CompTIA Updated DA0-001 Exam Questions and Answers by zayd

Missing data is a type of data quality issue that occurs when some values in a data set are not recorded or available. Missing data can affect the validity and reliability of survey results, especially if the missing values are not random or ignorable. Missing data can also reduce the sample size and the statistical power of the analysis12

If 100% participation is needed on survey results, a data analyst would look for missing data first, because missing data would indicate that some participants did not complete or submit the survey, or that some responses were not recorded or transmitted correctly. A data analyst would need to identify the causes and patterns of missing data, and apply appropriate methods to handle or prevent missing data, such as imputation, deletion, weighting, or follow-up12

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

When dealing with multiple date columns from different data sources, it’s crucial to ensure consistency and accuracy in the dataset. The best practice is to merge the date columns and standardize the date format across the entire dataset. This approach helps maintain data integrity, simplifies analysis, and avoids confusion that could arise from having multiple date formats. Unifying the date format is particularly important when the data will be used for time series analysis or when dates are key to joining with other datasets.

References:

Best practices in data merging emphasize the importance of a single point of reference and the need to avoid data loss or damage to individual data structures1.

Power BI guides suggest that merging columns should be done carefully to maintain data integrity and avoid errors and inconsistencies2.

Oracle Blogs highlight the need for a consistent number of columns among data sources when combining data with unions3.

Excel tutorials recommend organizing data before merging and using formulas for complex merges4.

An Excel guide on merging date and time columns advises employing functions to ensure seamless handling of non-date values5.