diff --git a/lessons/5-NLP/15-LanguageModeling/README.md b/lessons/5-NLP/15-LanguageModeling/README.md
index 961c75b7..29023275 100644
--- a/lessons/5-NLP/15-LanguageModeling/README.md
+++ b/lessons/5-NLP/15-LanguageModeling/README.md
@@ -3,7 +3,7 @@
 
 Semantic embeddings, such as Word2Vec and GloVe, are in fact a first step towards **language modeling** - creating models that somehow *understand* (or *represent*) the nature of the language.
 
-## [Pre-lecture quiz](tbd)
+## [Pre-lecture quiz](https://black-ground-0cc93280f.1.azurestaticapps.net/quiz/115)
 
 The main idea behind language modeling is training them on unlabeled datasets in an unsupervised manner. This is important because we have huge amounts of unlabeled text available, while the amount of labeled text would always be limited by the amount of effort we can spend on labeling. Most often, we can build language models that can **predict missing words** in the text, because it is easy to mask out a random word in text and use it as a training sample.
 
@@ -25,7 +25,7 @@ TBD
 
 TBD
 
-## [Post-lecture quiz](tbd)
+## [Post-lecture quiz](https://black-ground-0cc93280f.1.azurestaticapps.net/quiz/215)
 
 ## Review & Self Study