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| Section | Weight | Objectives |
|---|---|---|
| Data Concepts and Environments | 15% | - Data sources and acquisition methods - Data structures and file formats - Data concepts and terminology |
| Data Mining | 25% | - Pattern identification and extraction - Data acquisition and cleaning techniques - Data transformation and profiling |
| Data Analysis | 23% | - Data interpretation and reporting - Statistical methods and calculations - Trend and correlation analysis |
| Visualization | 23% | - Tool-based visualization techniques - Chart and dashboard creation - Visualization best practices |
| Data Governance, Quality, and Controls | 14% | - Data quality management - Security, privacy, and compliance considerations - Data governance frameworks |
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NEW QUESTION # 202
Which of the following is a control measure for preventing a data breach?
Answer: C
Explanation:
Explanation
This is because data encryption is a type of control measure that prevents a data breach, which is an unauthorized or illegal access or use of data by an external or internal party. Data encryption can prevent a data breach by protecting and securing the data using a code or a key that scrambles or transforms the data into an unreadable or incomprehensible format, which can only be decoded or restored by authorized users who have the correct code or key. For example, data encryption can prevent a data breach by encrypting the data in transit or at rest, such as when the data is sent over a network or stored in a device. The other control measures are not used for preventing a data breach. Here is why:
Data transmission is a type of process that transfers and exchanges data between different sources or systems, such as databases, cloud services, or web applications. Data transmission does not prevent a data breach, but rather exposes the data to potential risks or threats during the transfer or exchange.
However, data transmission can be made more secure and less vulnerable to a data breach by using encryption or other methods, such as authentication or authorization.
Data attribution is a type of feature or function that assigns and tracks the ownership and origin of the data, such as the creator, modifier, or source of the data. Data attribution does not prevent a data breach but rather provides information and evidence about the data provenance and history. However, data attribution can be useful for detecting and responding to a data breach by using audit logs or metadata to identify and trace any unauthorized or illegal access or use of the data.
Data retention is a type of policy or standard that specifies and regulates the storage and preservation of the data, such as the duration, location, or format of the data. Data retention does not prevent a data breach, but rather affects the availability and accessibility of the data for future use or reference.
However, data retention can be optimized and aligned with the legal and ethical requirements and standards of the industry or the organization to reduce the risk or impact of a data breach.
NEW QUESTION # 203
An analyst needs to join two data sets that compare vehicle weights. One data set is in pounds, and the other has various units of measure. Which of the following should the analyst do first to the data prior to any type of join?
Answer: D
Explanation:
Before merging (joining) two datasets, it is crucial to ensure that theunits of measurement are consistentto maintain accuracy and comparability. This process is callednormalization.
* Option A (Blend):Incorrect. Blending is used to combine data from multiple sources but does not standardize unit measurements.
* Option B (Reduce):Incorrect. Reducing data refers to filtering or aggregating data, which does not address unit inconsistencies.
* Option C (Concatenate):Incorrect. Concatenation combines datasets without standardizing units, leading to inconsistent data.
* Option D (Normalize):Correct.Normalization ensures that all values in a dataset are converted to a common scale (e.g., converting kilograms to pounds) before performing operations like joins.
Reference:The CompTIA Data+ certification coversdata normalization as a key preprocessing step before joining datasets.
NEW QUESTION # 204
Given the following tables:
Which of the following will be the dimensions from a FULL JOIN of the tables above?
Answer: D
Explanation:
A FULL JOIN in SQL combines all rows from two or more tables, regardless of whether a match exists. The result includes all records when there is a match in the joined tables and fills in NULLs for missing matches on either side. Given the two tables in the image, the first table has three rows, and the second table has four rows. The FULL JOIN of these tables will include all rows from both tables, resulting in four rows. Since there are three unique columns in the first table (ID, Title) and three unique columns in the second table(ID, Name, Project_ID), with the common column being ID, the resulting table will have four columns (ID, Title, Name, Project_ID).
References:
SQL documentation on FULL JOIN operations.
NEW QUESTION # 205
A data set was recorded using multimedia technology. Which of the following is a necessary step on the way to interpretation?
Answer: A
Explanation:
The correct answer is B. Transcription.
Transcription is a necessary step on the way to interpretation when a data set was recorded using multimedia technology. Multimedia technology refers to the use of various forms of media, such as audio, video, images, and text, to capture and present information1 Transcription is the process of converting multimedia data into written or textual form, which can then be analyzed using various methods and tools2 Transcription can help to make the data more accessible, searchable, and manageable, as well as to preserve the data for future use.
Structural equation modeling is not correct, because it is a statistical technique that tests the causal relationships between multiple variables using observed and latent variables. Structural equation modeling is not a necessary step on the way to interpretation, but rather an optional method that can be applied to certain types of data.
Sequential analysis is not correct, because it is a method of analyzing the order and timing of events or behaviors in a data set. Sequential analysis is not a necessary step on the way to interpretation, but rather an optional method that can be applied to certain types of data.
Sampling is not correct, because it is the process of selecting a subset of data from a larger population for analysis. Sampling is not a necessary step on the way to interpretation, but rather a preliminary step that can be done before collecting or analyzing the data.
NEW QUESTION # 206
Given the following data:
Which of the following BEST describes the data set?
Answer: B
Explanation:
This is because inconsistency is a type of data quality issue that occurs when the data does not follow a common format, structure, or rule across different sources or systems, which can affect the efficiency and performance of the analysis or process. Inconsistency can be caused by having different spellings, punctuations, capitalizations, or abbreviations for the same or similar values in a data set, such as "M", "m", "Male", or "male" for gender in this case. Inconsistency can be eliminated or reduced by using data cleansing techniques, such as standardizing or normalizing the data values. The other options are not correct descriptions of the data set. Here is why:
Data bias is a type of data quality issue that occurs when the data is not representative or proportional of the population or the parameter, which can affect the validity and reliability of the analysis or process. Data bias can be caused by having a sample that is too small, too large, or too skewed for the population or the parameter, such as having only male customers for a product that targets both genders in this case. Data bias can be eliminated or reduced by using sampling techniques, such as stratified or cluster sampling.
The data is incomplete is a type of data quality issue that occurs when the data is absent or missing in a data set, which can affect the accuracy and reliability of the analysis or process. The data is incomplete can be caused by various factors, such as human error, system error, or non-response. The data is incomplete can be addressed by using various methods, such as replacing or imputing the missing values with some reasonable estimates, such as mean, median, mode, or regression.
The data is outliers is a type of data quality issue that occurs when the data has values that are unusually high or low compared to the rest of the data set, which can affect the quality and validity of the analysis or process. The data is outliers can be caused by various factors, such as measurement error, natural variation, or extreme events. The data is outliers can be addressed by using various methods, such as removing or filtering out the outliers, or using robust statistics that are less sensitive to outliers, such as median, interquartile range, or box plot.
NEW QUESTION # 207
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