Explanation:

Hash tables are data structures that provide efficient average case solutions for various problems. However, they may not be suitable for all types of problems. In this case, the correct option is 'D' - Symbol table lookup, which is a problem that hash tables may not be efficient for.

Counting distinct values:
- A hash table can be used to efficiently count the number of distinct values in a collection of data.
- Each value can be hashed and stored in the hash table, and the count of distinct values can be determined by the number of entries in the hash table.

Dynamic dictionary:
- A hash table can be used to implement a dynamic dictionary, where elements can be inserted, deleted, and searched efficiently.
- Each element can be hashed and stored in the hash table, allowing for constant time insertion, deletion, and search operations.

Range search:
- Hash tables can also be used for range search problems, where elements within a given range need to be identified.
- Each element can be hashed and stored in the hash table, and the range search can be performed by iterating through the hash table entries within the specified range.

Symbol table lookup:
- Symbol table lookup involves searching for a particular key in a collection of key-value pairs.
- While hash tables can be used for symbol table lookup, they may not provide an efficient average case solution.
- In worst case scenarios, there can be collisions, where different keys map to the same hash value, resulting in degraded performance.
- Additionally, hash tables may require resizing and rehashing operations when the number of elements exceeds a certain threshold, which can also impact performance.

Therefore, while hash tables can contribute to efficient average case solutions for counting distinct values, dynamic dictionaries, and range search problems, they may not be the most suitable data structure for symbol table lookup.