Associate-Developer-Apache-Spark Dumps (2026) Prepare Your Exam With 179 Questions [Q102-Q116]

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Associate-Developer-Apache-Spark Dumps (2026) Prepare Your Exam With 179 Questions

New Associate-Developer-Apache-Spark Dumps – Real Databricks Exam Questions

Databricks Associate-Developer-Apache-Spark exam is a challenging exam that requires a significant amount of preparation. Candidates should have a solid understanding of Apache Spark and its ecosystem, as well as experience working with big data. Preparation for the exam can include taking online courses, attending workshops, and working on hands-on projects. Passing the exam demonstrates a candidate’s expertise in Apache Spark and can help them advance their career in the field of big data.

Databricks Certified Associate Developer for Apache Spark 3.0 certification is a great way for developers to demonstrate their expertise in Spark and Databricks. Databricks Certified Associate Developer for Apache Spark 3.0 Exam certification is recognized globally and is a valuable asset for developers who are looking to advance their careers in the field of big data and analytics. Databricks Certified Associate Developer for Apache Spark 3.0 Exam certification is also a great way for employers to assess the skills and expertise of their employees.

NEW QUESTION 102
Which of the following code blocks applies the boolean-returning Python function evaluateTestSuccess to column storeId of DataFrame transactionsDf as a user-defined function?

1.from pyspark.sql import types as T
2.evaluateTestSuccessUDF = udf(evaluateTestSuccess, T.BooleanType())
3.transactionsDf.withColumn(“result”, evaluateTestSuccessUDF(col(“storeId”)))

1.evaluateTestSuccessUDF = udf(evaluateTestSuccess)
2.transactionsDf.withColumn(“result”, evaluateTestSuccessUDF(storeId))

1.from pyspark.sql import types as T
2.evaluateTestSuccessUDF = udf(evaluateTestSuccess, T.IntegerType())
3.transactionsDf.withColumn(“result”, evaluateTestSuccess(col(“storeId”)))

1.evaluateTestSuccessUDF = udf(evaluateTestSuccess)
2.transactionsDf.withColumn(“result”, evaluateTestSuccessUDF(col(“storeId”)))

1.from pyspark.sql import types as T
2.evaluateTestSuccessUDF = udf(evaluateTestSuccess, T.BooleanType())
3.transactionsDf.withColumn(“result”, evaluateTestSuccess(col(“storeId”)))

NEW QUESTION 103
Which of the following describes the difference between client and cluster execution modes?

In cluster mode, the driver runs on the worker nodes, while the client mode runs the driver on the client machine.

In cluster mode, the driver runs on the edge node, while the client mode runs the driver in a worker node.

In cluster mode, each node will launch its own executor, while in client mode, executors will exclusively run on the client machine.

In client mode, the cluster manager runs on the same host as the driver, while in cluster mode, the cluster manager runs on a separate node.

In cluster mode, the driver runs on the master node, while in client mode, the driver runs on a virtual machine in the cloud.

NEW QUESTION 104
Which of the following statements about reducing out-of-memory errors is incorrect?

Concatenating multiple string columns into a single column may guard against out-of-memory errors.

Reducing partition size can help against out-of-memory errors.

Limiting the amount of data being automatically broadcast in joins can help against out-of-memory errors.

Setting a limit on the maximum size of serialized data returned to the driver may help prevent out-of-memory errors.

Decreasing the number of cores available to each executor can help against out-of-memory errors.

Explanation
Concatenating multiple string columns into a single column may guard against out-of-memory errors.
Exactly, this is an incorrect answer! Concatenating any string columns does not reduce the size of the data, it just structures it a different way. This does little to how Spark processes the data and definitely does not reduce out-of-memory errors.
Reducing partition size can help against out-of-memory errors.
No, this is not incorrect. Reducing partition size is a viable way to aid against out-of-memory errors, since executors need to load partitions into memory before processing them. If the executor does not have enough memory available to do that, it will throw an out-of-memory error. Decreasing partition size can therefore be very helpful for preventing that.
Decreasing the number of cores available to each executor can help against out-of-memory errors.
No, this is not incorrect. To process a partition, this partition needs to be loaded into the memory of an executor. If you imagine that every core in every executor processes a partition, potentially in parallel with other executors, you can imagine that memory on the machine hosting the executors fills up quite quickly. So, memory usage of executors is a concern, especially when multiple partitions are processed at the same time. To strike a balance between performance and memory usage, decreasing the number of cores may help against out-of-memory errors.
Setting a limit on the maximum size of serialized data returned to the driver may help prevent out-of-memory errors.
No, this is not incorrect. When using commands like collect() that trigger the transmission of potentially large amounts of data from the cluster to the driver, the driver may experience out-of-memory errors. One strategy to avoid this is to be careful about using commands like collect() that send back large amounts of data to the driver. Another strategy is setting the parameter spark.driver.maxResultSize. If data to be transmitted to the driver exceeds the threshold specified by the parameter, Spark will abort the job and therefore prevent an out-of-memory error.
Limiting the amount of data being automatically broadcast in joins can help against out-of-memory errors.
Wrong, this is not incorrect. As part of Spark’s internal optimization, Spark may choose to speed up operations by broadcasting (usually relatively small) tables to executors. This broadcast is happening from the driver, so all the broadcast tables are loaded into the driver first. If these tables are relatively big, or multiple mid-size tables are being broadcast, this may lead to an out-of- memory error. The maximum table size for which Spark will consider broadcasting is set by the spark.sql.autoBroadcastJoinThreshold parameter.
More info: Configuration – Spark 3.1.2 Documentation and Spark OOM Error – Closeup. Does the following look familiar when… | by Amit Singh Rathore | The Startup | Medium

NEW QUESTION 105
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?

transactionsDf.summary()

transactionsDf.agg(“count”, “mean”, “stddev”, “25%”, “50%”, “75%”, “min”)

transactionsDf.summary(“count”, “mean”, “stddev”, “25%”, “50%”, “75%”, “max”).show()

transactionsDf.agg(“count”, “mean”, “stddev”, “25%”, “50%”, “75%”, “min”).show()

transactionsDf.summary().show()

NEW QUESTION 106
Which of the following code blocks applies the Python function to_limit on column predError in table transactionsDf, returning a DataFrame with columns transactionId and result?

1.spark.udf.register(“LIMIT_FCN”, to_limit)
2.spark.sql(“SELECT transactionId, LIMIT_FCN(predError) AS result FROM transactionsDf”) (Correct)

1.spark.udf.register(“LIMIT_FCN”, to_limit)
2.spark.sql(“SELECT transactionId, LIMIT_FCN(predError) FROM transactionsDf AS result”)

1.spark.udf.register(“LIMIT_FCN”, to_limit)
2.spark.sql(“SELECT transactionId, to_limit(predError) AS result FROM transactionsDf”) spark.sql(“SELECT transactionId, udf(to_limit(predError)) AS result FROM transactionsDf”)

1.spark.udf.register(to_limit, “LIMIT_FCN”)
2.spark.sql(“SELECT transactionId, LIMIT_FCN(predError) AS result FROM transactionsDf”)

NEW QUESTION 107
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.

The commas in the tuples with the colors should be eliminated.

The colors red, blue, and green should be expressed as a simple Python list, and not a list of tuples.

Instead of color, a data type should be specified.

The “color” expression needs to be wrapped in brackets, so it reads [“color”].

NEW QUESTION 108
The code block shown below should return a copy of DataFrame transactionsDf with an added column cos.
This column should have the values in column value converted to degrees and having the cosine of those converted values taken, rounded to two decimals. Choose the answer that correctly fills the blanks in the code block to accomplish this.
Code block:
transactionsDf.__1__(__2__, round(__3__(__4__(__5__)),2))

1. withColumn
2. col(“cos”)
3. cos
4. degrees
5. transactionsDf.value

1. withColumnRenamed
2. “cos”
3. cos
4. degrees
5. “transactionsDf.value”

1. withColumn
2. “cos”
3. cos
4. degrees
5. transactionsDf.value

1. withColumn
2. col(“cos”)
3. cos
4. degrees
5. col(“value”)
E
. 1. withColumn
2. “cos”
3. degrees
4. cos
5. col(“value”)

NEW QUESTION 109
The code block displayed below contains an error. The code block should display the schema of DataFrame transactionsDf. Find the error.
Code block:
transactionsDf.rdd.printSchema

There is no way to print a schema directly in Spark, since the schema can be printed easily through using print(transactionsDf.columns), so that should be used instead.

The code block should be wrapped into a print() operation.

printSchema is only accessible through the spark session, so the code block should be rewritten as spark.printSchema(transactionsDf).

printSchema is a method and should be written as printSchema(). It is also not callable through transactionsDf.rdd, but should be called directly from transactionsDf.
(Correct)

printSchema is a not a method of transactionsDf.rdd. Instead, the schema should be printed via transactionsDf.print_schema().

NEW QUESTION 110
The code block displayed below contains an error. When the code block below has executed, it should have divided DataFrame transactionsDf into 14 parts, based on columns storeId and transactionDate (in this order). Find the error.
Code block:
transactionsDf.coalesce(14, (“storeId”, “transactionDate”))

The parentheses around the column names need to be removed and .select() needs to be appended to the code block.

Operator coalesce needs to be replaced by repartition, the parentheses around the column names need to be removed, and .count() needs to be appended to the code block.
(Correct)

Operator coalesce needs to be replaced by repartition, the parentheses around the column names need to be removed, and .select() needs to be appended to the code block.

Operator coalesce needs to be replaced by repartition and the parentheses around the column names need to be replaced by square brackets.

Operator coalesce needs to be replaced by repartition.

NEW QUESTION 111
Which of the following code blocks creates a new DataFrame with two columns season and wind_speed_ms where column season is of data type string and column wind_speed_ms is of data type double?

spark.DataFrame({“season”: [“winter”,”summer”], “wind_speed_ms”: [4.5, 7.5]})

spark.createDataFrame([(“summer”, 4.5), (“winter”, 7.5)], [“season”, “wind_speed_ms”])

1. from pyspark.sql import types as T
2. spark.createDataFrame(((“summer”, 4.5), (“winter”, 7.5)), T.StructType([T.StructField(“season”,

CharType()), T.StructField(“season”, T.DoubleType())]))

spark.newDataFrame([(“summer”, 4.5), (“winter”, 7.5)], [“season”, “wind_speed_ms”])

spark.createDataFrame({“season”: [“winter”,”summer”], “wind_speed_ms”: [4.5, 7.5]})

Explanation
spark.createDataFrame([(“summer”, 4.5), (“winter”, 7.5)], [“season”, “wind_speed_ms”]) Correct. This command uses the Spark Session’s createDataFrame method to create a new DataFrame. Notice how rows, columns, and column names are passed in here: The rows are specified as a Python list. Every entry in the list is a new row. Columns are specified as Python tuples (for example (“summer”, 4.5)). Every column is one entry in the tuple.
The column names are specified as the second argument to createDataFrame(). The documentation (link below) shows that “when schema is a list of column names, the type of each column will be inferred from data” (the first argument). Since values 4.5 and 7.5 are both float variables, Spark will correctly infer the double type for column wind_speed_ms. Given that all values in column
“season” contain only strings, Spark will cast the column appropriately as string.
Find out more about SparkSession.createDataFrame() via the link below.
spark.newDataFrame([(“summer”, 4.5), (“winter”, 7.5)], [“season”, “wind_speed_ms”]) No, the SparkSession does not have a newDataFrame method.
from pyspark.sql import types as T
spark.createDataFrame(((“summer”, 4.5), (“winter”, 7.5)), T.StructType([T.StructField(“season”,
T.CharType()), T.StructField(“season”, T.DoubleType())]))
No. pyspark.sql.types does not have a CharType type. See link below for available data types in Spark.
spark.createDataFrame({“season”: [“winter”,”summer”], “wind_speed_ms”: [4.5, 7.5]}) No, this is not correct Spark syntax. If you have considered this option to be correct, you may have some experience with Python’s pandas package, in which this would be correct syntax. To create a Spark DataFrame from a Pandas DataFrame, you can simply use spark.createDataFrame(pandasDf) where pandasDf is the Pandas DataFrame.
Find out more about Spark syntax options using the examples in the documentation for SparkSession.createDataFrame linked below.
spark.DataFrame({“season”: [“winter”,”summer”], “wind_speed_ms”: [4.5, 7.5]}) No, the Spark Session (indicated by spark in the code above) does not have a DataFrame method.
More info: pyspark.sql.SparkSession.createDataFrame – PySpark 3.1.1 documentation and Data Types – Spark 3.1.2 Documentation Static notebook | Dynamic notebook: See test 1

NEW QUESTION 112
The code block shown below should write DataFrame transactionsDf to disk at path csvPath as a single CSV file, using tabs (t characters) as separators between columns, expressing missing values as string n/a, and omitting a header row with column names. Choose the answer that correctly fills the blanks in the code block to accomplish this.
transactionsDf.__1__.write.__2__(__3__, ” “).__4__.__5__(csvPath)

1. coalesce(1)
2. option
3. “sep”
4. option(“header”, True)
5. path

1. coalesce(1)
2. option
3. “colsep”
4. option(“nullValue”, “n/a”)
5. path

1. repartition(1)
2. option
3. “sep”
4. option(“nullValue”, “n/a”)
5. csv
(Correct)

1. csv
2. option
3. “sep”
4. option(“emptyValue”, “n/a”)
5. path
* 1. repartition(1)
2. mode
3. “sep”
4. mode(“nullValue”, “n/a”)
5. csv

NEW QUESTION 113
Which of the following describes the conversion of a computational query into an execution plan in Spark?

Spark uses the catalog to resolve the optimized logical plan.

The catalog assigns specific resources to the optimized memory plan.

The executed physical plan depends on a cost optimization from a previous stage.

Depending on whether DataFrame API or SQL API are used, the physical plan may differ.

The catalog assigns specific resources to the physical plan.

NEW QUESTION 114
The code block displayed below contains an error. The code block should count the number of rows that have a predError of either 3 or 6. Find the error.
Code block:
transactionsDf.filter(col(‘predError’).in([3, 6])).count()

The number of rows cannot be determined with the count() operator.

Instead of filter, the select method should be used.

The method used on column predError is incorrect.

Instead of a list, the values need to be passed as single arguments to the in operator.

Numbers 3 and 6 need to be passed as string variables.

NEW QUESTION 115
Which of the following statements about executors is correct, assuming that one can consider each of the JVMs working as executors as a pool of task execution slots?

Slot is another name for executor.

There must be less executors than tasks.

An executor runs on a single core.

There must be more slots than tasks.

Tasks run in parallel via slots.

NEW QUESTION 116
Which of the following describes tasks?

A task is a command sent from the driver to the executors in response to a transformation.

Tasks transform jobs into DAGs.

A task is a collection of slots.

A task is a collection of rows.

Tasks get assigned to the executors by the driver.

Get Ready with Associate-Developer-Apache-Spark Exam Dumps: https://www.trainingdump.com/Databricks/Associate-Developer-Apache-Spark-practice-exam-dumps.html

Associate-Developer-Apache-Spark Dumps (2026) Prepare Your Exam With 179 Questions [Q102-Q116]

Associate-Developer-Apache-Spark Dumps (2026) Prepare Your Exam With 179 Questions [Q102-Q116]

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