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| Section | Weight | Objectives |
|---|---|---|
| Data Mining | 25% | - Explain common techniques for data manipulation and optimization - Given a scenario, execute techniques for data manipulation - Explain data acquisition concepts - Identify common reasons for cleansing and profiling datasets |
| Data Analysis | 23% | - Given a scenario, apply the appropriate descriptive statistical methods - Summarize types of analysis and key analysis techniques - Explain the purpose of inferential statistical methods |
| Visualization | 23% | - Given a scenario, translate business requirements to support data-driven decisions - Compare and contrast types of reports - Given a scenario, use appropriate methods for dashboard development - Explain important aspects of a report that tell a data story - Given a scenario, apply the appropriate type of visualization |
| Data Concepts and Environments | 15% | - Compare and contrast different data types - Identify basic concepts of data schemas and dimensions - Compare and contrast common data structures and file formats |
| Data Governance, Quality, and Controls | 14% | - Summarize important data governance concepts - Explain key aspects of data quality - Compare and contrast types of data controls |
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問題 #150
Which of the following is the best description of the term "data governance"?
答案：C
解題說明：
Data governance refers to the overarching management of data's availability, usability, integrity, and security within an organization. It involves setting policies and standards that govern data usage, determining data ownership, implementing data security measures, and ensuring that data is accessible for business insights while maintaining its quality. The goal of data governance is to ensure that data is consistent, trustworthy, and not misused, supporting compliance with data privacy regulations and enabling effective data analytics to optimize operations and drive business decision-making.
References:
* Understanding Data Governance and Its Importance1.
* The Role of Data Governance in Data Management2.
* Defining Data Governance and Its Business Value3.
問題 #151
Given the following data:
CustomerID
ItemBought
Date
Tre_234
Sofa
2022-09-08
216_Tre
Shoes
08/02/2021
215/Tre
Blanket
2021/06/20
045/Tre
Mug
12-26-2021
Tre-345
Lamp
31/08/2022
TREJD19
Bucket
2022'08/01
Which of the following best describes the main issue in the data set?
答案：B
解題說明：
Comprehensive and Detailed In-Depth
The dataset exhibits inconsistencies in both the CustomerID and Date columns:
CustomerID Column: The identifiers use various formats, including underscores (Tre_234), slashes (215/Tre), hyphens (Tre-345), and combinations of letters and numbers (TREJD19). This inconsistency can lead to difficulties in data retrieval and analysis.
Date Column: Dates are presented in multiple formats, such as YYYY-MM-DD (2022-09-08), MM/DD/YYYY (08/02/2021), YYYY/MM/DD (2021/06/20), MM-DD-YYYY (12-26-2021), DD/MM/YYYY (31/08/2022), and an unconventional format (2022'08/01). Such variability can cause errors in date computations and analyses.
Addressing these inconsistencies is essential to ensure data quality and reliability.
問題 #152
Which of the following best describe qualitative data? (Select two).
答案：A,B
解題說明：
Comprehensive and Detailed In-Depth
Qualitative data refers to non-numeric information that describes qualities or characteristics. It is often categorized based on attributes or properties and is typically descriptive in nature.
Ordinal Data: This type of qualitative data represents categories with a meaningful order but without a consistent interval between them. Examples include rankings (e.g., customer satisfaction levels such as 'satisfied,' 'neutral,' 'dissatisfied') where the order matters, but the difference between ranks is not uniform.
Nominal Data: This qualitative data type consists of categories without any inherent order. Examples include gender, race, or types of products. Each category is distinct, and there is no logical sequence.
In contrast, options A (Discrete) and D (Continuous) refer to quantitative data types:
Discrete Data: Quantitative data that can take on only specific, distinct values (e.g., the number of students in a class).
Continuous Data: Quantitative data that can take on any value within a range (e.g., height, weight).
Options C (Batch) and F (Real-time) pertain to data processing methods rather than data types.
Reference:
CompTIA Partners
問題 #153
Which of the following best describes a 95% confidence interval?
答案：C
解題說明：
Comprehensive and Detailed In-Depth Explanation:
A95% confidence intervalis a statistical range, derived from sample data, that is likely to contain the true population mean. It reflects the degree of uncertainty or certainty in a sampling method.
Option C:A set of ranges contains the population mean with 95% certainty.
* Rationale:This statement accurately describes a 95% confidence interval. It means that if we were to take 100 different samples and compute a confidence interval for each sample, we would expect about
95 of the intervals to contain the true population mean.
Option A:There is a 95% probability that a sample is within one standard deviation of the mean.
* Rationale:This statement confuses confidence intervals with standard deviation. A 95% confidence interval pertains to the range within which the population mean is expected to lie, not the dispersion of individual sample points around the mean.
Option B:A stated range may contain 95% of the population mean, 95% of the time.
* Rationale:This statement is incorrect because the population mean is a single value, not something that can be 95%
問題 #154
A database administrator needs to increase performance on a large dimension table. Which of the following is the best way to accomplish this task?
答案：A
解題說明：
Comprehensive and Detailed In-Depth
Improving the performance of large dimension tables in a database is crucial for efficient data retrieval and processing.
Partitioning: This technique involves dividing a large table into smaller, more manageable pieces called partitions. Each partition can be accessed and maintained separately, which enhances query performance and simplifies data management. Partitioning allows the database to read only the relevant partitions during a query, reducing the amount of data processed and speeding up response times.
Sampling: This involves analyzing a subset of data rather than the entire dataset. While useful for statistical analysis, it doesn't improve the performance of queries on the full dataset.
Windowing: This refers to functions that perform calculations across a set of table rows related to the current row, such as moving averages. It's useful for analytical purposes but doesn't inherently improve table performance.
Sorting: Organizing data in a specific order can aid in readability and certain query operations but doesn't significantly impact performance on large tables unless combined with indexing.
Therefore, partitioning is the most effective method to enhance performance on large dimension tables.
Reference:
CompTIA Partners
問題 #155
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DA0-001證照資訊: https://www.vcesoft.com/DA0-001-pdf.html
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