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NEW QUESTION 21
Which of the following code blocks returns all unique values of column storeId in DataFrame transactionsDf?
- A. transactionsDf.select("storeId").distinct()
(Correct) - B. transactionsDf.select(col("storeId").distinct())
- C. transactionsDf.filter("storeId").distinct()
- D. transactionsDf["storeId"].distinct()
- E. transactionsDf.distinct("storeId")
Answer: A
Explanation:
Explanation
distinct() is a method of a DataFrame. Knowing this, or recognizing this from the documentation, is the key to solving this question.
More info: pyspark.sql.DataFrame.distinct - PySpark 3.1.2 documentation Static notebook | Dynamic notebook: See test 2
NEW QUESTION 22
Which of the following describes the conversion of a computational query into an execution plan in Spark?
- A. Spark uses the catalog to resolve the optimized logical plan.
- B. Depending on whether DataFrame API or SQL API are used, the physical plan may differ.
- C. The executed physical plan depends on a cost optimization from a previous stage.
- D. The catalog assigns specific resources to the optimized memory plan.
- E. The catalog assigns specific resources to the physical plan.
Answer: C
Explanation:
Explanation
The executed physical plan depends on a cost optimization from a previous stage.
Correct! Spark considers multiple physical plans on which it performs a cost analysis and selects the final physical plan in accordance with the lowest-cost outcome of that analysis. That final physical plan is then executed by Spark.
Spark uses the catalog to resolve the optimized logical plan.
No. Spark uses the catalog to resolve the unresolved logical plan, but not the optimized logical plan. Once the unresolved logical plan is resolved, it is then optimized using the Catalyst Optimizer.
The optimized logical plan is the input for physical planning.
The catalog assigns specific resources to the physical plan.
No. The catalog stores metadata, such as a list of names of columns, data types, functions, and databases.
Spark consults the catalog for resolving the references in a logical plan at the beginning of the conversion of the query into an execution plan. The result is then an optimized logical plan.
Depending on whether DataFrame API or SQL API are used, the physical plan may differ.
Wrong - the physical plan is independent of which API was used. And this is one of the great strengths of Spark!
The catalog assigns specific resources to the optimized memory plan.
There is no specific "memory plan" on the journey of a Spark computation.
More info: Spark's Logical and Physical plans ... When, Why, How and Beyond. | by Laurent Leturgez | datalex | Medium
NEW QUESTION 23
Which of the following code blocks displays various aggregated statistics of all columns in DataFrame transactionsDf, including the standard deviation and minimum of values in each column?
- A. transactionsDf.summary()
- B. transactionsDf.summary().show()
- C. transactionsDf.agg("count", "mean", "stddev", "25%", "50%", "75%", "min").show()
- D. transactionsDf.agg("count", "mean", "stddev", "25%", "50%", "75%", "min")
- E. transactionsDf.summary("count", "mean", "stddev", "25%", "50%", "75%", "max").show()
Answer: B
Explanation:
Explanation
The DataFrame.summary() command is very practical for quickly calculating statistics of a DataFrame. You need to call .show() to display the results of the calculation. By default, the command calculates various statistics (see documentation linked below), including standard deviation and minimum.
Note that the answer that lists many options in the summary() parentheses does not include the minimum, which is asked for in the question.
Answer options that include agg() do not work here as shown, since DataFrame.agg() expects more complex, column-specific instructions on how to aggregate values.
More info:
- pyspark.sql.DataFrame.summary - PySpark 3.1.2 documentation
- pyspark.sql.DataFrame.agg - PySpark 3.1.2 documentation
Static notebook | Dynamic notebook: See test 3
NEW QUESTION 24
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