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Question 56

When reviewing the results of their analysis, the team is determining if the data supports their hypothesis and can be presented to decision makers. They are reviewing measures of variation, sample size and statistical significance. They realize that the p-value of 0.02 is lower than the initial target.This clearly indicates the team can:

Correct Answer: C
According to the Guide to Business Data Analytics, a p-value is the probability of obtaining a test statistic at least as extreme as the one observed, assuming that the null hypothesis is true. A p-value is used to make conclusions in hypothesis testing by comparing it to a significance level, which is the maximum probability of making a type I error (rejecting the null hypothesis when it is true). If the p-value is less than or equal to the significance level, then there is strong evidence against the null hypothesis and it is rejected in favor of the alternative hypothesis. If the p-value is greater than the significance level, then there is weak evidence against the null hypothesis and it is not rejected. In this situation, the team realizes that the p-value of 0.02 is lower than the initial target, which means that the probability of observing such a result under the null hypothesis is very low. This clearly indicates that the team can reject the null hypothesis in favor of the alternative hypothesis, as there is sufficient evidence to support their hypothesis.
References: Guide to Business Data Analytics, page 57-58; CBDA Exam Blueprint, page 7; Understanding P-values | Definition and Examples - Scribbr
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Question 57

An online retailer of men's athletic apparel is seeking to become the market leader in the industry. To deliver on this strategy, the analytics team continuously collects data on the prices of competitor products and uses this information to adjust the retailer's prices. What type of analytics is the retailer using to maintain their pricing structure?

Correct Answer: D
Explanation
Prescriptive analytics is the type of analytics that the retailer is using to maintain their pricing structure, because it is a technique that uses data and models to recommend the best course of action for a given situation. Prescriptive analytics can help the retailer optimize their prices based on the data collected from the competitors, the market conditions, and the customer preferences, and thus achieve their strategic goal of becoming the market leader. References:
*Business Analysis Certification in Data Analytics, CBDA | IIBA®, CBDA Competencies, Domain 3:
Analyze Data
*Understanding the Guide to Business Data Analytics, page 17
*CERTIFICATION IN BUSINESS DATA ANALYTICS HANDBOOK - IIBA®, page 8, CBDA Exam Sample Questions and Self-Assessment, Question 11
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Question 58

The CustomerOrder entity will include information on all customer orders. Applying database normalization rules, which set of attributes will need to be normalized to avoid redundancies?
*Customerld
*CustomerPhone
*Orderld
*OrderDate
*ProductName
*ProductQuantity
*OrderTotal

Correct Answer: B
Database normalization is the process of organizing the data in a database to reduce redundancy and improve integrity, consistency, and performance1. Database normalization rules are based on the concept of normal forms, which are levels of database design that meet certain criteria2. One of the most common normal forms is the third normal form (3NF), which states that a table should not have any transitive dependencies, meaning that a non-key attribute should not depend on another non-key attribute3. In the CustomerOrder entity, the set of attributes that will need to be normalized to avoid redundancies are ProductName and ProductQuantity, as they are non-key attributes that depend on another non-key attribute, Orderld. This means that the same product information may be repeated for different orders, which could lead to data inconsistency, duplication, or update anomalies. To normalize this set of attributes, a separate table should be created for the OrderDetails entity, which would have Orderld, ProductName, and ProductQuantity as its attributes, and Orderld and ProductName as its composite primary key.
The other sets of attributes do not need to be normalized to avoid redundancies, as they do not violate the 3NF.
CustomerPhone and ProductName are non-key attributes that depend on the primary key, Customerld and Orderld respectively, which is allowed by the 3NF. Orderld and ProductName are part of the composite primary key of the OrderDetails entity, which is also allowed by the 3NF. Customerld and OrderDate are both primary keys of the Customer and Order entities respectively, which are also allowed by the 3NF.
References:1: Guide to Business Data Analytics, IIBA, 2020, p. 442: Introduction to Business Data Analytics:
A Practitioner View, IIBA, 2019, p. 93: Database Normalization: The Definitive Guide, Tableau, . : Database Normalization: The Definitive Guide, Tableau, . : Guide to Business Data Analytics, IIBA, 2020, p. 44. :
Introduction to Business Data Analytics: A Practitioner View, IIBA, 2019, p. 9. : Database Normalization: The Definitive Guide, Tableau, . : Database Normalization: The Definitive Guide, Tableau, .
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Question 59

A database analyst is modelling a database for a large toy manufacturer. Which statement describes a logical database model?

Correct Answer: C
Explanation
A logical database model is a data model of a specific problem domain expressed independently of a particular database management product or storage technology. It describes data using notation that corresponds to a data organization used by a database management system, such as relational tables and columns. It also includes rules of normalization, which are the process of converting complex data structures into simple, stable data structures12 References: 1: Logical schema - Wikipedia 2: What Is a Data Model? | Coursera
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Question 60

An analyst is looking at a particular dataset that includes the scores across all 8th grade students, across three schools. The analyst is trying to determine which type of statistics average to use to best represent the results.
On looking through the dataset, the analyst has identified a few extreme outliers. As a result, the analyst was led to use the following type of average:

Correct Answer: A
The median is the type of statistics average that the analyst should use to best represent the results, because it is a measure of central tendency that divides the data set into two equal halves. The median is the middle value of the data set when it is arranged in ascending or descending order. The median is not affected by extreme outliers, unlike the mean, which is the arithmetic average of the data set. The median can give a more accurate representation of the typical score of the 8th grade students across the three schools. Options B, C, and D are not types of statistics average, but types of statistics measures that describe other aspects of the data set. The range is a measure of dispersion that shows the difference between the highest and the lowest values of the data set. The mean is a measure of central tendency that shows the sum of the values of the data set divided by the number of values. The mode is a measure of central tendency that shows the most frequent value of the data set. References:
*Business Analysis Certification in Data Analytics, CBDA | IIBA®, CBDA Competencies, Domain 3:
Analyze Data
*Understanding the Guide to Business Data Analytics, page 17
*Business Data Analytics (IIBA®-CBDA Exam preparation) | Udemy, Section 3: Analyze Data, Lecture 13:
Descriptive Statistics
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