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NEW QUESTION 51
Which of the following code blocks saves DataFrame transactionsDf in location /FileStore/transactions.csv as a CSV file and throws an error if a file already exists in the location?
Answer: D
Explanation:
Explanation
Static notebook | Dynamic notebook: See test 1
(https://flrs.github.io/spark_practice_tests_code/#1/28.html ,
https://bit.ly/sparkpracticeexams_import_instructions)
NEW QUESTION 52
The code block shown below should return a copy of DataFrame transactionsDf with an added column cos.
This column should have the values in column value converted to degrees and having the cosine of those converted values taken, rounded to two decimals. Choose the answer that correctly fills the blanks in the code block to accomplish this.
Code block:
transactionsDf.__1__(__2__, round(__3__(__4__(__5__)),2))
2. "cos"
3. cos
4. degrees
5. "transactionsDf.value"B. 1. withColumn
2. col("cos")
3. cos
4. degrees
5. col("value")
E
. 1. withColumn
2. "cos"
3. degrees
4. cos
5. col("value")C. 1. withColumn
2. "cos"
3. cos
4. degrees
5. transactionsDf.valueD. 1. withColumn
2. col("cos")
3. cos
4. degrees
5. transactionsDf.value
Answer: C
Explanation:
Explanation
Correct code block:
transactionsDf.withColumn("cos", round(cos(degrees(transactionsDf.value)),2)) This question is especially confusing because col, "cos" are so similar. Similar-looking answer options can also appear in the exam and, just like in this question, you need to pay attention to the details to identify what the correct answer option is.
The first answer option to throw out is the one that starts with withColumnRenamed: The question NO:
speaks specifically of adding a column. The withColumnRenamed operator only renames an existing column, however, so you cannot use it here.
Next, you will have to decide what should be in gap 2, the first argument of transactionsDf.withColumn().
Looking at the documentation (linked below), you can find out that the first argument of withColumn actually needs to be a string with the name of the column to be added. So, any answer that includes col("cos") as the option for gap 2 can be disregarded.
This leaves you with two possible answers. The real difference between these two answers is where the cos and degree methods are, either in gaps 3 and 4, or vice-versa. From the question you can find out that the new column should have "the values in column value converted to degrees and having the cosine of those converted values taken". This prescribes you a clear order of operations: First, you convert values from column value to degrees and then you take the cosine of those values. So, the inner parenthesis (gap 4) should contain the degree method and then, logically, gap 3 holds the cos method. This leaves you with just one possible correct answer.
More info: pyspark.sql.DataFrame.withColumn - PySpark 3.1.2 documentation Static notebook | Dynamic notebook: See test 3
NEW QUESTION 53
Which of the following statements about reducing out-of-memory errors is incorrect?
Answer: D
Explanation:
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
NEW QUESTION 54
The code block displayed below contains an error. The code block should return DataFrame transactionsDf, but with the column storeId renamed to storeNumber. Find the error.
Code block:
transactionsDf.withColumn("storeNumber", "storeId")
Answer: E
Explanation:
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
NEW QUESTION 55
......
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