DA0-001 Online Practice Questions and Answers

Correct Answer: AB

Explanation: The reasons to create and maintain a data dictionary are to improve data acquisition and to remember specifics about data fields. A data dictionary is a document or a database that describes the structure, meaning, and usage of the data elements in a data source or a database. A data dictionary can help to improve data acquisition by providing clear and consistent definitions, rules, and standards for the data collection process. A data dictionary can also help to remember specifics about data fields by providing information such as data type, format, length, range, default value, constraints, relationships, etc. The other options are not reasons to create and maintain a data dictionary, as they are related to other aspects of data management or security. A data dictionary does not specify user groups for databases, as this is a function of access control or authorization. A data dictionary does not provide continuity through personnel turnover, as this is a function of documentation or knowledge transfer. A data dictionary does not confine breaches of PHI data, as this is a function of encryption or anonymization. A data dictionary does not reduce processing power requirements, as this is a function of optimization or compression. Reference: [What is a Data Dictionary? - DataCamp]

Correct Answer: D

Data merging is the process of combining two or more data sets into a single data set. Most often, this process is necessary when you have raw data stored in multiple files, worksheets, or data tables, that you want to analyze all in one go.

Correct Answer: C

Explanation: A measure of the amount of dispersion of a set of values. This is because standard deviation is a type of statistical measure that quantifies how much the values in a data set vary or deviate from the mean or the average of the

data set. Standard deviation can be used to describe the spread or the distribution of the data, as well as to identify any outliers or extreme values in the data. For example, a low standard deviation indicates that the values are close to the

mean, while a high standard deviation indicates that the values are far from the mean. The other options are not correct descriptions of standard deviation.

Here is why:

A measure that is used to establish a relationship between two variables is not a correct description of standard deviation, but rather a description of correlation or regression, which are types of statistical measures that quantify how two

variables are related or associated with each other. Correlation or regression can be used to test or model the dependence or the influence of one variable on another variable, as well as to predict or estimate the value of one variable based

on the value of another variable.

A measure of how data is distributed is not a correct description of standard deviation, but rather a description of frequency or probability, which are types of statistical measures that quantify how often or how likely a value or an event occurs

in a data set. Frequency or probability can be used to describe the occurrence or the chance of the data, as well as to compare or contrast different categories or groups of the data.

A measure that is used to find the significant difference between variables is not a correct description of standard deviation, but rather a description of hypothesis testing or inferential statistics, which are types of statistical methods that use

sample data to make generalizations or conclusions about a population or a parameter. Hypothesis testing or inferential statistics can be used to test or verify a claim or an assumption about the data, as well as to measure the confidence or

the error of the estimation.

Correct Answer: C

Answer: C. 2019 goal data To report how the sales team is performing to target, the sales analyst needs to compare the actual sales revenue with the expected or planned sales revenue for the same period. The 2019 goal data is the file that contains the expected or planned sales revenue for the year 2019, which is the target that the sales team is aiming to achieve. By comparing the 2019 goal data with the 2019 actual revenue, the sales analyst can calculate the performance attainment, which is the percentage of the goal that was met by the sales team. Option A is incorrect, as 2018 goal data is not relevant for determining 2019 performance attainment. The 2018 goal data contains the expected or planned sales revenue for the year 2018, which is not the target that the sales team is aiming to achieve in 2019. Option B is incorrect, as 2018 actual revenue is not relevant for determining 2019 performance attainment. The 2018 actual revenue contains the actual sales revenue for the year 2018, which is not comparable with the 2019 goal data or the 2019 actual revenue. Option D is incorrect, as 2019 commission plan is not relevant for determining 2019 performance attainment. The 2019 commission plan contains the rules and rates for calculating and paying commissions to the sales team based on their performance attainment, but it does not contain the expected or planned sales revenue for the year 2019.

Correct Answer: D

Optimizing the dashboard is the process of improving its performance and reducing its loading time by applying various techniques and best practices. Some of the common ways to optimize a dashboard are: Reducing the size and complexity of the data model, such as removing unnecessary columns, aggregating data at the source, or using data compression techniques12 Leveraging caching strategies, such as setting appropriate cache refresh intervals or utilizing Power BI's built-in caching mechanisms, to minimize data retrieval delays2 Utilizing query folding, direct query, or live connection to enhance data processing efficiency and enable real-time data updates23 Optimizing DAX queries, such as avoiding nested calculations, using variables, or simplifying measures, to improve data calculation speed23 Reducing visualizations and calculations, such as using fewer or simpler charts, filters, or parameters, to speed up dashboard rendering12 Evaluating the impact of custom visuals on dashboard load time and avoiding or replacing those that are slow or inefficient2 Applying aggregation and summarization techniques, such as using extract filters, context filters, or level of detail expressions, to reduce the amount of data displayed on the dashboard1 Troubleshooting and resolving any issues that may cause slow dashboard load, such as network latency, server overload, or hardware limitations24

Correct Answer: D

Explanation: Customer name is the field that should be masked, because it contains sensitive information that could identify the government accounts that the financial institution deals with. Masking is a technique that replaces or obscures sensitive data with dummy or random data, such as asterisks or hashes. Masking can help protect the privacy and security of the data, while still allowing for some analysis and reporting. Therefore, the correct answer is D. References: [Data Masking | Definition, Techniques and Examples - Talend], [Data masking - Wikipedia]

Correct Answer: C

Explanation: 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.

Correct Answer: A

Explanation: This is because a data lake is a type of storage system that stores unstructured data from different sources, such as text, images, audio, video, etc. A data lake can be used to store unstructured data from different sources by using a schema-on- read approach, which means that it does not impose any structure or format on the data when it is stored, but rather applies it when it is read or accessed. A data lake can also be used to store unstructured data from different sources by using a distributed file system, such as Hadoop, which means that it can store large volumes and varieties of data across multiple servers or nodes. The other storage systems are not used to store unstructured data from different sources. Here is why: A database management system is a type of software application that manages and controls databases, which are collections of structured or semi-structured data that are organized into tables, rows, and columns. A database management system is not used to store unstructured data from different sources, but rather to store structured or semi-structured data from specific sources by using a schema- on-write approach, which means that it imposes a structure or format on the data when it is stored, and requires it to follow certain rules and constraints, such as primary keys, foreign keys, or referential integrity. A database is a type of storage system that stores structured or semi-structured data that are organized into tables, rows, and columns. A database is not used to store unstructured data from different sources, but rather to store structured or semi-structured data from specific sources by using a relational model, which means that it establishes and maintains relationships between different tables based on common columns or keys. A database can also be used to store structured or semi-structured data from specific sources by using a query language, such as SQL, which means that it can access and manipulate the data using statements or commands. A data warehouse is a type of storage system that stores structured or semi- structured data that are integrated and aggregated from different sources or systems, such as databases, cloud services, or web applications. A data warehouse is not used to store unstructured data from different sources, but rather to store structured or semi-structured data from various sources by using an ETL process, which means that it extracts, transforms, and loads the data into a common format, structure, or schema. A data warehouse can also be used to store structured or semi-structured data from various sources by using an OLAP model, which means that it supports online analytical processing of the data using multidimensional cubes or queries.

Correct Answer: C

Correct Answer: A

Explanation: The term that best describes the action of changing the names of categories that you used for different types of books is recoding. Recoding is a process of transforming or modifying the values of a variable or a category to make them more meaningful, consistent, or accurate. For example, you can recode the names of book genres from "Fiction", "Non-Fiction", "Biography", etc. to "FIC", "NF", "BIO", etc. to make them shorter and easier to use. Reference: Recoding Data - SPSS Tutorials - LibGuides at Kent State University