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NEW QUESTION # 31
Which of the following allows a data analyst to send out a spreadsheet containing sensitive information without revealing personal details?
Answer: C
Explanation:
This question pertains to theData Governancedomain, focusing on data privacy and security. The task is to share a spreadsheet with sensitive information while protecting personal details.
* Using a UUID in the data file (Option A): A UUID (Universally Unique Identifier) can anonymize records, but if other PII (e.g., names) remains, personal details are still exposed.
* Redacting all PII (Option B): Redacting personally identifiable information (PII) removes sensitive details (e.g., names, addresses), ensuring personal information isn't revealed while sharing the spreadsheet.
* Adding access controls to the ID column (Option C): Access controls limit who can view the data, but the question focuses on the spreadsheet content itself, not access.
* Encrypting the spreadsheet (Option D): Encryption protects the file during transmission, but once opened, personal details are still visible unless redacted.
The DA0-002 Data Governance domain includes "data privacy concepts," and redacting PII is the most direct method to protect personal details in a shared spreadsheet.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 5.0 Data Governance.
NEW QUESTION # 32
Which of the following pieces of information, if made public, results in a data privacy violation?
Answer: C
Explanation:
This question falls under theData Governancedomain, which in DA0-002 includes understanding data privacy and compliance with regulations like GDPR. The question asks which piece of information, if made public, constitutes a privacy violation, meaning it must be personally identifiable information (PII).
* Gender (Option A): Gender is not typically considered PII on its own, as it's not uniquely identifiable.
* Driver's license (Option B): A driver's license number is PII because it uniquely identifies an individual and can be linked to other personal information, such as name and address. Making it public violates privacy regulations.
* Age (Option C): Age alone isn't PII, as it's not uniquely identifiable.
* Employment status (Option D): Employment status (e.g., employed, unemployed) isn't PII, as it doesn't uniquely identify an individual.
The DA0-002 Data Governance domain includes "identifying PII and data privacy concepts," and a driver's license is a clear example of PII that, if exposed, results in a privacy violation.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 5.0 Data Governance.
NEW QUESTION # 33
A data analyst created a dashboard to illustrate the traffic volume and mean response time for a call center.
The traffic data is current, but the mean response time has not updated for more than an hour. Which of the following is the best way to verify the data's freshness?
Answer: D
Explanation:
This question pertains to theData Governancedomain, which in DA0-002 includes ensuring data quality and freshness, especially in dashboards. The issue is that the mean response time isn't updating, while traffic data is current, indicating a potential issue with the data refresh process forthe response time metric.
* Refactoring the code base (Option A): Refactoring might improve long-term performance but doesn't directly address verifying data freshness.
* Testing for network connectivity issues (Option B): Network issues could cause delays, but since traffic data is updating, connectivity is likely not the issue.
* Checking the last time the calculation script ran (Option C): Mean response time is a calculated metric, likely derived from a script. Checking when the script last ran directly verifies if the data refresh process failed, making this the best approach.
* Determining the number of calls with no timestamps (Option D): Missing timestamps might indicate data quality issues, but it doesn't directly verify why the mean response time isn't updating.
The DA0-002 Data Governance domain focuses on "data quality control concepts," including ensuring data freshness in reporting. Checking the script's last run time aligns with this objective.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 5.0 Data Governance.
NEW QUESTION # 34
A data analyst receives the following sales data for a convenience store:
Item
Quantity
Price
Chocolate Bars
7
$1.99
Vanilla Ice Bars
2
$4.99
Chocolate Wafers
6
$0.99
Peanut Butter
2
$2.99
Cups
3
$4.99
Strawberry Jam
3
$4.99
Chocolate Cake
9
$6.99
Milk Chocolate
2
$2.99
Almonds
5
$2.99
The analyst needs to provide information on the products that contain chocolate. Which of the following RegEx should the analyst use to filter the chocolate products?
Answer: A
Explanation:
This question falls under theData Acquisition and Preparationdomain, which includes techniques for manipulating and filtering data, such as using regular expressions (RegEx) to identify specific patterns in text data. The task is to filter items containing the word "Chocolate."
* Chocolate! (Option A): In RegEx, "!" is not a valid pattern for matching a word like "Chocolate." It typically denotes negation in some contexts, but here it's incorrect.
* Chocolate$ (Option B): The "$" in RegEx anchors the pattern to the end of the string, meaning it matches "Chocolate" at the end of an item name (e.g., "Milk Chocolate"). This is the most appropriate pattern for identifying items ending with "Chocolate," which applies to the relevant items in the list.
* %Chocolate& (Option C): "%" and "&" are not standard RegEx anchors; they're often used in SQL LIKE patterns, not RegEx, making this incorrect.
* #Chocolate#$ (Option D): "#" is not a standard RegEx anchor, and this pattern would look for
"Chocolate" surrounded by "#", which doesn't match the data.
The DA0-002 Data Acquisition and Preparation domain includes "executing data manipulation" , and RegEx is a common technique for filtering text data. The pattern "Chocolate$" correctly identifies items like
"Chocolate Bars," "Chocolate Wafers," "Chocolate Cake," and "Milk Chocolate." Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 2.0 Data Acquisition and Preparation
NEW QUESTION # 35
A data analyst is creating a forecast for a product line introduced early last year. Which of the following should the analyst use to create projected sales and customer satisfaction for next year?
Answer: B
Explanation:
This question pertains to theData Analysisdomain, focusing on data types and methods for forecasting. The task involves projecting sales (numerical) and customer satisfaction (likely ordinal,e.g., ratings), requiring appropriate data attributes.
* Standard deviation and constraints (Option A): Standard deviation measures data spread, and constraints are conditions, neither of which directly supports forecasting.
* Mean and median (Option B): Mean and median are descriptive statistics, not sufficient for forecasting future values.
* Boolean data and an array (Option C): Boolean data (true/false) and arrays (data structures) are not relevant for forecasting sales and satisfaction.
* Numerical and ordinal attributes (Option D): Sales are numerical (e.g., units sold), and customer satisfaction is often ordinal (e.g., 1-5 ratings). These attributes are suitable for forecasting models (e.g., time-series analysis for sales, regression for satisfaction).
The DA0-002 Data Analysis domain includes "applying the appropriate descriptive statistical methods," and numerical and ordinal attributes are key for forecasting sales and satisfaction.
Reference: CompTIA Data+ DA0-002 Draft Exam Objectives, Domain 3.0 Data Analysis.
NEW QUESTION # 36
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