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Home Databricks Certified Associate Developer for Apache Spark

Databricks Certified Associate Developer for Apache Spark Exam Practice Questions (P. 4)

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Question #31
Which of the following code blocks will most quickly return an approximation for the number of distinct values in column division in DataFrame storesDF?
  1. A
    storesDF.agg(approx_count_distinct(col("division")).alias("divisionDistinct"))
  2. B
    storesDF.agg(approx_count_distinct(col("division"), 0.01).alias("divisionDistinct"))
  3. C
    storesDF.agg(approx_count_distinct(col("division"), 0.15).alias("divisionDistinct"))
  4. D
    storesDF.agg(approx_count_distinct(col("division"), 0.0).alias("divisionDistinct"))
  5. E
    storesDF.agg(approx_count_distinct(col("division"), 0.05).alias("divisionDistinct"))

Correct Answer: C ?️

Question #32
The code block shown below contains an error. The code block is intended to return a new DataFrame with the mean of column sqft from DataFrame storesDF in column sqftMean. Identify the error.
Code block:
storesDF.agg(mean("sqft").alias("sqftMean"))
  1. A
    The argument to the mean() operation should be a Column abject rather than a string column name.
  2. B
    The argument to the mean() operation should not be quoted.
  3. C
    The mean() operation is not a standalone function – it’s a method of the Column object.
  4. D
    The agg() operation is not appropriate here – the withColumn() operation should be used instead.
  5. E
    The only way to compute a mean of a column is with the mean() method from a DataFrame.

Correct Answer:
A

Question #33
Which of the following operations can be used to return the number of rows in a DataFrame?
  1. A
    DataFrame.numberOfRows()
  2. B
    DataFrame.n()
  3. C
    DataFrame.sum()
  4. D
    DataFrame.count()
  5. E
    DataFrame.countDistinct()

Correct Answer:
D

Question #34
Which of the following operations returns a GroupedData object?
  1. A
    DataFrame.GroupBy()
  2. B
    DataFrame.cubed()
  3. C
    DataFrame.group()
  4. D
    DataFrame.groupBy()
  5. E
    DataFrame.grouping_id()

Correct Answer:
D

Question #35
Which of the following code blocks returns a collection of summary statistics for all columns in
DataFrame storesDF?
  1. A
    storesDF.summary("mean")
  2. B
    storesDF.describe(all = True)
  3. C
    storesDF.describe("all")
  4. D
    storesDF.summary("all")
  5. E
    storesDF.describe()

Correct Answer:
E

Question #36
Which of the following code blocks fails to return a DataFrame reverse sorted alphabetically based on column division?
  1. A
    storesDF.orderBy("division", ascending – False)
  2. B
    storesDF.orderBy(["division"], ascending = [0])
  3. C
    storesDF.orderBy(col("division").asc())
  4. D
    storesDF.sort("division", ascending – False)
  5. E
    storesDF.sort(desc("division"))

Correct Answer:
C

Question #37
Which of the following code blocks returns a 15 percent sample of rows from DataFrame storesDF without replacement?
  1. A
    storesDF.sample(fraction = 0.10)
  2. B
    storesDF.sampleBy(fraction = 0.15)
  3. C
    storesDF.sample(True, fraction = 0.10)
  4. D
    storesDF.sample()
  5. E
    storesDF.sample(fraction = 0.15)

Correct Answer:
E

Question #38
Which of the following code blocks returns all the rows from DataFrame storesDF?
  1. A
    storesDF.head()
  2. B
    storesDF.collect()
  3. C
    storesDF.count()
  4. D
    storesDF.take()
  5. E
    storesDF.show()

Correct Answer:
B

Question #39
Which of the following code blocks applies the function assessPerformance() to each row of DataFrame storesDF?
  1. A
    [assessPerformance(row) for row in storesDF.take(3)]
  2. B
    [assessPerformance() for row in storesDF]
  3. C
    storesDF.collect().apply(lambda: assessPerformance)
  4. D
    [assessPerformance(row) for row in storesDF.collect()]
  5. E
    [assessPerformance(row) for row in storesDF]

Correct Answer:
D

Question #40
The code block shown below contains an error. The code block is intended to print the schema of DataFrame storesDF. Identify the error.
Code block:
storesDF.printSchema
  1. A
    There is no printSchema member of DataFrame – schema and the print() function should be used instead.
  2. B
    The entire line needs to be a string – it should be wrapped by str().
  3. C
    There is no printSchema member of DataFrame – the getSchema() operation should be used instead.
  4. D
    There is no printSchema member of DataFrame – the schema() operation should be used instead.
  5. E
    The printSchema member of DataFrame is an operation and needs to be followed by parentheses.

Correct Answer:
E

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