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NEW QUESTION 37
In which order should the code blocks shown below be run in order to return the number of records that are not empty in column value in the DataFrame resulting from an inner join of DataFrame transactionsDf and itemsDf on columns productId and itemId, respectively?
1. .filter(~isnull(col('value')))
2. .count()
3. transactionsDf.join(itemsDf, col("transactionsDf.productId")==col("itemsDf.itemId"))
4. transactionsDf.join(itemsDf, transactionsDf.productId==itemsDf.itemId, how='inner')
5. .filter(col('value').isnotnull())
6. .sum(col('value'))
Answer: D
Explanation:
Explanation
Correct code block:
transactionsDf.join(itemsDf, transactionsDf.productId==itemsDf.itemId,
how='inner').filter(~isnull(col('value'))).count()
Expressions col("transactionsDf.productId") and col("itemsDf.itemId") are invalid. col() does not accept the name of a DataFrame, only column names.
Static notebook | Dynamic notebook: See test 2
NEW QUESTION 38
Which of the following code blocks returns only rows from DataFrame transactionsDf in which values in column productId are unique?
Answer: B
Explanation:
Explanation
Although the question suggests using a method called unique() here, that method does not actually exist in PySpark. In PySpark, it is called distinct(). But then, this method is not the right one to use here, since with distinct() we could filter out unique values in a specific column.
However, we want to return the entire rows here. So the trick is to use dropDuplicates with the subset keyword parameter. In the documentation for dropDuplicates, the examples show that subset should be used with a list. And this is exactly the key to solving this question: The productId column needs to be fed into the subset argument in a list, even though it is just a single column.
More info: pyspark.sql.DataFrame.dropDuplicates - PySpark 3.1.1 documentation Static notebook | Dynamic notebook: See test 1
NEW QUESTION 39
The code block displayed below contains an error. The code block should use Python method find_most_freq_letter to find the letter present most in column itemName of DataFrame itemsDf and return it in a new column most_frequent_letter. Find the error.
Code block:
1. find_most_freq_letter_udf = udf(find_most_freq_letter)
2. itemsDf.withColumn("most_frequent_letter", find_most_freq_letter("itemName"))
Answer: E
Explanation:
Explanation
Correct code block:
find_most_freq_letter_udf = udf(find_most_frequent_letter)
itemsDf.withColumn("most_frequent_letter", find_most_freq_letter_udf("itemName")) Spark should use the previously registered find_most_freq_letter_udf method here - but it is not doing that in the original codeblock. There, it just uses the non-UDF version of the Python method.
Note that typically, we would have to specify a return type for udf(). Except in this case, since the default return type for udf() is a string which is what we are expecting here. If we wanted to return an integer variable instead, we would have to register the Python function as UDF using find_most_freq_letter_udf = udf(find_most_freq_letter, IntegerType()).
More info: pyspark.sql.functions.udf - PySpark 3.1.1 documentation
NEW QUESTION 40
Which of the following is one of the big performance advantages that Spark has over Hadoop?
Answer: B
Explanation:
Explanation
Spark achieves great performance by storing data in the DAG format, whereas Hadoop can only use parquet files.
Wrong, there is no "DAG format". DAG stands for "directed acyclic graph". The DAG is a means of representing computational steps in Spark. However, it is true that Hadoop does not use a DAG.
The introduction of the DAG in Spark was a result of the limitation of Hadoop's map reduce framework in which data had to be written to and read from disk continuously.
Graph DAG in Apache Spark - DataFlair
Spark achieves great performance by storing data in the HDFS format, whereas Hadoop can only use parquet files.
No. Spark can certainly store data in HDFS (as well as other formats), but this is not a key performance advantage over Hadoop. Hadoop can use multiple file formats, not only parquet.
Spark achieves higher resiliency for queries since, different from Hadoop, it can be deployed on Kubernetes.
No, resiliency is not asked for in the question. The question is about performance improvements.
Both Hadoop and Spark can be deployed on Kubernetes.
Spark achieves performance gains for developers by extending Hadoop's DataFrames with a user-friendly API.
No. DataFrames are a concept in Spark, but not in Hadoop.
NEW QUESTION 41
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