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

The correct answer is A. UNION ALL.
UNION ALL is a SQL statement that appends two tables that have the same number of columns and compatible data types. UNION ALL preserves all the rows from both tables, including any duplicates12 B: MERGE is not correct, because MERGE is a SQL statement that combines the data of two tables based on a common column. MERGE can perform insert, update, or delete operations on the target table depending on the matching or non-matching rows from the source table34 C: GROUP BY is not correct, because GROUP BY is a SQL clause that groups the rows of a table based on one or more columns. GROUP BY is often used with aggregate functions, such as SUM, AVG, COUNT, etc., to calculate summary statistics for each group56 D: JOIN is not correct, because JOIN is a SQL clause that combines the data of two tables based on a common column or condition. JOIN can produce different results depending on the type of join, such as INNER JOIN, LEFT JOIN, RIGHT JOIN, etc.

Correct answer B. Data steward.
A data steward is responsible for leading an organization's data governance activities, which include data quality, security, privacy, and regulatory compliance.

1. Duplicates
2. Misspellings
The most common data quality issues are difficult to resolve in Excel because of their rigidity. It forces analysts to do a ton of manual work, which results in a high probability of an error being introduced to the data set. Those common issues include:
- Blanks
- Nulls
- Outliers
- Duplicates
- Extra spaces
- Misspellings
- Abbreviations and domain-specific variations
- Formula error codes
When introduced, these errors can skew or even invalidate the resulting analysis. A smart tool would minimize the possibility of error by automating the manual work. In Excel, you might look for data quality issues in one of two ways. First, you might use auto filters on specific columns to scan for anomalies and blanks or you might use a pivot table to find gaps and discrepancies.
In either case, you're scanning for the anomalies yourself. Suffice it to say that's not a very efficient process. It also means accuracy is only as good as the analyst's eye, so the probability of error varies throughout the day.