Download Certified Associate Developer for Apache Spark.CERTIFIED-ASSOCIATE-DEVELOPER-FOR-APACHE-SPARK.ExamTopics.2026-01-04.186q.vcex

Vendor: Databricks
Exam Code: CERTIFIED-ASSOCIATE-DEVELOPER-FOR-APACHE-SPARK
Exam Name: Certified Associate Developer for Apache Spark
Date: Jan 04, 2026
File Size: 2 MB
Downloads: 2
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Demo Questions

Question 1
Which of the following statements about Spark’s stability is incorrect?
  1. Spark is designed to support the loss of any set of worker nodes.
  2. Spark will rerun any failed tasks due to failed worker nodes.
  3. Spark will recompute data cached on failed worker nodes.
  4. Spark will spill data to disk if it does not fit in memory.
  5. Spark will reassign the driver to a worker node if the driver’s node fails.
Correct answer: E
Explanation:
E: 19 - Mosted
E: 19 - Mosted
Question 2
Which of the following operations fails to return a DataFrame with no duplicate rows?
  1. DataFrame.dropDuplicates()
  2. DataFrame.distinct()
  3. DataFrame.drop_duplicates()
  4. DataFrame.drop_duplicates(subset = None)
  5. DataFrame.drop_duplicates(subset = "all")
Correct answer: E
Explanation:
B: 1E: 24 - Mosted
B: 1E: 24 - Mosted
Question 3
Of the following situations, in which will it be most advantageous to store DataFrame df at the MEMORY_AND_DISK storage level rather than the MEMORY_ONLY storage level?
  1. When all of the computed data in DataFrame df can fit into memory.
  2. When the memory is full and it’s faster to recompute all the data in DataFrame df rather than read it from disk.
  3. When it’s faster to recompute all the data in DataFrame df that cannot fit into memory based on its logical plan rather than read it from disk.
  4. When it’s faster to read all the computed data in DataFrame df that cannot fit into memory from disk rather than recompute it based on its logical plan.
  5. The storage level MENORY_ONLY will always be more advantageous because it’s faster to read data from memory than it is to read data from disk.
Correct answer: D
Explanation:
D: 4 - Mosted
D: 4 - Mosted
Question 4
Which of the following cluster configurations is most likely to experience an out-of-memory error in response to data skew in a single partition?
Note: each configuration has roughly the same compute power using 100 GB of RAM and 200 cores.
  1. Scenario #4
  2. Scenario #5
  3. Scenario #6
  4. More information is needed to determine an answer.
  5. Scenario #1
Correct answer: C
Explanation:
C: 16 - MostedE: 2
C: 16 - MostedE: 2
Question 5
Which of the following describes the relationship between nodes and executors?
  1. Executors and nodes are not related.
  2. Anode is a processing engine running on an executor.
  3. An executor is a processing engine running on a node.
  4. There are always the same number of executors and nodes.
  5. There are always more nodes than executors.
Correct answer: C
Explanation:
C: 16 - Mosted
C: 16 - Mosted
Question 6
Which of the following will occur if there are more slots than there are tasks?
  1. The Spark job will likely not run as efficiently as possible.
  2. The Spark application will fail – there must be at least as many tasks as there are slots.
  3. Some executors will shut down and allocate all slots on larger executors first.
  4. More tasks will be automatically generated to ensure all slots are being used.
  5. The Spark job will use just one single slot to perform all tasks.
Correct answer: A
Explanation:
A: 15 - MostedE: 2
A: 15 - MostedE: 2
Question 7
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. storesDF.withColumn("storeDescription", regexp_replace(col("storeDescription"), "^Description: "))
  2. storesDF.withColumn("storeDescription", col("storeDescription").regexp_replace("^Description: ", ""))
  3. storesDF.withColumn("storeDescription", regexp_extract(col("storeDescription"), "^Description: ", ""))
  4. storesDF.withColumn("storeDescription", regexp_replace("storeDescription", "^Description: ", ""))
  5. storesDF.withColumn("storeDescription", regexp_replace(col("storeDescription"), "^Description: ", ""))
Correct answer: E
Explanation:
A: 2D: 5E: 9 - Mosted
A: 2D: 5E: 9 - Mosted
Question 8
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. 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. The na.fill() operation does not work and should be replaced by the dropna() operation.
  3. he argument to the subset parameter of fill() should be a the numerical position of the column rather than a Column object.
  4. The na.fill() operation does not work and should be replaced by the nafill() operation.
  5. The na.fill() operation does not work and should be replaced by the fillna() operation.
Correct answer: A
Explanation:
A: 5 - MostedE: 1
A: 5 - MostedE: 1
Question 9
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. storesDF.agg(approx_count_distinct(col("division")).alias("divisionDistinct"))
  2. storesDF.agg(approx_count_distinct(col("division"), 0.01).alias("divisionDistinct"))
  3. storesDF.agg(approx_count_distinct(col("division"), 0.15).alias("divisionDistinct"))
  4. storesDF.agg(approx_count_distinct(col("division"), 0.0).alias("divisionDistinct"))
  5. storesDF.agg(approx_count_distinct(col("division"), 0.05).alias("divisionDistinct"))
Correct answer: C
Explanation:
B: 5C: 12 - Mosted
B: 5C: 12 - Mosted
Question 10
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. (storesDF.withColumn("storeValueCategory", split(col("storeCategory"), "_")[1])
    .withColumn("storeSizeCategory", split(col("storeCategory"), "_")[2]))
  2. (storesDF.withColumn("storeValueCategory", col("storeCategory").split("_")[0])
    .withColumn("storeSizeCategory", col("storeCategory").split("_")[1]))
  3. (storesDF.withColumn("storeValueCategory", split(col("storeCategory"), "_")[0])
    .withColumn("storeSizeCategory", split(col("storeCategory"), "_")[1]))
  4. (storesDF.withColumn("storeValueCategory", split("storeCategory", "_")[0])
    .withColumn("storeSizeCategory", split("storeCategory", "_")[1]))
  5. (storesDF.withColumn("storeValueCategory", col("storeCategory").split("_")[1])
    .withColumn("storeSizeCategory", col("storeCategory").split("_")[2]))
Correct answer: C
Explanation:
C: 6 - Mosted
C: 6 - Mosted
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