Nest 0 Cart
Nest Account
Popular Exams
All Categories...
Nest
Nest 0
Contact
Log In / Sign Up
Copyright 2024 © store. All rights reserved.
Home Databricks Certified Associate Developer for Apache Spark

Databricks Certified Associate Developer for Apache Spark Exam Practice Questions (P. 3)

  • Full Access (343 questions)
  • Six months of Premium Access
  • Access to one million comments
  • Seamless ChatGPT Integration
  • Ability to download PDF files
  • Anki Flashcard files for revision
  • No Captcha & No AdSense
  • Advanced Exam Configuration
Get Contributor Access
Question #21
Which of the following code blocks returns a DataFrame containing only the rows from DataFrame storesDF where the value in column sqft is less than or equal to 25,000 OR the value in column customerSatisfaction is greater than or equal to 30?
  1. A
    storesDF.filter(col("sqft") <= 25000 | col("customerSatisfaction") >= 30)
  2. B
    storesDF.filter(col("sqft") <= 25000 or col("customerSatisfaction") >= 30)
  3. C
    storesDF.filter(sqft <= 25000 or customerSatisfaction >= 30)
  4. D
    storesDF.filter(col(sqft) <= 25000 | col(customerSatisfaction) >= 30)
  5. E
    storesDF.filter((col("sqft") <= 25000) | (col("customerSatisfaction") >= 30))

Correct Answer:
E

Question #22
Which of the following code blocks returns a new DataFrame from DataFrame storesDF where column storeId is of the type string?
  1. A
    storesDF.withColumn("storeId, cast(col("storeId"), StringType()))
  2. B
    storesDF.withColumn("storeId, col("storeId").cast(StringType()))
  3. C
    storesDF.withColumn("storeId, cast(storeId).as(StringType)
  4. D
    storesDF.withColumn("storeId, col(storeId).cast(StringType)
  5. E
    storesDF.withColumn("storeId, cast("storeId").as(StringType()))

Correct Answer:
B

Question #23
Which of the following code blocks returns a new DataFrame with a new column employeesPerSqft that is the quotient of column numberOfEmployees and column sqft, both of which are from DataFrame storesDF? Note that column employeesPerSqft is not in the original DataFrame storesDF.
  1. A
    storesDF.withColumn("employeesPerSqft", col("numberOfEmployees") / col("sqft"))
  2. B
    storesDF.withColumn("employeesPerSqft", "numberOfEmployees" / "sqft")
  3. C
    storesDF.select("employeesPerSqft", "numberOfEmployees" / "sqft")
  4. D
    storesDF.select("employeesPerSqft", col("numberOfEmployees") / col("sqft"))
  5. E
    storesDF.withColumn(col("employeesPerSqft"), col("numberOfEmployees") / col("sqft"))

Correct Answer:
A

Question #24
The code block shown below should return a new DataFrame from DataFrame storesDF where column modality is the constant string "PHYSICAL", Assume DataFrame storesDF is the only defined language variable. Choose the response that correctly fills in the numbered blanks within the code block to complete this task.
Code block:
storesDF. _1_(_2_,_3_(_4_))
  1. A
    1. withColumn
    2. "modality"
    3. col
    4. "PHYSICAL"
  2. B
    1. withColumn
    2. "modality"
    3. lit
    4. PHYSICAL
  3. C
    1. withColumn
    2. "modality"
    3. lit
    4. "PHYSICAL"
  4. D
    1. withColumn
    2. "modality"
    3. SrtringType
    4. "PHYSICAL"
  5. E
    1. newColumn
    2. modality
    3. SrtringType
    4. PHYSICAL

Correct Answer:
C

Question #25
Which of the following code blocks returns a DataFrame where column storeCategory from DataFrame storesDF is split at the underscore character into column storeValueCategory and column storeSizeCategory?
A sample of DataFrame storesDF is displayed below:
  1. A
    (storesDF.withColumn("storeValueCategory", split(col("storeCategory"), "_")[1])
    .withColumn("storeSizeCategory", split(col("storeCategory"), "_")[2]))
  2. B
    (storesDF.withColumn("storeValueCategory", col("storeCategory").split("_")[0])
    .withColumn("storeSizeCategory", col("storeCategory").split("_")[1]))
  3. C
    (storesDF.withColumn("storeValueCategory", split(col("storeCategory"), "_")[0])
    .withColumn("storeSizeCategory", split(col("storeCategory"), "_")[1]))
  4. D
    (storesDF.withColumn("storeValueCategory", split("storeCategory", "_")[0])
    .withColumn("storeSizeCategory", split("storeCategory", "_")[1]))
  5. E
    (storesDF.withColumn("storeValueCategory", col("storeCategory").split("_")[1])
    .withColumn("storeSizeCategory", col("storeCategory").split("_")[2]))

Correct Answer:
C

Question #26
Which of the following code blocks returns a new DataFrame where column productCategories only has one word per row, resulting in a DataFrame with many more rows than DataFrame storesDF?
A sample of storesDF is displayed below:
  1. A
    storesDF.withColumn("productCategories", explode(col("productCategories")))
  2. B
    storesDF.withColumn("productCategories", split(col("productCategories")))
  3. C
    storesDF.withColumn("productCategories", col("productCategories").explode())
  4. D
    storesDF.withColumn("productCategories", col("productCategories").split())
  5. E
    storesDF.withColumn("productCategories", explode("productCategories"))

Correct Answer:
A

Question #27
Which of the following code blocks returns a new DataFrame with column storeDescription where the pattern "Description: " has been removed from the beginning of column storeDescription in DataFrame storesDF?
A sample of DataFrame storesDF is below:
  1. A
    storesDF.withColumn("storeDescription", regexp_replace(col("storeDescription"), "^Description: "))
  2. B
    storesDF.withColumn("storeDescription", col("storeDescription").regexp_replace("^Description: ", ""))
  3. C
    storesDF.withColumn("storeDescription", regexp_extract(col("storeDescription"), "^Description: ", ""))
  4. D
    storesDF.withColumn("storeDescription", regexp_replace("storeDescription", "^Description: ", ""))
  5. E
    storesDF.withColumn("storeDescription", regexp_replace(col("storeDescription"), "^Description: ", ""))

Correct Answer:
E

Question #28
Which of the following code blocks returns a new DataFrame where column division from DataFrame storesDF has been replaced and renamed to column state and column managerName from DataFrame storesDF has been replaced and renamed to column managerFullName?
  1. A
    (storesDF.withColumnRenamed(["division", "state"], ["managerName", "managerFullName"])
  2. B
    (storesDF.withColumn("state", col("division"))
    .withColumn("managerFullName", col("managerName")))
  3. C
    (storesDF.withColumn("state", "division")
    .withColumn("managerFullName", "managerName"))
  4. D
    (storesDF.withColumnRenamed("state", "division")
    .withColumnRenamed("managerFullName", "managerName"))
  5. E
    (storesDF.withColumnRenamed("division", "state")
    .withColumnRenamed("managerName", "managerFullName"))

Correct Answer:
E

Question #29
The code block shown contains an error. The code block is intended to return a new DataFrame where column sqft from DataFrame storesDF has had its missing values replaced with the value 30,000. Identify the error.
A sample of DataFrame storesDF is displayed below:

Code block:
storesDF.na.fill(30000, col("sqft"))
  1. A
    The argument to the subset parameter of fill() should be a string column name or a list of string column names rather than a Column object.
  2. B
    The na.fill() operation does not work and should be replaced by the dropna() operation.
  3. C
    he argument to the subset parameter of fill() should be a the numerical position of the column rather than a Column object.
  4. D
    The na.fill() operation does not work and should be replaced by the nafill() operation.
  5. E
    The na.fill() operation does not work and should be replaced by the fillna() operation.

Correct Answer:
A

Question #30
Which of the following operations fails to return a DataFrame with no duplicate rows?
  1. A
    DataFrame.dropDuplicates()
  2. B
    DataFrame.distinct()
  3. C
    DataFrame.drop_duplicates()
  4. D
    DataFrame.drop_duplicates(subset = None)
  5. E
    DataFrame.drop_duplicates(subset = "all")

Correct Answer: E ?️

Previous Questions Next Questions

All Pages