<prompt> Rala sebopeho sa data ho boloka bukantswe ya mantswe a 5,000.nan  <response>: Instruction in English: Design a data structure to store a dictionary of 5,000 words. . Response in English: One possible data structure to efficiently store a dictionary of 5,000 words is a Trie also known as a prefix tree. A Trie is a tree-like data structure where each node represents a character or prefix, and edge represents an additional character added to the prefix. Each word is stored as a series of connected nodes, starting from the root node and ending with a special end of word node or marker.For example, lets say were storing the words cat, car, and cart in our Trie. The root node would have two children, c and a. The c node would then have a child a, and the a node would have two children, t and r. The t node would have an end of word marker to indicate that the word cat is stored in the Trie. The r node would have a child t, which would have an end of word marker, indicating that the word cart is stored in the Trie. The r node would also have its own end of word marker, indicating that the word car is stored in the Trie.Some benefits of using a Trie to store a dictionary include:- Fast lookup time: Words can be found by traversing the Trie from the root node, following the edges that correspond with each character in the word. The time complexity is Om, where m is the length of the word being searched for.- Efficient storage: Since common prefixes are shared among the nodes in the Trie, it can save space for large dictionaries.- Easy to implement prefix-based search: A Trie can quickly return all words that start with a given prefix by traversing the Trie until the end of the prefix is reached, and then performing a depth-first search to collect all words that are children of that node.An alternative data structures to store dictionary could be a hash table or balanced search tree, but Tries provide an optimal solution for the given use case. . Response in Sesotho: Sebopeho se seng se ka khonehang sa ho boloka bukana ea mantsoe a 5,000 hantle ke Trie e tsejoang hape e le sefate sa prefix. Trie ke sebopeho sa data se kang sefate moo node e ngoe le e ngoe e emelang tlhaku kapa sehlongoapele, me bohale bo emela tlhaku e eketsehileng e kenyellelitsoeng ho sehlongwapele. Lentsoe le leng le le leng le bolokoa e le letoto la li-node tse hokahaneng, ho qala ka node ea motso le ho qetella ka qetello e khethehileng ea node ea lentsoe kapa letšoao. Mohlala, ha re re re boloka mantsoe katse, koloi, le koloi ho Trie ea rona. Node ea motso e ne e tla ba le bana ba babeli, c le a. Node ea c e ne e tla ba le ngoana a, me node ea a e be le bana ba babeli, t le r. Node ea t e tla ba le pheletso ea letšoao la lentsoe ho bontša hore lentsoe katse le bolokiloe ho Trie. Node ea r e ne e tla ba le ngoana t, e neng e tla ba le qetellong ea letšoao la lentsoe, e leng se bontšang hore lentsoe koloi le bolokiloe ho Trie. Node ea r e tla boela e be le qetello ea eona ea letšoao la lentsoe, e leng se bontšang hore lentsoe koloi le bolokiloe ho Trie. Melemo e meng ea ho sebelisa Trie ho boloka buka e hlalosang mantsoe e kenyelletsa: - Nako ea ho batla ka potlako: Mantsoe a ka fumanoa ka ho tšela Trie ho tloha motsong oa motso, ho latela mapheo a lumellanang le tlhaku ka ngoe ea lentsoe. Nako e rarahaneng ke Om, moo m e leng bolelele ba lentsoe le batlisisoang. - Polokelo e sebetsang hantle: Kaha li-prefixes tse tloaelehileng li arolelanoa hara li-node tsa Trie, li ka boloka sebaka bakeng sa dikishinari tse kholo. - Ho bonolo ho kenya tsebetsong patlisiso e thehiloeng ho li-prefix: Trie e ka khutlisa kapele mantsoe ohle a qalang ka sehlongwapele se fanoeng ka ho haola le Trie ho fihlela qetellong ea sehlongwapele se fihla, ebe e etsa patlisiso e tebileng pele ho bokella mantsoe ohle ao e leng bana. ea node eo. Libopeho tse ling tsa data ho boloka bukantswe e ka ba tafole ya hash kapa sefate sa ho batla se leka-lekaneng, empa Tries e fana ka tharollo e nepahetseng bakeng sa tshebediso e fanoeng. 