Synthetic keys are automatically created by Qlik when two or more tables have two or more common fields, which can lead to complex, spider-web associations and severely impact performance. The professional approach is to proactively analyze the data model with Table Viewer, identify these synthetic keys, and resolve them by creating explicit, optimized composite keys in the load script. This addresses the root cause of the model complexity.

The associative engine is Qlik’s core strength. Loading the targets and sales as separate tables linked by common dimensions (e.g., Region, Month) is the optimal approach. It preserves the granularity of both tables, allows for flexible analysis (e.g., seeing targets for regions with no sales), and is more maintainable than forcing a join which can duplicate data or lose context.

The issue is one of context. The variable `vMaxYear` is defined as `=Max(Year)`, which calculates the maximum year within the user’s current selection. When no selection is made, it evaluates over the entire dataset. However, if the selection is *truly empty* (e.g., a field is cleared to a state with no valid data), `Max(Year)` can return NULL, causing the whole measure to return NULL. The solution is often to define the variable with a default value or use set analysis that is independent of the user’s product selection.

The “Description” property in an object’s properties panel is specifically designed for accessibility. Screen readers will read this text aloud, allowing users with visual impairments to understand the purpose and key content of the chart. This is a fundamental and required practice for accessible dashboard design, far more effective than stylistic changes or external files.

In Qlik, a logical comparison like `Sum(Sales) > Sum(Target)` returns `-1` for TRUE and `0` for FALSE. The `If()` function correctly interprets `-1` as a TRUE condition. Therefore, `If(Sum(Sales) > Sum(Target), ‘On Track’, ‘Review Needed’)` is the standard, readable, and functionally correct way to implement this logic. The `Pick()` function in option B would require the condition to return `1` or `2` for its index.

For Section Access to dynamically reduce the data loaded into memory for each user, the **`Reduce Data`** option must be explicitly enabled in the app properties (found in the Data Load Editor under “Reduce Data”). Without this, the Section Access logic will still control sheet/object access but will load the entire dataset into memory first, defeating the purpose of data-level security. This is a common oversight.

Option 2 correctly calculates the percentage change: `(Current – Previous) / Previous`. It uses the `Num()` function with the format `’#,##0.0%’` and prepends a `’+’` sign manually to ensure it always shows. The set analysis `{$}` is appropriate. Option 4 is close but the `’+’` in the `Num()` format string only shows for positive numbers, which is acceptable, but option 2 is more explicit and a common pattern.

The core pattern for incremental load involves: 1) Loading only records where a timestamp or ID is greater than the last successfully stored load time/pointer. 2) Persisting this pointer (e.g., `vLastLoadTime`) after each successful load, typically by storing it in a control table or a small QVD. This ensures the next load picks up where the last one left off, dramatically improving reload performance for large datasets.

The chart’s sort by “Expression” feature is the most dynamic and appropriate solution. The expression `=If(Product=’AlphaPrime’, 1, Sum(Sales))` assigns a value of `1` to “AlphaPrime” and the actual sales amount to all other products. Sorting descending will place the item with value `1` at the top, followed by all others sorted by their sales value. This is done at the visualization level without modifying the data model.

Synthetic keys are automatically created by Qlik when two or more tables have two or more common fields, which can lead to complex, spider-web associations and severely impact performance. The professional approach is to proactively analyze the data model with Table Viewer, identify these synthetic keys, and resolve them by creating explicit, optimized composite keys in the load script. This addresses the root cause of the model complexity.0