Explanation
To solve this question, you need to know that DataFrame.sample() is not guaranteed to return the exact fraction of the number of rows specified as an argument. Furthermore, since duplicates may be returned, you should understand that the operator's withReplacement argument should be set to True. A force= argument for the operator does not exist.
While the take argument returns an exact number of rows, it will just take the first specified number of rows (1000 in this question) from the DataFrame. Since the DataFrame does not include duplicate rows, there is no potential of any of those returned rows being duplicates when using take(), so the correct answer cannot involve take().
More info: pyspark.sql.DataFrame.sample - PySpark 3.1.2 documentation
Static notebook | Dynamic notebook: See test 2

Explanation
Due to the mutability of DataFrames after transformations, Spark reproduces them using observed lineage in case of worker node failure.
Wrong - Between transformations, DataFrames are immutable. Given that Spark also records the lineage, Spark can reproduce any DataFrame in case of failure. These two aspects are the key to understanding fault tolerance in Spark.
Spark builds a fault-tolerant layer on top of the legacy RDD data system, which by itself is not fault tolerant.
Wrong. RDD stands for Resilient Distributed Dataset and it is at the core of Spark and not a "legacy system".
It is fault-tolerant by design.
Spark helps fast recovery of data in case of a worker fault by providing the MEMORY_AND_DISK storage level option.
This is not true. For supporting recovery in case of worker failures, Spark provides "_2", "_3", and so on, storage level options, for example MEMORY_AND_DISK_2. These storage levels are specifically designed to keep duplicates of the data on multiple nodes. This saves time in case of a worker fault, since a copy of the data can be used immediately, vs. having to recompute it first.
Spark is only fault-tolerant if this feature is specifically enabled via the spark.fault_recovery.enabled property.
No, Spark is fault-tolerant by design.

Explanation
Correct code block:
transactionsDf.filter(transactionsDf.storeId.isNotNull()).groupBy("storeId").pivot("productId", [2,
3]).mean("predError").orderBy("storeId")
Output of correct code block:
+-------+----+----+
|storeId| 2| 3|
+-------+----+----+
| 2| 6.0|null|
| 3|null|null|
| 25| 3.0| 3.0|
+-------+----+----+
This question is quite convoluted and requires you to think hard about the correct order of operations.
The pivot method also makes an appearance - a method that you may not know all that much about (yet).
At the first position in all answers is code block 4, so the question is essentially just about the ordering of the remaining 4 code blocks.
The question states that the returned DataFrame should be sorted by column storeId. So, it should make sense to have code block 3 which includes the orderBy operator at the very end of the code block. This leaves you with only two answer options.
Now, it is useful to know more about the context of pivot in PySpark. A common pattern is groupBy, pivot, and then another aggregating function, like mean. In the documentation linked below you can see that pivot is a method of pyspark.sql.GroupedData - meaning that before pivoting, you have to use groupBy. The only answer option matching this requirement is the one in which code block 2 (which includes groupBy) is stated before code block 5 (which includes pivot).
More info: pyspark.sql.GroupedData.pivot - PySpark 3.1.2 documentation
Static notebook | Dynamic notebook: See test 3

Explanation
Output of correct code block:
+----------------------------+
|size(split(supplier, , -1))|
+----------------------------+
| 3|
| 1|
| 3|
+----------------------------+
This question shows a typical use case for the split command: Splitting a string into words. An additional difficulty is that you are asked to count the words. Although it is tempting to use the count method here, the size method (as in: size of an array) is actually the correct one to use. Familiarize yourself with the split and the size methods using the linked documentation below.
More info:
Split method: pyspark.sql.functions.split - PySpark 3.1.2 documentation Size method: pyspark.sql.functions.size - PySpark 3.1.2 documentation Static notebook | Dynamic notebook: See test 2