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NEW QUESTION: 1
You are designing a big data storage solution. The solution must meet the following requirements:
* Provide unlimited account sizes.
* Support a hierarchical file system.
* Be optimized for parallel analytics workloads.
Which storage solution should you use?
A. Azure Data Lake Storage Gen2
B. Apache HBase in Azure HDInsight
C. Azure Blob storage
D. Azure Cosmos DB
Answer: A
Explanation:
Explanation
Azure Data Lake Storage is optimized performance for parallel analytics workloads A key mechanism that allows Azure Data Lake Storage Gen2 to provide file system performance at object storage scale and prices is the addition of a hierarchical namespace. This allows the collection of objects/files within an account to be organized into a hierarchy of directories and nested subdirectories in the same way that the file system on your computer is organized.
References:
https://docs.microsoft.com/en-us/azure/storage/blobs/data-lake-storage-namespace
NEW QUESTION: 2
A. Option D
B. Option C
C. Option A
D. Option B
E. Option E
Answer: C
NEW QUESTION: 3
FI can be deployed in
A. No Server required to deploy FI
B. None of the options are correct
C. Application Server
D. Web Server
Answer: C
NEW QUESTION: 4
George Armor, CFA, is a new stock analyst for Pedad Investments. One tool that Pedad uses to compare stock valuations is the dividend discount model (DOM). In particular, the firm evaluates stocks in terms of
"justified" multiples of sales and book value. These multiples are based on algebraic manipulation of the DDM. Over time, these multiples seem to provide a good check on the market valuation of a stock relative to the company's fundamentals. Any stock which is currently priced below its value based on a justified multiple of sales or book value is considered attractive for purchase by Pedad portfolio managers. Exhibit 1 contains financial information from the year just ended for three stable companies in the meat-packing industry: Able Corp, Baker, Inc., and Charles Company, from which Armor will derive his valuation estimates.
One of Pedad's other equity analysts, Marie Swift, CFA, recently held a meeting with Armor to discuss a relatively new model the firm is implementing to determine the P/E ratios of companies that Pedad researches. Swift explains that the model utilizes a cross-sectional regression using the previous year-end data of a group of comparable companies' P/E ratios against their dividend payout ratios (r), sustainable growth rates (g), and returns on equity (ROE). The resulting regression equation is used to determine a predicted P/E ratio for the subject company using the subject company's most recent year-end data. Swift has developed the following model, which has an R-squared of 81%, for the meat packing industry (16 companies):
Predicted P/E = 2.74 + 8.21(r) + 14.21(g) + 2.81(ROE)
(STD error) (2.11) (6.52) (9.24) (2.10)
After Swift presents the model to Armor, she points out that models of this nature are subject to limitations.
In particular, multicollinearity, which appears to be present in the meat packing industry model, can create great difficulty in interpreting the effects of the individual coefficients of the model. Swift continues by stating that in spite of this limitation, models of this nature generally have known and significant predictive power across different time periods although not across different stocks.
Evaluate Swift's comments regarding multicollinearity and predictive power. Which of the following comments is correct?
A. Both comments are correct.
B. Only the comment about multicollinearity is correct.
C. Only the comment about predictive power is correct.
Answer: B
Explanation:
Explanation/Reference:
Explanation:
Swift has correctly stated that if multicollinearity is present in a model, the interpretation of the individual regression coefficients becomes problematic. The existence of multicollinearity is generally signaled by a high R-squared value and low f-statistics on the regression coefficients. The r-stat for the coefficients for r, g, and ROE can be calculated as (8.21 / 6.52) = 1.26, (14.21 / 9.24) = 1.54, and (2.81 / 2.10) = 1.34, respectively. Note that all of these f-stats are well below the approximate critical value of 2, indicating they are statistically insignificant. With the high R-squared of 81% and insignificant f-stats, it appears that multicollinearity is indeed present in this model. Swift's comment regarding predictive power is incorrect.
Cross-sectional regressions have unknown predictive power outside the specific sample and time period used to generate the regression. (Study Session 12, LOS 42.k)