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NEW QUESTION 22
Which of the following code blocks reads in the two-partition parquet file stored at filePath, making sure all columns are included exactly once even though each partition has a different schema?
Schema of first partition:
1.root
2. |-- transactionId: integer (nullable = true)
3. |-- predError: integer (nullable = true)
4. |-- value: integer (nullable = true)
5. |-- storeId: integer (nullable = true)
6. |-- productId: integer (nullable = true)
7. |-- f: integer (nullable = true)
Schema of second partition:
1.root
2. |-- transactionId: integer (nullable = true)
3. |-- predError: integer (nullable = true)
4. |-- value: integer (nullable = true)
5. |-- storeId: integer (nullable = true)
6. |-- rollId: integer (nullable = true)
7. |-- f: integer (nullable = true)
8. |-- tax_id: integer (nullable = false)

• A. spark.read.parquet(filePath, mergeSchema='y')
• B. 1.nx = 0
  2.for file in dbutils.fs.ls(filePath):
  3. if not file.name.endswith(".parquet"):
  4. continue
  5. df_temp = spark.read.parquet(file.path)
  6. if nx == 0:
  7. df = df_temp
  8. else:
  9. df = df.union(df_temp)
  10. nx = nx+1
  11.df
• C. spark.read.option("mergeSchema", "true").parquet(filePath)
• D. 1.nx = 0
  2.for file in dbutils.fs.ls(filePath):
  3. if not file.name.endswith(".parquet"):
  4. continue
  5. df_temp = spark.read.parquet(file.path)
  6. if nx == 0:
  7. df = df_temp
  8. else:
  9. df = df.join(df_temp, how="outer")
  10. nx = nx+1
  11.df
• E. spark.read.parquet(filePath)

Answer: C

Explanation:
Explanation
This is a very tricky question and involves both knowledge about merging as well as schemas when reading parquet files.
spark.read.option("mergeSchema", "true").parquet(filePath)
Correct. Spark's DataFrameReader's mergeSchema option will work well here, since columns that appear in both partitions have matching data types. Note that mergeSchema would fail if one or more columns with the same name that appear in both partitions would have different data types.
spark.read.parquet(filePath)
Incorrect. While this would read in data from both partitions, only the schema in the parquet file that is read in first would be considered, so some columns that appear only in the second partition (e.g. tax_id) would be lost.
nx = 0
for file in dbutils.fs.ls(filePath):
if not file.name.endswith(".parquet"):
continue
df_temp = spark.read.parquet(file.path)
if nx == 0:
df = df_temp
else:
df = df.union(df_temp)
nx = nx+1
df
Wrong. The key idea of this solution is the DataFrame.union() command. While this command merges all data, it requires that both partitions have the exact same number of columns with identical data types.
spark.read.parquet(filePath, mergeSchema="y")
False. While using the mergeSchema option is the correct way to solve this problem and it can even be called with DataFrameReader.parquet() as in the code block, it accepts the value True as a boolean or string variable. But 'y' is not a valid option.
nx = 0
for file in dbutils.fs.ls(filePath):
if not file.name.endswith(".parquet"):
continue
df_temp = spark.read.parquet(file.path)
if nx == 0:
df = df_temp
else:
df = df.join(df_temp, how="outer")
nx = nx+1
df
No. This provokes a full outer join. While the resulting DataFrame will have all columns of both partitions, columns that appear in both partitions will be duplicated - the question says all columns that are included in the partitions should appear exactly once.
More info: Merging different schemas in Apache Spark | by Thiago Cordon | Data Arena | Medium Static notebook | Dynamic notebook: See test 3

NEW QUESTION 23
Which of the following statements about RDDs is incorrect?

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

Answer: A

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

NEW QUESTION 24
Which of the following statements about executors is correct, assuming that one can consider each of the JVMs working as executors as a pool of task execution slots?

• A. There must be more slots than tasks.
• B. Slot is another name for executor.
• C. An executor runs on a single core.
• D. Tasks run in parallel via slots.
• E. There must be less executors than tasks.

Answer: D

Explanation:
Explanation
Tasks run in parallel via slots.
Correct. Given the assumption, an executor then has one or more "slots", defined by the equation spark.executor.cores / spark.task.cpus. With the executor's resources divided into slots, each task takes up a slot and multiple tasks can be executed in parallel.
Slot is another name for executor.
No, a slot is part of an executor.
An executor runs on a single core.
No, an executor can occupy multiple cores. This is set by the spark.executor.cores option.
There must be more slots than tasks.
No. Slots just process tasks. One could imagine a scenario where there was just a single slot for multiple tasks, processing one task at a time. Granted - this is the opposite of what Spark should be used for, which is distributed data processing over multiple cores and machines, performing many tasks in parallel.
There must be less executors than tasks.
No, there is no such requirement.
More info: Spark Architecture | Distributed Systems Architecture (https://bit.ly/3x4MZZt)

NEW QUESTION 25
Which of the following code blocks uses a schema fileSchema to read a parquet file at location filePath into a DataFrame?

• A. spark.read.schema(fileSchema).format("parquet").load(filePath)
• B. spark.read().schema(fileSchema).parquet(filePath)
• C. spark.read.schema("fileSchema").format("parquet").load(filePath)
• D. spark.read.schema(fileSchema).open(filePath)
• E. spark.read().schema(fileSchema).format(parquet).load(filePath)

Answer: A

Explanation:
Explanation
Pay attention here to which variables are quoted. fileSchema is a variable and thus should not be in quotes.
parquet is not a variable and therefore should be in quotes.
SparkSession.read (here referenced as spark.read) returns a DataFrameReader which all subsequent calls reference - the DataFrameReader is not callable, so you should not use parentheses here.
Finally, there is no open method in PySpark. The method name is load.
Static notebook | Dynamic notebook: See test 1

NEW QUESTION 26
......