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NEW QUESTION 36
The code block shown below should write DataFrame transactionsDf as a parquet file to path storeDir, using brotli compression and replacing any previously existing file. Choose the answer that correctly fills the blanks in the code block to accomplish this.
transactionsDf.__1__.format("parquet").__2__(__3__).option(__4__, "brotli").__5__(storeDir)
2. mode
3. "replace"
4. "compression"
5. pathB. 1. save
2. mode
3. "ignore"
4. "compression"
5. pathC. 1. store
2. with
3. "replacement"
4. "compression"
5. pathD. 1. write
2. mode
3. "overwrite"
4. compression
5. parquetE. 1. write
2. mode
3. "overwrite"
4. "compression"
5. save
(Correct)
Answer: A
Explanation:
Explanation
Correct code block:
transactionsDf.write.format("parquet").mode("overwrite").option("compression", "snappy").save(storeDir) Solving this question requires you to know how to access the DataFrameWriter (link below) from the DataFrame API - through DataFrame.write.
Another nuance here is about knowing the different modes available for writing parquet files that determine Spark's behavior when dealing with existing files. These, together with the compression options are explained in the DataFrameWriter.parquet documentation linked below.
Finally, bracket __5__ poses a certain challenge. You need to know which command you can use to pass down the file path to the DataFrameWriter. Both save and parquet are valid options here.
More info:
- DataFrame.write: pyspark.sql.DataFrame.write - PySpark 3.1.1 documentation
- DataFrameWriter.parquet: pyspark.sql.DataFrameWriter.parquet - PySpark 3.1.1 documentation Static notebook | Dynamic notebook: See test 1
NEW QUESTION 37
The code block displayed below contains an error. The code block should display the schema of DataFrame transactionsDf. Find the error.
Code block:
transactionsDf.rdd.printSchema
(Correct)D. There is no way to print a schema directly in Spark, since the schema can be printed easily through using print(transactionsDf.columns), so that should be used instead.E. printSchema is a not a method of transactionsDf.rdd. Instead, the schema should be printed via transactionsDf.print_schema().
Answer: C
Explanation:
Explanation
Correct code block:
transactionsDf.printSchema()
This is more of a knowledge question that you should just memorize or look up in the provided documentation during the exam. You can get more info about DataFrame.printSchema() in the documentation (link below). However - it is a plain simple method without any arguments.
One answer points to an alternative of printing the schema: You could also use print(transactionsDf.schema).
This will give you readable, but not nicely formatted, description of the schema.
More info: pyspark.sql.DataFrame.printSchema - PySpark 3.1.1 documentation Static notebook | Dynamic notebook: See test 1
NEW QUESTION 38
Which of the following is a viable way to improve Spark's performance when dealing with large amounts of data, given that there is only a single application running on the cluster?
Answer: C
Explanation:
Explanation
Decrease values for the properties spark.default.parallelism and spark.sql.partitions No, these values need to be increased.
Increase values for the properties spark.sql.parallelism and spark.sql.partitions Wrong, there is no property spark.sql.parallelism.
Increase values for the properties spark.sql.parallelism and spark.sql.shuffle.partitions See above.
Increase values for the properties spark.dynamicAllocation.maxExecutors, spark.default.parallelism, and spark.sql.shuffle.partitions The property spark.dynamicAllocation.maxExecutors is only in effect if dynamic allocation is enabled, using the spark.dynamicAllocation.enabled property. It is disabled by default. Dynamic allocation can be useful when to run multiple applications on the same cluster in parallel. However, in this case there is only a single application running on the cluster, so enabling dynamic allocation would not yield a performance benefit.
More info: Practical Spark Tips For Data Scientists | Experfy.com and Basics of Apache Spark Configuration Settings | by Halil Ertan | Towards Data Science (https://bit.ly/3gA0A6w ,
https://bit.ly/2QxhNTr)
NEW QUESTION 39
Which of the following code blocks displays the 10 rows with the smallest values of column value in DataFrame transactionsDf in a nicely formatted way?
Answer: C
Explanation:
Explanation
show() is the correct method to look for here, since the question specifically asks for displaying the rows in a nicely formatted way. Here is the output of show (only a few rows shown):
+-------------+---------+-----+-------+---------+----+---------------+
|transactionId|predError|value|storeId|productId| f|transactionDate|
+-------------+---------+-----+-------+---------+----+---------------+
| 3| 3| 1| 25| 3|null| 1585824821|
| 5| null| 2| null| 2|null| 1575285427|
| 4| null| 3| 3| 2|null| 1583244275|
+-------------+---------+-----+-------+---------+----+---------------+
With regards to the sorting, specifically in ascending order since the smallest values should be shown first, the following expressions are valid:
- transactionsDf.sort(col("value")) ("ascending" is the default sort direction in the sort method)
- transactionsDf.sort(asc(col("value")))
- transactionsDf.sort(asc("value"))
- transactionsDf.sort(transactionsDf.value.asc())
- transactionsDf.sort(transactionsDf.value)
Also, orderBy is just an alias of sort, so all of these expressions work equally well using orderBy.
Static notebook | Dynamic notebook: See test 1
NEW QUESTION 40
Which is the highest level in Spark's execution hierarchy?
Answer: A
NEW QUESTION 41
......
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