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Distinct long strings compress poorly and enlarge columnar storage. That targets reduce unnecessary high-cardinality memory, not load only needed transaction history.

Coarser date values have lower cardinality than millisecond timestamps. It may suit compress date-grain reporting fields, not load only needed transaction history.

Avoiding duplicate key fields saves memory without breaking relationships. It is useful for keeping required keys without redundancy, not for load only needed transaction history.

Mixed grains create duplicated values and unnecessary rows. It is useful for avoiding inflated cross-grain monthly totals, not for retain accurate nonadditive aggregation semantics.

Coarser preaggregation reduces fact row volume for common visuals. That is meant for improve daily regional summary speed, not retain accurate nonadditive aggregation semantics.

Aggregation tables answer summary queries while detailed storage supports exceptions. It would address accelerate summaries without removing drillthrough, not retain accurate nonadditive aggregation semantics.

Folding inspection shows which transformations delegate to the data source. It would address diagnose source query delegation, not shift heavy nonfoldable preprocessing upstream.

Database-side projection avoids unnecessary transfer and model load. That targets minimize imported source fields, not shift heavy nonfoldable preprocessing upstream.

Early pushdown reduces rows that must be processed locally. It is useful for preserving database-side date filtering, not for shift heavy nonfoldable preprocessing upstream.

Each visual can add query and rendering overhead. It is useful for control excessive page visual count, not for reducing custom visual render bottlenecks.

Smaller visual result sets reduce rendering and transfer costs. Its scope is avoid massive detail table rendering, not reduce custom visual render bottlenecks.

Region-level aggregation reduces mark count and visual complexity. That is meant for improve geographic visual responsiveness, not reduce custom visual render bottlenecks.

DirectQuery performance depends heavily on source query plans and database latency. That targets accelerate frequent source filter queries, not simplify complex live-query joins.

Reducing requests helps control source load and interactive latency. It may suit limit redundant DirectQuery queries, not simplify complex live-query joins.

Import trades instantaneous source freshness for compressed in-memory query speed. It is useful for choose cached mode for slow DirectQuery, not for simplify complex live-query joins.

Large unique text blobs add memory without supporting measures. It is useful for removing unused unstructured payload, not for avoiding redundant materialized columns.

Smaller categorical representations can improve dictionary compression. That is meant for optimize repetitive dimension attributes, not avoid redundant materialized columns.

Narrow valid types can reduce memory and improve processing efficiency. It would address efficient numeric column typing, not avoid redundant materialized columns.

Partition elimination relies on efficient source-filter execution. It would address enable effective incremental refresh pushdown, not avoid needless partitions for small lookup.

Retain and refresh ranges can differ under incremental refresh policies. That targets long history with narrow mutable window, not avoid needless partitions for small lookup.

Incremental policies limit processing of stable historical partitions. It is useful for avoiding full-history daily reload, not for avoiding needless partitions for small lookup.

A simple column aggregation can avoid unnecessary row-wise formula evaluation. It is useful for efficient simple additive sum, not for replacing repeated runtime text scanning.

Variables promote reuse and make repeated expressions easier to optimize. Its scope is reduce redundant expression evaluation, not replace repeated runtime text scanning.

Large intermediate tables can consume computation without changing business results. That is meant for avoid large unnecessary virtual cartesian sets, not replace repeated runtime text scanning.

Distinct query and rendering timings point to different remediation paths. That targets distinguish backend query from visual rendering, not trace source-side live query latency.

Rendering bottlenecks can exist even with efficient backend query evaluation. It may suit resolve client-side visual slowness, not trace source-side live query latency.

Refresh transformation time is a different stage than visual query execution. It is useful for diagnosing ingestion bottleneck rather than DAX, not for trace source-side live query latency.

Coarser rows reduce memory while retaining permitted reporting resolution. It is useful for trim excessive time-series detail, not for avoiding repeated constant text in fact.

Column pruning reduces compressed data and scan overhead. That is meant for smaller model by removing dead columns, not avoid repeated constant text in fact.

Integer surrogate keys can improve relationship storage efficiency and lookup performance. It would address replace unwieldy text join keys, not avoid repeated constant text in fact.

Composite models can incur costly cross-source query behavior. It would address reduce federated model execution cost, not cache slow-changing lookup attributes.

Fallback execution can increase latency and change query behavior. That targets mitigate Direct Lake query fallback, not cache slow-changing lookup attributes.

Dual dimensions can serve cached Import queries and DirectQuery relationships depending on execution context. It is useful for optimize hybrid model dimensions, not for cache slow-changing lookup attributes.