Pass the CompTIA Data+ DA0-001 Questions and answers with Dumpstech

In hypothesis testing, the p-value represents the probability of observing a test statistic at least as extreme as the one observed, assuming the null hypothesis is true.

Key idea:

A low p-value (typically below a chosen significance level such as 0.05):

Indicates that the observed result is unlikely under the null hypothesis.

Suggests there is evidence against the null hypothesis.

Is therefore interpreted as potentially meaningful / statistically significant.

By contrast:

The test statistics themselves (e.g., t-statistic, F-statistic, chi-square) are usually interpreted such that larger absolute values indicate stronger evidence against the null (and lead to smaller p-values).

The question explicitly asks which test statistic shows that a low value implies meaningful result — that is p-value, which is itself a probability metric used to evaluate significance.

Therefore, the correct choice is p-value (B).

CompTIA Data+ Reference (concept alignment):

DA0-001 Exam Objectives – Statistical methods: hypothesis testing, p-values, significance.

Study materials: explanation that small p-values indicate statistically significant results.

Getting stakeholder approval is the next step the analyst should take in the dashboard creation process, after confirming the data sources and creating a wireframe. Stakeholder approval means getting feedback and validation from the intended users or clients of the dashboard, to ensure that it meets their expectations and requirements. This step helps to avoid rework and ensure customer satisfaction. References: CompTIA Data+ Certification Exam Objectives, page 14

An INNER JOIN returns only the rows that match the join condition in both tables. A LEFT JOIN returns all the rows from the left table, and the matched rows from the right table, or NULL if there is no match. In this case, the customer table is the left table and the in-store transactions table is the right table. The join condition is based on the customer_id column, which is common in both tables.

To perform an INNER JOIN, we can use the following SQL query:

SELECT * FROM customer INNER JOIN in_store_transactions ON customer.customer_id = in_store_transactions.customer_id;

This query will return 9 rows of data, as shown below:

customer_id | name | lastname | gender | marital_status | transaction_id | amount | date 1 | MARC | TESCO | M | Y | 1 | 1000 | 2020-01-01 1 | MARC | TESCO | M | Y | 2 | 5000 | 2020-01-02 2 | ANNA | MARTIN | F | N | 3 | 2000 | 2020-01-03 2 | ANNA | MARTIN | F | N | 4 | 3000 | 2020-01-04 3 | EMMA | JOHNSON | F | Y | 5 | 4000 | 2020-01-05 4 | DARIO | PENTAL | M | N | 6 | 5000 | 2020-01-06 5 | ELENA | SIMSON| F| N|7|6000|2020-01-07 6|TIM|ROBITH|M|N|8|7000|2020-01-08 7|MILA|MORRIS|F|N|9|8000|2020-01-09

To perform a LEFT JOIN, we can use the following SQL query:

SELECT * FROM customer LEFT JOIN in_store_transactions ON customer.customer_id = in_store_transactions.customer_id;

This query will return 15 rows of data, as shown below:

customer_id|name|lastname|gender|marital_status|transaction_id|amount|date 1|MARC|TESCO|M|Y|1|1000|2020-01-01 1|MARC|TESCO|M|Y|2|5000|2020-01-02 2|ANNA|MARTIN|F|N|3|2000|2020-01-03 2|ANNA|MARTIN|F|N|4|3000|2020-01-04 3|EMMA|JOHNSON|F|Y|5|4000|2020-01-05 4|DARIO|PENTAL|M|N|6|5000|2020-01-06 5|ELENA|SIMSON||F||N||7||6000||2020-01-07 6||TIM||ROBITH||M||N||8||7000||2020-01-08 7||MILA||MORRIS||F||N||9||8000||2020-01-09 8||JENNY||DWARTH||F||Y||NULL||NULL||NULL

As you can see, the customers who do not have any transactions (customer_id = 8) are still included in the result, but with NULL values for the transaction_id, amount, and date columns.

Therefore, the correct answer is C: INNER: 9 rows; LEFT: 15 rows.

The most appropriate frequency for the sales report is weekly, as this will provide the CEO with more up-to-date sales data for improved visibility prior to month-end. A weekly sales report can show the sales performance, trends, and issues of the sales team on a regular basis, and help the CEO to monitor and evaluate the progress and results of the sales activities. A weekly sales report can also help the CEO to identify and address any problems or opportunities that may arise during the month, and to make timely and informed decisions.

This is because percent change is a type of descriptive statistic that measures the relative change or difference of a variable over time, such as the sugar content of cereal over years in this case. Percent change can be used to determine whether the sugar content of cereal has increased over years by comparing the initial and final values of the sugar content, as well as calculating the ratio or proportion of the change. For example, percent change can be used to determine whether the sugar content of cereal has increased over years by finding out how much more (or less) sugar there is in cereal now than before, as well as expressing it as a fraction or a percentage of the original sugar content. The other descriptive statistics are not appropriate to use to determine whether the sugar content of cereal has increased over years. Here is why:

Frequency is a type of descriptive statistic that measures how often or how likely a value or an event occurs in a data set, such as how many times a certain sugar content appears in cereal in this case. Frequency does not measure the relative change or difference of a variable over time, but rather measures the occurrence or chance of a variable at a given time.

Variance is a type of descriptive statistic that measures how much the values in a data set vary or deviate from the mean or average of the data set, such as how much variation there is in sugar content among different cereals in this case. Variance does not measure the relative change or difference of a variable over time, but rather measures the dispersion or spread of a variable at a given time.

Mean is a type of descriptive statistic that measures the average value or central tendency of a data set, such as what is the typical sugar content of cereal in this case. Mean does not measure the relative change or difference of a variable over time, but rather measures the summary or representation of a variable at a given time.

When dealing with large datasets, optimizing data storage is crucial for performance and efficiency. The Date column, containing date-time values, can be optimized by reducing its precision, thereby decreasing the storage requirements.

Changing the data type to text: Converting the Date column to text would likely increase the storage size and complicate date-time operations, as text representations are less efficient for date-time computations.

Trimming the date: This option is ambiguous. If it refers to removing time components, it could lead to loss of essential information.

Rounding the hour to the start of the hour: By adjusting the time to the top of the hour (e.g., 10:45 becomes 10:00), the precision is reduced, which can decrease the storage size and improve performance without significant loss of essential information.

Splitting into two columns—time and date: This approach increases the number of columns and may not effectively reduce the overall data size.

Therefore, rounding the hour to the start of the hour is the most effective method to reduce the data model's size while preserving essential temporal information.

Discrete data is a type of data that can only take certain values, usually whole numbers or integers. Discrete data can be counted, but not measured. For example, the number of students in a class, the number of books in a library, or the number of phone calls that a call center receives in a day are all examples of discrete data. Discrete data is different from continuous data, which can take any value within a range, and can be measured with precision. For example, the height of a person, the weight of a fruit, or the temperature of a room are all examples of continuous data. Therefore, the correct answer is D. References: [Discrete vs Continuous Data: Definition and Examples - Statistics How To], [Discrete Data - Definition and Examples | Math Goodies]

This is because a data dictionary is a type of document that defines and describes the data elements, attributes, and relationships in a database or a data set. A data dictionary can be used to facilitate the MDM (Master Data Management) process, which is a process that aims to ensure the quality, consistency, and accuracy of the data across different sources and systems. By creating a data dictionary first, the analyst can establish a common understanding and standardization of the data field names, types, formats, and meanings, as well as identify any potential issues or conflicts in the data, such as missing values, duplicate values, or inconsistent values. The other MDM processes can take place after creating a data dictionary. Here is why:

Compliance with regulations is a type of MDM process that ensures that the data meets the legal and ethical requirements and standards of the industry or the organization. Compliance with regulations can take place after creating a data dictionary, because the data dictionary can help theanalyst to identify and apply the relevant rules and policies to the data, such as data privacy, security, or retention.

Standardization of data field names is a type of MDM process that ensures that the data field names are consistent and uniform across different sources and systems. Standardization of data field names can take place after creating a data dictionary, because the data dictionary can provide a reference and a guideline for naming and labeling the data fields, as well as resolving any discrepancies or ambiguities in the data field names.

Consolidation of multiple data fields is a type of MDM process that combines or merges the data fields from different sources or systems into a single source or system. Consolidation of multiple data fields can take place after creating a data dictionary because the data dictionary can help the analyst to map and match the data fields from different sources or systems based on their definitions and descriptions, as well as eliminating any redundant or duplicate data fields.

Here, the problem is about granularity and detail level of the data:

The organization only captures sales by region, not by store.

This means certain attributes (e.g., store identifier) are not present or not captured at the required level of detail.

In CompTIA Data+ language, this is a case of data attribute limitations:

The data does not include all the necessary attributes (or the required level of those attributes) to perform finer-grained analysis (e.g., store-level performance, store comparisons).

Why the other options are incorrect:

Data accuracy (B): Deals with whether data values are correct/true; here, the issue is not that regional sales are wrong, but that they lack detail.

Data integrity (C): Refers to internal correctness and reliability (e.g., foreign keys, referential integrity, completeness of relationships).

Data consistency (D): Concerns whether data is uniform across systems or over time (e.g., same format, same definitions).

The main issue is not enough attributes / insufficient granularity, which matches Data attribute limitations (A).

CompTIA Data+ Reference (concept alignment):

DA0-001 Exam Objectives – Data quality: completeness, granularity, and attribute sufficiency.

CompTIA Data+ Study Guide – discussions on data attribute limitations and granularity impacting the types of analysis that can be performed.