DA0-001 Exam Dumps - CompTIA Data+ Certification Exam

The error in merging data from Table 2 into Table 1 using last names could be due to discrepancies in spelling or data type between the two tables. It is essential to ensure that the last names are spelled consistently and that the data types are compatible for a successful merge. Option D suggests reviewing these aspects, which can potentially resolve the error, ensuring that each last name in Table 1 accurately corresponds to the same last name in Table 2, allowing for a successful data pull of addresses.

Logical functions are used in spreadsheets to create conditions that return YES/NO or TRUE/FALSE results. These functions include IF, AND, OR, NOT, etc., which are specifically designed for decision-making processes in data analysis. For example, the IF function in Excel evaluates a condition and returns one value if TRUE and another if FALSE, perfectly implementing YES/NO logic.

CompTIA Data+ Reference:

CompTIA Data+ Study Guide (Exam DA0-001), Chapter 4: Data Manipulation and Analysis, Section “Data Manipulation Functions – Logical”, Official CompTIA CertMaster Learn for Data+, Module 4.3 “Logical Functions in Spreadsheets”.

 Data cleansing is a critical process in data analytics to ensure the accuracy and quality of data. The reasons to undertake data cleansing include:

Missing Data (B): Missing data can lead to incomplete analysis and biased results. It is essential to identify and address gaps in the dataset to maintain the integrity of the analysis1.

Invalid Data (D): Invalid data includes entries that are out of range, improperly formatted, or illogical (e.g., a negative age). Such data can corrupt analysis and lead to incorrect conclusions1.

Other options, such as non-parametric data (A), are not inherently errors but refer to a type of data that doesn’t assume a normal distribution. Duplicate data © and redundant data (E) could also be reasons for data cleansing, but they are not listed as options to select from in the provided image details. Normalized data (F) refers to data that has been processed to fit into a certain range or format and is typically not a reason for data cleansing.

A quarter-over-quarter closed won report requires:

Identification of which quarter each deal belongs to

The closed dollar amount for those deals

The ability to compare quarter vs quarter (e.g., Q1 vs Q2, etc.)

Therefore, the essential fields are:

Closed won quarter – defines the time bucket (Q1, Q2, Q3, Q4).

Closed dollar amount – the value to aggregate and compare.

Why the other options are insufficient:

A. Report create date + closed amount – the report creation date doesn’t provide the quarter in which the deal was closed.

C. Segment + closed amount – gives segmentation, but not the quarter dimension needed for quarter-over-quarter comparisons.

D. Closed won year + sales leader name – year is too coarse (you need quarter), and sales leader is not required for quarter-over-quarter totals.

Thus, to build a closed won quarter-over-quarter report, the correct combination is Closed won quarter and closed dollar amount (B).

CompTIA Data+ Reference (concept alignment):

DA0-001 Objectives – Reporting and analysis: using time dimensions (quarters, months) and measures (revenue) to build period-over-period reports.

Study discussions of time-based aggregations (quarter-over-quarter, year-over-year).

The date parameters that the analyst should use are Q3 2019 vs. Q3 2020, as this will allow the analyst to compare the sales performance of the Q3 2020 promotion with the same period of last year. This will help to eliminate any seasonal or cyclical effects that might affect the sales data. The other options are not relevant for this purpose, as they either compare different quarters or different years. Reference: CertMaster Practice for Data+ Exam Prep - CompTIA

 A password-protected dashboard is a type of web-based application that can display the health scorecards of the troops in a secure and interactive way. A password-protected dashboard can provide the following benefits for the commander:

It can protect the PHI data from unauthorized access or disclosure by requiring a valid username and password to log in. This can ensure that only the commander and other authorized personnel can view the information12

It can allow the commander to filter the data based on gender and rank by using drop-down menus, sliders, checkboxes, or other controls. This can enable the commander to customize the view and focus on the relevant data13

It can update the data daily by connecting to a data source that refreshes automatically or on demand. This can ensure that the commander always sees the latest and most accurate information14

It can present the data in a visual and intuitive way by using charts, graphs, tables, or other elements. This can help the commander to understand and analyze the data more easily and effectively1