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Download Databricks Certified Associate Developer for Apache Spark 3.0 Exam Exam Dumps

NEW QUESTION 52
Which of the following describes the role of tasks in the Spark execution hierarchy?

• A. Tasks are the second-smallest element in the execution hierarchy.
• B. Within one task, the slots are the unit of work done for each partition of the data.
• C. Stages with narrow dependencies can be grouped into one task.
• D. Tasks are the smallest element in the execution hierarchy.
• E. Tasks with wide dependencies can be grouped into one stage.

Answer: D

Explanation:
Explanation
Stages with narrow dependencies can be grouped into one task.
Wrong, tasks with narrow dependencies can be grouped into one stage.
Tasks with wide dependencies can be grouped into one stage.
Wrong, since a wide transformation causes a shuffle which always marks the boundary of a stage. So, you cannot bundle multiple tasks that have wide dependencies into a stage.
Tasks are the second-smallest element in the execution hierarchy.
No, they are the smallest element in the execution hierarchy.
Within one task, the slots are the unit of work done for each partition of the data.
No, tasks are the unit of work done per partition. Slots help Spark parallelize work. An executor can have multiple slots which enable it to process multiple tasks in parallel.

NEW QUESTION 53
The code block displayed below contains an error. The code block should combine data from DataFrames itemsDf and transactionsDf, showing all rows of DataFrame itemsDf that have a matching value in column itemId with a value in column transactionsId of DataFrame transactionsDf. Find the error.
Code block:
itemsDf.join(itemsDf.itemId==transactionsDf.transactionId)

• A. The join expression is malformed.
• B. The union method should be used instead of join.
• C. The merge method should be used instead of join.
• D. The join statement is incomplete.
• E. The join method is inappropriate.

Answer: D

Explanation:
Explanation
Correct code block:
itemsDf.join(transactionsDf, itemsDf.itemId==transactionsDf.transactionId) The join statement is incomplete.
Correct! If you look at the documentation of DataFrame.join() (linked below), you see that the very first argument of join should be the DataFrame that should be joined with. This first argument is missing in the code block.
The join method is inappropriate.
No. By default, DataFrame.join() uses an inner join. This method is appropriate for the scenario described in the question.
The join expression is malformed.
Incorrect. The join expression itemsDf.itemId==transactionsDf.transactionId is correct syntax.
The merge method should be used instead of join.
False. There is no DataFrame.merge() method in PySpark.
The union method should be used instead of join.
Wrong. DataFrame.union() merges rows, but not columns as requested in the question.
More info: pyspark.sql.DataFrame.join - PySpark 3.1.2 documentation, pyspark.sql.DataFrame.union - PySpark 3.1.2 documentation Static notebook | Dynamic notebook: See test 3

NEW QUESTION 54
Which of the following statements about RDDs is incorrect?

• A. RDDs are immutable.
• B. An RDD consists of a single partition.
• C. The high-level DataFrame API is built on top of the low-level RDD API.
• D. RDD stands for Resilient Distributed Dataset.
• E. RDDs are great for precisely instructing Spark on how to do a query.

Answer: B

Explanation:
Explanation
An RDD consists of a single partition.
Quite the opposite: Spark partitions RDDs and distributes the partitions across multiple nodes.

NEW QUESTION 55
Which of the following statements about lazy evaluation is incorrect?

• A. Predicate pushdown is a feature resulting from lazy evaluation.
• B. Lineages allow Spark to coalesce transformations into stages
• C. Spark will fail a job only during execution, but not during definition.
• D. Accumulators do not change the lazy evaluation model of Spark.
• E. Execution is triggered by transformations.

Answer: E

Explanation:
Explanation
Execution is triggered by transformations.
Correct. Execution is triggered by actions only, not by transformations.
Lineages allow Spark to coalesce transformations into stages.
Incorrect. In Spark, lineage means a recording of transformations. This lineage enables lazy evaluation in Spark.
Predicate pushdown is a feature resulting from lazy evaluation.
Wrong. Predicate pushdown means that, for example, Spark will execute filters as early in the process as possible so that it deals with the least possible amount of data in subsequent transformations, resulting in a performance improvements.
Accumulators do not change the lazy evaluation model of Spark.
Incorrect. In Spark, accumulators are only updated when the query that refers to the is actually executed. In other words, they are not updated if the query is not (yet) executed due to lazy evaluation.
Spark will fail a job only during execution, but not during definition.
Wrong. During definition, due to lazy evaluation, the job is not executed and thus certain errors, for example reading from a non-existing file, cannot be caught. To be caught, the job needs to be executed, for example through an action.

NEW QUESTION 56
Which of the following code blocks reads in the parquet file stored at location filePath, given that all columns in the parquet file contain only whole numbers and are stored in the most appropriate format for this kind of data?

• A. 1.spark.read.schema(
  2. StructType([
  3. StructField("transactionId", IntegerType(), True),
  4. StructField("predError", IntegerType(), True)]
  5. )).format("parquet").load(filePath)
• B. 1.spark.read.schema([
  2. StructField("transactionId", NumberType(), True),
  3. StructField("predError", IntegerType(), True)
  4. ]).load(filePath)
• C. 1.spark.read.schema(
  2. StructType([
  3. StructField("transactionId", StringType(), True),
  4. StructField("predError", IntegerType(), True)]
  5. )).parquet(filePath)
• D. 1.spark.read.schema(
  2. StructType(
  3. StructField("transactionId", IntegerType(), True),
  4. StructField("predError", IntegerType(), True)
  5. )).load(filePath)
• E. 1.spark.read.schema([
  2. StructField("transactionId", IntegerType(), True),
  3. StructField("predError", IntegerType(), True)
  4. ]).load(filePath, format="parquet")

Answer: A

Explanation:
Explanation
The schema passed into schema should be of type StructType or a string, so all entries in which a list is passed are incorrect.
In addition, since all numbers are whole numbers, the IntegerType() data type is the correct option here.
NumberType() is not a valid data type and StringType() would fail, since the parquet file is stored in the "most appropriate format for this kind of data", meaning that it is most likely an IntegerType, and Spark does not convert data types if a schema is provided.
Also note that StructType accepts only a single argument (a list of StructFields). So, passing multiple arguments is invalid.
Finally, Spark needs to know which format the file is in. However, all of the options listed are valid here, since Spark assumes parquet as a default when no file format is specifically passed.
More info: pyspark.sql.DataFrameReader.schema - PySpark 3.1.2 documentation and StructType - PySpark 3.1.2 documentation

NEW QUESTION 57
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