Find Interview Questions for GLG

Right Answer:

Interpreting data from tables, charts, and graphs means turning visual information into insights. It involves understanding what’s being shown, comparing values, identifying patterns or trends, and drawing conclusions based on the visual representation.

Each format serves a unique purpose:

🔹 Tables
Tables present exact data in rows and columns. Focus on headers to know what each row and column means, and scan the data to find highs, lows, and patterns.

🔹 Charts & Graphs
Visual tools like bar charts, line graphs, pie charts, and scatter plots help you quickly compare values, track changes over time, or understand relationships between variables.

Key tips:

• Read titles, labels, and legends carefully

• Look for trends (increasing, decreasing, steady)

• Compare heights, lengths, or angles visually

• Watch for anomalies or outliers

Right Answer:

Correlation is a statistical measure that shows the relationship between two variables. In simple terms, it tells you whether — and how strongly — two things are connected.

For example, if ice cream sales increase whenever the temperature goes up, we say there is a positive correlation between temperature and ice cream sales.

Right Answer:

Incomplete or missing data is a common challenge in data analysis. Whether it’s skipped survey responses, blank spreadsheet cells, or unavailable values, missing data can affect the accuracy and reliability of your results.

The key is to handle missing data thoughtfully so you can still draw valid conclusions without misleading your interpretation.

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🔍 Common Ways to Handle Missing Data:

1. Identify the Missing Data
 Start by locating where and how much data is missing.
 Check: Is it random or following a pattern? Are entire sections missing or just a few values?

2. Remove Incomplete Entries (if appropriate)
 If only a small number of rows are missing data, and they don’t heavily impact the dataset, you can safely remove them.

3. Use Imputation (Estimate Missing Values)
 If the dataset is large and important, you can fill in missing values using methods like:
– Mean or median substitution (for numerical data)
– Mode (for categorical data)
– Regression or predictive models (for more advanced cases)

4. Use Available Data Only
 In some cases, you can perform analysis using just the complete parts of the dataset — as long as it doesn’t bias your results.

5. Flag and Acknowledge Missing Data
 Be transparent in reports. Clearly mention how much data is missing and how it was handled.

6. Ask Why the Data Is Missing
 Sometimes missing data reveals a deeper issue (e.g., system errors, survey confusion). Understanding the cause can help prevent future problems.

Right Answer:
In Siebel, visibility is controlled using "Visibility Rules" and "Applet Properties." You can set visibility for applets, fields, and buttons by configuring their properties in the Siebel Tools, such as using the "Visible" property or applying "Visibility Conditions" based on user roles, data conditions, or other criteria.

Right Answer:
The number of cats is 999.

Right Answer:
To import List of Values (LOVs) in Siebel, follow these steps:

1. Navigate to the "List of Values" screen in Siebel.
2. Use the "File" menu and select "Import".
3. Choose the appropriate file format (usually CSV or Excel).
4. Map the fields in your file to the corresponding LOV fields in Siebel.
5. Validate the data and then execute the import process.
6. Review the import results for any errors or warnings.

Right Answer:
In accounting, "relax" typically refers to the process of easing or reducing certain financial constraints or requirements, such as payment terms or credit limits, to improve cash flow or customer relations.

Right Answer:
Yes, I have created a root node in the CRM system.