[FLINK-18422][docs] Update Prefer tag in documentation 'Fault Toleran…

…ce training lesson'

This closes apache#12778

docs/learn-flink/fault_tolerance.md

@@ -139,7 +139,7 @@ checkpoint, it has all of the sources record their offsets and insert numbered _
 into their streams. These barriers flow through the job graph, indicating the part of the stream
 before and after each checkpoint. 
 
-<img src="{{ site.baseurl }}/fig/stream_barriers.svg" alt="Checkpoint barriers are inserted into the streams" class="center" width="80%" />
+<img src="{% link /fig/stream_barriers.svg %}" alt="Checkpoint barriers are inserted into the streams" class="center" width="80%" />
 
 Checkpoint _n_ will contain the state of each operator that resulted from having consumed **every
 event before checkpoint barrier _n_, and none of the events after it**.
@@ -149,7 +149,7 @@ with two input streams (such as a `CoProcessFunction`) perform _barrier alignmen
 snapshot will reflect the state resulting from consuming events from both input streams up to (but
 not past) both barriers.
 
-<img src="{{ site.baseurl }}/fig/stream_aligning.svg" alt="Barrier alignment" class="center" width="100%" />
+<img src="{% link /fig/stream_aligning.svg %}" alt="Barrier alignment" class="center" width="100%" />
 
 Flink's state backends use a copy-on-write mechanism to allow stream processing to continue
 unimpeded while older versions of the state are being asynchronously snapshotted. Only when the

docs/learn-flink/fault_tolerance.zh.md

@@ -113,13 +113,13 @@ Flink 使用 [Chandy-Lamport algorithm](https://en.wikipedia.org/wiki/Chandy-Lam
 
 当 checkpoint coordinator（job manager 的一部分）指示 task manager 开始 checkpoint 时，它会让所有 sources 记录它们的偏移量，并将编号的 _checkpoint barriers_ 插入到它们的流中。这些 barriers 流经 job graph，标注每个 checkpoint 前后的流部分。
 
-<img src="{% link fig/stream_barriers.svg %}" alt="Checkpoint barriers are inserted into the streams" class="center" width="80%" />
+<img src="{% link /fig/stream_barriers.svg %}" alt="Checkpoint barriers are inserted into the streams" class="center" width="80%" />
 
 Checkpoint _n_ 将包含每个 operator 的 state，这些 state 是对应的 operator 消费了**严格在 checkpoint barrier _n_ 之前的所有事件，并且不包含在此（checkpoint barrier _n_）后的任何事件**后而生成的状态。
 
 当 job graph 中的每个 operator 接收到 barriers 时，它就会记录下其状态。拥有两个输入流的 Operators（例如 `CoProcessFunction`）会执行 _barrier 对齐（barrier alignment）_ 以便当前快照能够包含消费两个输入流 barrier 之前（但不超过）的所有 events 而产生的状态。
 
-<img src="{% link fig/stream_aligning.svg %}" alt="Barrier alignment" class="center" width="100%" />
+<img src="{% link /fig/stream_aligning.svg %}" alt="Barrier alignment" class="center" width="100%" />
 
 Flink 的 state backends 利用写时复制（copy-on-write）机制允许当异步生成旧版本的状态快照时，能够不受影响地继续流处理。只有当快照被持久保存后，这些旧版本的状态才会被当做垃圾回收。