   The sales representatives’ home addresses.
B.   Line-item SKU numbers.
C.   YTD total sales.
D.   The customers’ first and last names.
Answer: C
NEW QUESTION 2
A hypothesis test sometimes rejects the null hypothesis even if the true value of the population parameter is the same as the value in the null hypothesis. This type of result is known as: ____.
A.   A Type I Error.
B.   A Type II Error.
C.   A correct inference.
D.   The confidence level of the inference.
Answer: A
NEW QUESTION 3
The sales of a grocery store had an average of $8,000 per day. The store introduced several advertising campaigns in order to increase sales. To determine whether the advertising campaigns have been effective in increasing sales, a sample of 64 days of sales was selected, and the sample mean was $8,300 per day. The correct null and alternative hypotheses to test whether there has been a significant increase are: ____.
A.   Null: Sample mean is 8,000; Alternative: Sample mean is greater than or equal to 8,000.
B.   Null: Sample mean is 8,000; Alternative: Sample mean is greater than 8,000.
C.   Null: Population mean is 8,000; Alternative: Population mean is greater than or equal to 8,000.
D.   Null: Population mean is 8,000; Alternative: Population mean is greater than 8,000.
Answer: D
Explanation:
Since you are trying to determine whether sales increased above the current average of $8000, the population mean for the null hypothesis is $8000; whereas the mean for the alternative hypothesis is greater than $8000. Hypotheses are always stated in terms of population you are working with, ruling out options A and B. The test is whether sales are higher than the average of $8000 per day, which rules out option C.
NEW QUESTION 4
Which of the following can be used to translate data into another form so it can only be read by a user who has a key or a password?
A.   Data encryption.
B.   Data transmission.
C.   Data protection.
D.   Data masking.
Answer: A
Explanation:
Data encryption is a way of translating data from plaintext (unencrypted) to ciphertext (encrypted). Users can access encrypted data with an encryption key and decrypted data with a decryption key.
NEW QUESTION 5
Q3 2020 has just ended, and now a data analyst needs to create an ad-hoc sales report that demonstrates how well the Q3 2020 promotion went versus last year’s Q3 promotion. Which of the following date parameters should the analyst use?
A.   2019 vs. YTD 2020
B.   Q3 2019 vs. Q3 2020
C.   YTD 2019 vs. YTD 2020
D.   Q4 2019 vs. Q3 2020
Answer: B
NEW QUESTION 6
What Python library provides data analysts with access to tools that allow them to better structure data?
A.   Numpy
B.   TensorFlow
C.   pandas
D.   Keras
Answer: C
NEW QUESTION 7
Melinda is analyzing a movie dataset, where individual films have a star rating between 1 and 5. What type of data is this?
A.   Nonparametric data.
B.   Redundant data.
C.   Duplicate data.
D.   Data outlier.
Answer: A
NEW QUESTION 8
Which of the following is an example of a discrete data type?
A.   8in (20cm)
B.   5 kids
C.   2.5mi (4km)
D.   10.7lbs (4.9kg)
Answer: B
NEW QUESTION 9
George wants to integrate data from his city’s open data portal. Reading the website, he sees that he can download the data he wants as a CSV file. After manually downloading the file, he writes the code to transform the data and load it into his database. Presuming the data changes once a month, what can George do to ensure he has the most up-to-date data from the city?
A.   Manually check the city’s website every day.
B.   Contact the city and encourage the development of an API.
C.   Automate the process that downloads, transforms, and uploads the CSV file.
D.   Nothing, George has already successfully loaded the data.
Answer: C
NEW QUESTION 10
Which of the following contains alphanumeric values?
A.   10.1Ε?
B.   13.6
C.   1347
D.   A3J7
Answer: D
NEW QUESTION 11
When building a word cloud, what feature varies with the frequency that a word appears in the text?
A.   Font size.
B.   Font color.
C.   Font style.
D.   Font placement.
Answer: A
NEW QUESTION 12
How should data classifications be assigned?
A.   According to sensitivity.
B.   According to sensitivity and criticality.
C.   According to criticality.
D.   According to sensitivity, criticality and age.
Answer: B
NEW QUESTION 13
What type of metric is commonly shown on dashboards to assist senior leaders in assessing the organization’s progress toward significant goal?
A.   KMI
B.   KRI
C.   KPI
D.   KCI
Answer: C
NEW QUESTION 14
You have two databases tables that you would like to join together using a foreign key relationship. What term best describes this action?
A.   Merging.
B.   Blending.
C.   Mixing.
D.   Appending.
Answer: A
Explanation:
Data merging is the process of combining two or more data sets into a single data set. Most often, this process is necessary when you have raw data stored in multiple files, worksheets, or data tables, that you want to analyze all in one go.
NEW QUESTION 15
The scores of ten students in a test are 17, 23, 30, 36, 45, 51, 58, 66, 72, 77. Which of the following value is the measure of dispersion “range” between the scores of ten students in a test?
A.   80
B.   90
C.   70
D.   60
Answer: D
Explanation:
The correct answer is: 60. Range is the interval between the highest and the lowest score. Range is a measure of variability or scatteredness of the varieties or observations among themselves and does not give an idea about the spread of the observations around some central value. Symbolically R = Hs – Ls. Where R = Range; Hs is the ‘Highest score’ and Ls is the Lowest Score. The scores of ten students in a test are: 17, 23, 30, 36, 45, 51, 58, 66, 72, 77. The highest score is 77 and the lowest score is 17. So the range is the difference between these two scores Range = 77 – 17 = 60.
NEW QUESTION 16
You recently downloaded a file containing website visitor logs from your organization’s web server. What term best describes these logs at this point in the process?
A.   Intelligence.
B.   Information.
C.   Schema.
D.   Data.
Answer: D
NEW QUESTION 17
Which dimension of data quality ensures that data stored in multiple locations is the same?
A.   Consistency.
B.   Validity.
C.   Completeness.
D.   Accuracy.
Answer: A
Explanation:
Data consistency means that each user sees a consistent view of the data, including visible changes made by the user’s own transactions and transactions of other users.
NEW QUESTION 18
When taking the test at home, how much extra time is allowed compared to the in-person test?
A.   None.
B.   30 minutes.
C.   10 minutes.
D.   15 minutes.
Answer: A
NEW QUESTION 19
Which one the following is not considered an aggregate function?
A.   SUM
B.   SELECT
C.   MIN
D.   MAX
Answer: B
NEW QUESTION 20
The ACME Corporation hired an analyst to detect data quality issues in their excel documents. Which of the following are the most common issues? (Choose two.)
A.   Apostrophe.
B.   Symbols.
C.   Commas.
D.   Duplicates.
E.   Misspellings.
Answer: DE
Explanation:
The most common data quality issues are difficult to resolve in Excel because of their rigidity. It forces analysts to do a ton of manual work, which results in a high probability of an error being introduced to the data set. Those common issues include:
– Blanks.
– Nulls.
– Outliers.
– Duplicates.
– Extra spaces.
– Misspellings.
– Abbreviations and domain-specific variations.
– Formula error codes.
When introduced, these errors can skew or even invalidate the resulting analysis. A smart tool would minimize the possibility of error by automating the manual work. In Excel, you might look for data quality issues in one of two ways. First, you might use auto filters on specific columns to scan for anomalies and blanks or you might use a pivot table to find gaps and discrepancies. In either case, you’re scanning for the anomalies yourself. Suffice it to say that’s not a very efficient process. It also means accuracy is only as good as the analyst’s eye, so the probability of error varies throughout the day.
NEW QUESTION 21
Harry is looking at home sales prices in single zip code and notices that one home sold for $940,394 when the average selling price of similar homes is $210,420. What type of data does the $940,394 sales price represent?
A.   Invalid data.
B.   Redundant data.
C.   Data outlier.
D.   Duplicate data.
Answer: C
Explanation:
Since the value is more than four times the average, the $940,394 value is an outlier.
NEW QUESTION 22
A data scientist wants to see which products make the most money and which products attract the most customer purchasing interest in their company. Which of the following data manipulation techniques would he use to obtain this information?
A.   Data append.
B.   Data blending.
C.   Normalize data.
D.   Data merge.
Answer: B
Explanation:
Data blending is combining multiple data sources to create a single, new dataset, which can be presented visually in a dashboard or other visualization and can then be processed or analyzed. Enterprises get their data from a variety of sources, and users may want to temporarily bring together different datasets to compare data relationships or answer a specific question. Data append is incorrect. Data append is a process that involves adding new data elements to an existing database. An example of a common data append would be the enhancement of a company’s customer files. A data append takes the information they have, matches it against a larger database of business data, allowing the desired missing data fields to be added. Normalize data is incorrect. Data normalization is the process of structuring your relational customer database, following a series of normal forms. This improves the accuracy and integrity of your data while ensuring that your database is easier to navigate. Data merge is incorrect. Data merging is the process of combining two or more data sets into a single data set.
NEW QUESTION 23
A data analyst wants to create “Income Categories” that would be calculated based on the existing variable “Income”. The “Income Categories” would be as follows:
– Income category 1: less than $1.
– Income category 2: more than $1 and less than $20,000.
– Income category 3: more than $20,001 and less than $40,000.
– Income category 4: more than $40,001.
Which of the following data manipulation techniques should the data analyst use to create “Income Categories”?
A.   Data merge.
B.   Derived variables.
C.   Data blending.
D.   Data append.
Answer: B
Explanation:
Derived variables are variables that you create by calculating or categorizing variables that already exist in your data set. Data merge is incorrect. Data merging is the process of combining two or more data sets into a single data set. Data blending is incorrect. Data blending involves pulling data from different sources and creating a single, unique, dataset for visualization and analysis. Data append is incorrect. A data append is a process that involves adding new data elements to an existing database.
NEW QUESTION 24
Angela is aggregating data from CRM system with data from an employee system. While performing an initial quality check, she realizes that her employee ID is not associated with her identifier in the CRM system. What kind of issues is Angela facing?
A.   ETL process.
B.   Record linkage.
C.   ELT process.
D.   System integration.
Answer: B
Explanation:
While this scenario describes a system integration challenge that can be solved with ETL or ELT, Angela is facing a Record linkage issue.
NEW QUESTION 25
Andy is a pricing analyst for a retailer. Using a hypothesis test, he wants to assess whether people who receive electronic coupons spend more on average. What should Andy’s null hypothesis be?
A.   People who receive electronic coupons spend more on average.
B.   People who receive electronic coupons spend less on average.
C.   People who receive electronic coupons do not spend more on average.
D.   People who do not receive electronic coupons spend more on average.
Answer: C
Explanation:
The null hypothesis presumes the status quo. Andy is testing whether people who receive an electronic coupon spend more on average, so, the null hypothesis states that people who receive the coupon do spend more on average.
NEW QUESTION 26
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