Explanation
transactionsDf.coalesce(24)
No, the coalesce() method can only reduce, but not increase the number of partitions.
transactionsDf.repartition()
No, repartition() requires a numPartitions argument.
transactionsDf.repartition("itemId", 24)
No, here the cols and numPartitions argument have been mixed up. If the code block would be transactionsDf.repartition(24, "itemId"), this would be a valid solution.
transactionsDf.repartition(24, boost=True)
No, there is no boost argument in the repartition() method.

Explanation
Correct code block:
transactionsDf.select("storeId").printSchema()
The difficulty of this question is that it is hard to solve with the stepwise first-to-last-gap approach that has worked well for similar questions, since the answer options are so different from one another. Instead, you might want to eliminate answers by looking for patterns of frequently wrong answers.
A first pattern that you may recognize by now is that column names are not expressed in quotes. For this reason, the answer that includes storeId should be eliminated.
By now, you may have understood that the DataFrame.limit() is useful for returning a specified amount of rows. It has nothing to do with specific columns. For this reason, the answer that resolves to limit("storeId") can be eliminated.
Given that we are interested in information about the data type, you should question whether the answer that resolves to limit(1).columns provides you with this information. While DataFrame.columns is a valid call, it will only report back column names, but not column types. So, you can eliminate this option.
The two remaining options either use the printSchema() or print_schema() command. You may remember that DataFrame.printSchema() is the only valid command of the two. The select("storeId") part just returns the storeId column of transactionsDf - this works here, since we are only interested in that column's type anyways.
More info: pyspark.sql.DataFrame.printSchema - PySpark 3.1.2 documentation Static notebook | Dynamic notebook: See test 3

Explanation
Correct code block:
transactionsDf.withColumnRenamed("storeId", "storeNumber")
More info: pyspark.sql.DataFrame.withColumnRenamed - PySpark 3.1.1 documentation Static notebook | Dynamic notebook: See test 1

Explanation
Concatenating multiple string columns into a single column may guard against out-of-memory errors.
Exactly, this is an incorrect answer! Concatenating any string columns does not reduce the size of the data, it just structures it a different way. This does little to how Spark processes the data and definitely does not reduce out-of-memory errors.
Reducing partition size can help against out-of-memory errors.
No, this is not incorrect. Reducing partition size is a viable way to aid against out-of-memory errors, since executors need to load partitions into memory before processing them. If the executor does not have enough memory available to do that, it will throw an out-of-memory error. Decreasing partition size can therefore be very helpful for preventing that.
Decreasing the number of cores available to each executor can help against out-of-memory errors.
No, this is not incorrect. To process a partition, this partition needs to be loaded into the memory of an executor. If you imagine that every core in every executor processes a partition, potentially in parallel with other executors, you can imagine that memory on the machine hosting the executors fills up quite quickly. So, memory usage of executors is a concern, especially when multiple partitions are processed at the same time. To strike a balance between performance and memory usage, decreasing the number of cores may help against out-of-memory errors.
Setting a limit on the maximum size of serialized data returned to the driver may help prevent out-of-memory errors.
No, this is not incorrect. When using commands like collect() that trigger the transmission of potentially large amounts of data from the cluster to the driver, the driver may experience out-of-memory errors. One strategy to avoid this is to be careful about using commands like collect() that send back large amounts of data to the driver. Another strategy is setting the parameter spark.driver.maxResultSize. If data to be transmitted to the driver exceeds the threshold specified by the parameter, Spark will abort the job and therefore prevent an out-of-memory error.
Limiting the amount of data being automatically broadcast in joins can help against out-of-memory errors.
Wrong, this is not incorrect. As part of Spark's internal optimization, Spark may choose to speed up operations by broadcasting (usually relatively small) tables to executors. This broadcast is happening from the driver, so all the broadcast tables are loaded into the driver first. If these tables are relatively big, or multiple mid-size tables are being broadcast, this may lead to an out-of- memory error. The maximum table size for which Spark will consider broadcasting is set by the spark.sql.autoBroadcastJoinThreshold parameter.
More info: Configuration - Spark 3.1.2 Documentation and Spark OOM Error - Closeup. Does the following look familiar when... | by Amit Singh Rathore | The Startup | Medium

Explanation
DataFrame.select()
Correct, DataFrame.select() is a transformation. When the command is executed, it is evaluated lazily and returns an RDD when it is triggered by an action.
DataFrame.foreach()
Incorrect, DataFrame.foreach() is not a transformation, but an action. The intention of foreach() is to apply code to each element of a DataFrame to update accumulator variables or write the elements to external storage. The process does not return an RDD - it is an action!
DataFrame.first()
Wrong. As an action, DataFrame.first() executed immediately and returns the first row of a DataFrame.
DataFrame.count()
Incorrect. DataFrame.count() is an action and returns the number of rows in a DataFrame.
DataFrame.show()
No, DataFrame.show() is an action and displays the DataFrame upon execution of the command.