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下載Databricks Certified Associate Developer for Apache Spark 3.0 Exam考試題庫
NEW QUESTION 49
The code block displayed below contains an error. The code block should produce a DataFrame with color as the only column and three rows with color values of red, blue, and green, respectively.
Find the error.
Code block:
1.spark.createDataFrame([("red",), ("blue",), ("green",)], "color")
Instead of calling spark.createDataFrame, just DataFrame should be called.
- A. The commas in the tuples with the colors should be eliminated.
- B. The "color" expression needs to be wrapped in brackets, so it reads ["color"].
- C. The colors red, blue, and green should be expressed as a simple Python list, and not a list of tuples.
- D. Instead of color, a data type should be specified.
Answer: B
Explanation:
Explanation
Correct code block:
spark.createDataFrame([("red",), ("blue",), ("green",)], ["color"])
The createDataFrame syntax is not exactly straightforward, but luckily the documentation (linked below) provides several examples on how to use it. It also shows an example very similar to the code block presented here which should help you answer this question correctly.
More info: pyspark.sql.SparkSession.createDataFrame - PySpark 3.1.2 documentation Static notebook | Dynamic notebook: See test 2
NEW QUESTION 50
In which order should the code blocks shown below be run in order to assign articlesDf a DataFrame that lists all items in column attributes ordered by the number of times these items occur, from most to least often?
Sample of DataFrame articlesDf:
1.+------+-----------------------------+-------------------+
2.|itemId|attributes |supplier |
3.+------+-----------------------------+-------------------+
4.|1 |[blue, winter, cozy] |Sports Company Inc.|
5.|2 |[red, summer, fresh, cooling]|YetiX |
6.|3 |[green, summer, travel] |Sports Company Inc.|
7.+------+-----------------------------+-------------------+
- A. 2, 5, 3
- B. 1. articlesDf = articlesDf.groupby("col")
2. articlesDf = articlesDf.select(explode(col("attributes")))
3. articlesDf = articlesDf.orderBy("count").select("col")
4. articlesDf = articlesDf.sort("count",ascending=False).select("col")
5. articlesDf = articlesDf.groupby("col").count() - C. 2, 5, 4
- D. 4, 5
- E. 5, 2
- F. 2, 3, 4
Answer: F
Explanation:
Explanation
Correct code block:
articlesDf = articlesDf.select(explode(col('attributes')))
articlesDf = articlesDf.groupby('col').count()
articlesDf = articlesDf.sort('count',ascending=False).select('col')
Output of correct code block:
+-------+
| col|
+-------+
| summer|
| winter|
| blue|
| cozy|
| travel|
| fresh|
| red|
|cooling|
| green|
+-------+
Static notebook | Dynamic notebook: See test 2
NEW QUESTION 51
Which of the following code blocks reads in the JSON file stored at filePath as a DataFrame?
- A. spark.read().json(filePath)
- B. spark.read.json(filePath)
- C. spark.read.path(filePath, source="json")
- D. spark.read().path(filePath)
- E. spark.read.path(filePath)
Answer: B
Explanation:
Explanation
spark.read.json(filePath)
Correct. spark.read accesses Spark's DataFrameReader. Then, Spark identifies the file type to be read as JSON type by passing filePath into the DataFrameReader.json() method.
spark.read.path(filePath)
Incorrect. Spark's DataFrameReader does not have a path method. A universal way to read in files is provided by the DataFrameReader.load() method (link below).
spark.read.path(filePath, source="json")
Wrong. A DataFrameReader.path() method does not exist (see above).
spark.read().json(filePath)
Incorrect. spark.read is a way to access Spark's DataFrameReader. However, the DataFrameReader is not callable, so calling it via spark.read() will fail.
spark.read().path(filePath)
No, Spark's DataFrameReader is not callable (see above).
More info: pyspark.sql.DataFrameReader.json - PySpark 3.1.2 documentation, pyspark.sql.DataFrameReader.load - PySpark 3.1.2 documentation Static notebook | Dynamic notebook: See test 3
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. Tasks are the smallest element in the execution hierarchy.
- D. Tasks with wide dependencies can be grouped into one stage.
- E. Stages with narrow dependencies can be grouped into one task.
Answer: C
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
......
