[ML] AIOps: Adds cardinality check to Log Rate Analysis (#181129)

## Summary

Part of #181111.

This filters field/value items from the results if the field has a
cardinality of just 1 since it wouldn't be useful as a result.

Before (you can easily spot fields with cardinality of 1 because the
mini histogram in the table is just one color which means the value is
present in all logs):


![image](https://github.com/elastic/kibana/assets/230104/2904c026-5a69-43b7-b80f-87923368b506)

After:


![image](https://github.com/elastic/kibana/assets/230104/7a9bffae-9991-4584-91f1-cff9fdc1eaf1)

### Checklist

- [x] [Unit or functional
tests](https://www.elastic.co/guide/en/kibana/master/development-tests.html)
were updated or added to match the most common scenarios
- [x] This was checked for breaking API changes and was [labeled
appropriately](https://www.elastic.co/guide/en/kibana/master/contributing.html#kibana-release-notes-process)

x-pack/packages/ml/aiops_log_rate_analysis/queries/fetch_significant_term_p_values.ts

@@ -56,8 +56,18 @@ export const getSignificantTermRequest = (
     ];
   }
 
-  const pValueAgg: Record<'change_point_p_value', estypes.AggregationsAggregationContainer> = {
-    change_point_p_value: {
+  const pValueAgg: Record<
+    'sig_term_p_value' | 'distinct_count',
+    estypes.AggregationsAggregationContainer
+  > = {
+    // Used to identify fields with only one distinct value which we'll ignore in the analysis.
+    distinct_count: {
+      cardinality: {
+        field: fieldName,
+      },
+    },
+    // Used to calculate the p-value for terms of the field.
+    sig_term_p_value: {
       significant_terms: {
         field: fieldName,
         background_filter: {
@@ -158,13 +168,26 @@ export const fetchSignificantTermPValues = async (
     }
 
     const overallResult = (
-      randomSamplerWrapper.unwrap(resp.aggregations) as Record<'change_point_p_value', Aggs>
-    ).change_point_p_value;
+      randomSamplerWrapper.unwrap(resp.aggregations) as Record<'sig_term_p_value', Aggs>
+    ).sig_term_p_value;
+
+    const distinctCount = (
+      randomSamplerWrapper.unwrap(resp.aggregations) as Record<
+        'distinct_count',
+        estypes.AggregationsCardinalityAggregate
+      >
+    ).distinct_count.value;
 
     for (const bucket of overallResult.buckets) {
       const pValue = Math.exp(-bucket.score);
 
-      if (typeof pValue === 'number' && pValue < LOG_RATE_ANALYSIS_SETTINGS.P_VALUE_THRESHOLD) {
+      if (
+        typeof pValue === 'number' &&
+        // Skip items where the p-value is not significant.
+        pValue < LOG_RATE_ANALYSIS_SETTINGS.P_VALUE_THRESHOLD &&
+        // Skip items where the field has only one distinct value.
+        distinctCount > 1
+      ) {
         result.push({
           key: `${fieldName}:${String(bucket.key)}`,
           type: SIGNIFICANT_ITEM_TYPE.KEYWORD,