[docs] more fixes for broken links (#5941)

docs/FAQ.rst

@@ -231,7 +231,7 @@ If you are using any Python package that depends on ``threadpoolctl``, you also
 
 Detailed description of conflicts between multiple OpenMP instances is provided in the `following document <https://github.com/joblib/threadpoolctl/blob/master/multiple_openmp.md>`__.
 
-**Solution**: Assuming you are using LightGBM Python-package and conda as a package manager, we strongly recommend using ``conda-forge`` channel as the only source of all your Python package installations because it contains built-in patches to workaround OpenMP conflicts. Some other workarounds are listed `here <https://github.com/joblib/threadpoolctl/blob/master/multiple_openmp.md#workarounds-for-intel-openmp-and-llvm-openmp-case>`__.
+**Solution**: Assuming you are using LightGBM Python-package and conda as a package manager, we strongly recommend using ``conda-forge`` channel as the only source of all your Python package installations because it contains built-in patches to workaround OpenMP conflicts. Some other workarounds are listed `here <https://github.com/joblib/threadpoolctl/blob/master/multiple_openmp.md#user-content-workarounds-for-intel-openmp-and-llvm-openmp-case>`__.
 
 If this is not your case, then you should find conflicting OpenMP library installations on your own and leave only one of them.
 

docs/Parallel-Learning-Guide.rst

@@ -520,7 +520,7 @@ See `the mars documentation`_ for usage examples.
 
 .. _the Dask prediction example: https://github.com/microsoft/lightgbm/tree/master/examples/python-guide/dask/prediction.py
 
-.. _the Dask worker documentation: https://distributed.dask.org/en/latest/worker.html#memory-management
+.. _the Dask worker documentation: https://distributed.dask.org/en/stable/worker-memory.html
 
 .. _the metrics functions from dask-ml: https://ml.dask.org/modules/api.html#dask-ml-metrics-metrics