Free Associate-Developer-Apache-Spark braindumps download (Associate-Developer-Apache-Spark exam dumps Free Updated Jan 17, 2026) [Q46-Q69]

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Free Associate-Developer-Apache-Spark braindumps download (Associate-Developer-Apache-Spark exam dumps Free Updated Jan 17, 2026)

Associate-Developer-Apache-Spark Dumps for Pass Guaranteed – Pass Associate-Developer-Apache-Spark Exam 2026

Databricks Associate-Developer-Apache-Spark certification exam covers a range of topics, including Spark architecture, Spark SQL, Spark streaming, machine learning, and dataframes. Associate-Developer-Apache-Spark exam consists of multiple-choice questions and requires candidates to demonstrate their knowledge and skills in using Spark to solve real-world data processing problems. Databricks Certified Associate Developer for Apache Spark 3.0 Exam certification is highly respected in the industry and is recognized by leading organizations that use Apache Spark for data processing and analytics. Becoming certified can help developers advance their careers, gain recognition for their skills, and increase their earning potential.

NEW QUESTION 46
Which of the following code blocks reads in the JSON file stored at filePath as a DataFrame?

spark.read.json(filePath)

spark.read.path(filePath, source=”json”)

spark.read().path(filePath)

spark.read().json(filePath)

spark.read.path(filePath)

NEW QUESTION 47
In which order should the code blocks shown below be run in order to create a DataFrame that shows the mean of column predError of DataFrame transactionsDf per column storeId and productId, where productId should be either 2 or 3 and the returned DataFrame should be sorted in ascending order by column storeId, leaving out any nulls in that column?
DataFrame transactionsDf:
1.+————-+———+—–+——-+———+—-+
2.|transactionId|predError|value|storeId|productId| f|
3.+————-+———+—–+——-+———+—-+
4.| 1| 3| 4| 25| 1|null|
5.| 2| 6| 7| 2| 2|null|
6.| 3| 3| null| 25| 3|null|
7.| 4| null| null| 3| 2|null|
8.| 5| null| null| null| 2|null|
9.| 6| 3| 2| 25| 2|null|
10.+————-+———+—–+——-+———+—-+
1. .mean(“predError”)
2. .groupBy(“storeId”)
3. .orderBy(“storeId”)
4. transactionsDf.filter(transactionsDf.storeId.isNotNull())
5. .pivot(“productId”, [2, 3])

4, 5, 2, 3, 1

4, 2, 1

4, 1, 5, 2, 3

4, 2, 5, 1, 3

4, 3, 2, 5, 1

NEW QUESTION 48
The code block displayed below contains an error. The code block should return a copy of DataFrame transactionsDf where the name of column transactionId has been changed to transactionNumber. Find the error.
Code block:
transactionsDf.withColumn(“transactionNumber”, “transactionId”)

The arguments to the withColumn method need to be reordered.

The arguments to the withColumn method need to be reordered and the copy() operator should be appended to the code block to ensure a copy is returned.

The copy() operator should be appended to the code block to ensure a copy is returned.

Each column name needs to be wrapped in the col() method and method withColumn should be replaced by method withColumnRenamed.

The method withColumn should be replaced by method withColumnRenamed and the arguments to the method need to be reordered.

NEW QUESTION 49
Which of the following code blocks reads in the parquet file stored at location filePath, given that all columns in the parquet file contain only whole numbers and are stored in the most appropriate format for this kind of data?

1.spark.read.schema(
2. StructType(
3. StructField(“transactionId”, IntegerType(), True),
4. StructField(“predError”, IntegerType(), True)
5. )).load(filePath)

1.spark.read.schema([
2. StructField(“transactionId”, NumberType(), True),
3. StructField(“predError”, IntegerType(), True)
4. ]).load(filePath)

1.spark.read.schema(
2. StructType([
3. StructField(“transactionId”, StringType(), True),
4. StructField(“predError”, IntegerType(), True)]
5. )).parquet(filePath)

1.spark.read.schema(
2. StructType([
3. StructField(“transactionId”, IntegerType(), True),
4. StructField(“predError”, IntegerType(), True)]
5. )).format(“parquet”).load(filePath)

1.spark.read.schema([
2. StructField(“transactionId”, IntegerType(), True),
3. StructField(“predError”, IntegerType(), True)
4. ]).load(filePath, format=”parquet”)

NEW QUESTION 50
The code block displayed below contains an error. The code block should configure Spark so that DataFrames up to a size of 20 MB will be broadcast to all worker nodes when performing a join.
Find the error.
Code block:

spark.conf.set(“spark.sql.autoBroadcastJoinThreshold”, 20)

Spark will only broadcast DataFrames that are much smaller than the default value.

The correct option to write configurations is through spark.config and not spark.conf.

Spark will only apply the limit to threshold joins and not to other joins.

The passed limit has the wrong variable type.

The command is evaluated lazily and needs to be followed by an action.

NEW QUESTION 51
Which of the following code blocks concatenates rows of DataFrames transactionsDf and transactionsNewDf, omitting any duplicates?

transactionsDf.concat(transactionsNewDf).unique()

transactionsDf.union(transactionsNewDf).distinct()

spark.union(transactionsDf, transactionsNewDf).distinct()

transactionsDf.join(transactionsNewDf, how=”union”).distinct()

transactionsDf.union(transactionsNewDf).unique()

NEW QUESTION 52
Which of the following code blocks returns a copy of DataFrame transactionsDf where the column storeId has been converted to string type?

transactionsDf.withColumn(“storeId”, convert(“storeId”, “string”))

transactionsDf.withColumn(“storeId”, col(“storeId”, “string”))

transactionsDf.withColumn(“storeId”, col(“storeId”).convert(“string”))

transactionsDf.withColumn(“storeId”, col(“storeId”).cast(“string”))

transactionsDf.withColumn(“storeId”, convert(“storeId”).as(“string”))

NEW QUESTION 53
Which of the following is a characteristic of the cluster manager?

Each cluster manager works on a single partition of data.

The cluster manager receives input from the driver through the SparkContext.

The cluster manager does not exist in standalone mode.

The cluster manager transforms jobs into DAGs.

In client mode, the cluster manager runs on the edge node.

NEW QUESTION 54
Which of the following describes a difference between Spark’s cluster and client execution modes?

In cluster mode, the cluster manager resides on a worker node, while it resides on an edge node in client mode.

In cluster mode, executor processes run on worker nodes, while they run on gateway nodes in client mode.

In cluster mode, the driver resides on a worker node, while it resides on an edge node in client mode.

In cluster mode, a gateway machine hosts the driver, while it is co-located with the executor in client mode.

In cluster mode, the Spark driver is not co-located with the cluster manager, while it is co-located in client mode.

1. filter
2. col(“supplier”).isin(“Sports”)
3. “itemName”
4. explode(col(“attributes”))

1. where
2. col(“supplier”).contains(“Sports”)
3. “itemName”
4. “attributes”

1. where
2. col(supplier).contains(“Sports”)
3. explode(attributes)
4. itemName

1. where
2. “Sports”.isin(col(“Supplier”))
3. “itemName”
4. array_explode(“attributes”)

1. filter
2. col(“supplier”).contains(“Sports”)
3. “itemName”
4. explode(“attributes”)

Explanation
Output of correct code block:
+———————————-+——+
|itemName |col |
+———————————-+——+
|Thick Coat for Walking in the Snow|blue |
|Thick Coat for Walking in the Snow|winter|
|Thick Coat for Walking in the Snow|cozy |
|Outdoors Backpack |green |
|Outdoors Backpack |summer|
|Outdoors Backpack |travel|
+———————————-+——+
The key to solving this question is knowing about Spark’s explode operator. Using this operator, you can extract values from arrays into single rows. The following guidance steps through the answers systematically from the first to the last gap. Note that there are many ways to solving the gap questions and filtering out wrong answers, you do not always have to start filtering out from the first gap, but can also exclude some answers based on obvious problems you see with them.
The answers to the first gap present you with two options: filter and where. These two are actually synonyms in PySpark, so using either of those is fine. The answer options to this gap therefore do not help us in selecting the right answer.
The second gap is more interesting. One answer option includes “Sports”.isin(col(“Supplier”)). This construct does not work, since Python’s string does not have an isin method. Another option contains col(supplier). Here, Python will try to interpret supplier as a variable. We have not set this variable, so this is not a viable answer. Then, you are left with answers options that include col (“supplier”).contains(“Sports”) and col(“supplier”).isin(“Sports”). The question states that we are looking for suppliers whose name includes Sports, so we have to go for the contains operator here.
We would use the isin operator if we wanted to filter out for supplier names that match any entries in a list of supplier names.
Finally, we are left with two answers that fill the third gap both with “itemName” and the fourth gap either with explode(“attributes”) or “attributes”. While both are correct Spark syntax, only explode (“attributes”) will help us achieve our goal. Specifically, the question asks for one attribute from column attributes per row – this is what the explode() operator does.
One answer option also includes array_explode() which is not a valid operator in PySpark.
More info: pyspark.sql.functions.explode – PySpark 3.1.2 documentation
Static notebook | Dynamic notebook: See test 3

NEW QUESTION 56
The code block displayed below contains an error. The code block is intended to return all columns of DataFrame transactionsDf except for columns predError, productId, and value. Find the error.
Excerpt of DataFrame transactionsDf:
transactionsDf.select(~col(“predError”), ~col(“productId”), ~col(“value”))

The select operator should be replaced by the drop operator and the arguments to the drop operator should be column names predError, productId and value wrapped in the col operator so they should be expressed like drop(col(predError), col(productId), col(value)).

The select operator should be replaced with the deselect operator.

The column names in the select operator should not be strings and wrapped in the col operator, so they should be expressed like select(~col(predError), ~col(productId), ~col(value)).

The select operator should be replaced by the drop operator.

The select operator should be replaced by the drop operator and the arguments to the drop operator should be column names predError, productId and value as strings.
(Correct)

NEW QUESTION 57
Which of the following code blocks efficiently converts DataFrame transactionsDf from 12 into 24 partitions?

transactionsDf.repartition(24, boost=True)

transactionsDf.repartition()

transactionsDf.repartition(“itemId”, 24)

transactionsDf.coalesce(24)

transactionsDf.repartition(24)

NEW QUESTION 58
The code block shown below should return a copy of DataFrame transactionsDf without columns value and productId and with an additional column associateId that has the value 5. Choose the answer that correctly fills the blanks in the code block to accomplish this.
transactionsDf.__1__(__2__, __3__).__4__(__5__, ‘value’)

1. withColumn
2. ‘associateId’
3. 5
4. remove
5. ‘productId’

1. withNewColumn
2. associateId
3. lit(5)
4. drop
5. productId

1. withColumn
2. ‘associateId’
3. lit(5)
4. drop
5. ‘productId’

1. withColumnRenamed
2. ‘associateId’
3. 5
4. drop
5. ‘productId’

1. withColumn
2. col(associateId)
3. lit(5)
4. drop
5. col(productId)

NEW QUESTION 59
In which order should the code blocks shown below be run in order to read a JSON file from location jsonPath into a DataFrame and return only the rows that do not have value 3 in column productId?
1. importedDf.createOrReplaceTempView(“importedDf”)
2. spark.sql(“SELECT * FROM importedDf WHERE productId != 3”)
3. spark.sql(“FILTER * FROM importedDf WHERE productId != 3”)
4. importedDf = spark.read.option(“format”, “json”).path(jsonPath)
5. importedDf = spark.read.json(jsonPath)

4, 1, 2

5, 1, 3

5, 2

4, 1, 3

5, 1, 2

NEW QUESTION 60
The code block displayed below contains an error. The code block should use Python method find_most_freq_letter to find the letter present most in column itemName of DataFrame itemsDf and return it in a new column most_frequent_letter. Find the error.
Code block:
1. find_most_freq_letter_udf = udf(find_most_freq_letter)
2. itemsDf.withColumn(“most_frequent_letter”, find_most_freq_letter(“itemName”))

Spark is not using the UDF method correctly.

The UDF method is not registered correctly, since the return type is missing.

The “itemName” expression should be wrapped in col().

UDFs do not exist in PySpark.

Spark is not adding a column.

NEW QUESTION 61
Which of the following code blocks returns all unique values across all values in columns value and productId in DataFrame transactionsDf in a one-column DataFrame?

tranactionsDf.select(‘value’).join(transactionsDf.select(‘productId’), col(‘value’)==col(‘productId’),
‘outer’)

transactionsDf.select(col(‘value’), col(‘productId’)).agg({‘*’: ‘count’})

transactionsDf.select(‘value’, ‘productId’).distinct()

transactionsDf.select(‘value’).union(transactionsDf.select(‘productId’)).distinct()

transactionsDf.agg({‘value’: ‘collect_set’, ‘productId’: ‘collect_set’})

NEW QUESTION 62
Which of the following code blocks creates a new one-column, two-row DataFrame dfDates with column date of type timestamp?

1.dfDates = spark.createDataFrame([“23/01/2022 11:28:12″,”24/01/2022 10:58:34”], [“date”])
2.dfDates = dfDates.withColumn(“date”, to_timestamp(“dd/MM/yyyy HH:mm:ss”, “date”))

1.dfDates = spark.createDataFrame([(“23/01/2022 11:28:12”,),(“24/01/2022 10:58:34”,)], [“date”])
2.dfDates = dfDates.withColumnRenamed(“date”, to_timestamp(“date”, “yyyy-MM-dd HH:mm:ss”))

1.dfDates = spark.createDataFrame([(“23/01/2022 11:28:12”,),(“24/01/2022 10:58:34”,)], [“date”])
2.dfDates = dfDates.withColumn(“date”, to_timestamp(“date”, “dd/MM/yyyy HH:mm:ss”))

1.dfDates = spark.createDataFrame([“23/01/2022 11:28:12″,”24/01/2022 10:58:34”], [“date”])
2.dfDates = dfDates.withColumnRenamed(“date”, to_datetime(“date”, “yyyy-MM-dd HH:mm:ss”))

1.dfDates = spark.createDataFrame([(“23/01/2022 11:28:12”,),(“24/01/2022 10:58:34”,)], [“date”])

Explanation
This question is tricky. Two things are important to know here:
First, the syntax for createDataFrame: Here you need a list of tuples, like so: [(1,), (2,)]. To define a tuple in Python, if you just have a single item in it, it is important to put a comma after the item so that Python interprets it as a tuple and not just a normal parenthesis.
Second, you should understand the to_timestamp syntax. You can find out more about it in the documentation linked below.
For good measure, let’s examine in detail why the incorrect options are wrong:
dfDates = spark.createDataFrame([(“23/01/2022 11:28:12”,),(“24/01/2022 10:58:34”,)], [“date”]) This code snippet does everything the question asks for – except that the data type of the date column is a string and not a timestamp. When no schema is specified, Spark sets the string data type as default.
dfDates = spark.createDataFrame([“23/01/2022 11:28:12″,”24/01/2022 10:58:34”], [“date”]) dfDates = dfDates.withColumn(“date”, to_timestamp(“dd/MM/yyyy HH:mm:ss”, “date”)) In the first row of this command, Spark throws the following error: TypeError: Can not infer schema for type:
<class ‘str’>. This is because Spark expects to find row information, but instead finds strings. This is why you need to specify the data as tuples. Fortunately, the Spark documentation (linked below) shows a number of examples for creating DataFrames that should help you get on the right track here.
dfDates = spark.createDataFrame([(“23/01/2022 11:28:12”,),(“24/01/2022 10:58:34”,)], [“date”]) dfDates = dfDates.withColumnRenamed(“date”, to_timestamp(“date”, “yyyy-MM-dd HH:mm:ss”)) The issue with this answer is that the operator withColumnRenamed is used. This operator simply renames a column, but it has no power to modify its actual content. This is why withColumn should be used instead. In addition, the date format yyyy-MM-dd HH:mm:ss does not reflect the format of the actual timestamp: “23/01/2022 11:28:12”.
dfDates = spark.createDataFrame([“23/01/2022 11:28:12″,”24/01/2022 10:58:34”], [“date”]) dfDates = dfDates.withColumnRenamed(“date”, to_datetime(“date”, “yyyy-MM-dd HH:mm:ss”)) Here, withColumnRenamed is used instead of withColumn (see above). In addition, the rows are not expressed correctly – they should be written as tuples, using parentheses. Finally, even the date format is off here (see above).
More info: pyspark.sql.functions.to_timestamp – PySpark 3.1.2 documentation and pyspark.sql.SparkSession.createDataFrame – PySpark 3.1.1 documentation Static notebook | Dynamic notebook: See test 2

NEW QUESTION 63
The code block shown below should return an exact copy of DataFrame transactionsDf that does not include rows in which values in column storeId have the value 25. Choose the answer that correctly fills the blanks in the code block to accomplish this.

transactionsDf.remove(transactionsDf.storeId==25)

transactionsDf.where(transactionsDf.storeId!=25)

transactionsDf.filter(transactionsDf.storeId==25)

transactionsDf.drop(transactionsDf.storeId==25)

transactionsDf.select(transactionsDf.storeId!=25)

itemsDf.filter(col(supplier).not_contains(‘X’)).select(supplier).distinct()

itemsDf.select(~col(‘supplier’).contains(‘X’)).distinct()

itemsDf.filter(not(col(‘supplier’).contains(‘X’))).select(‘supplier’).unique()

itemsDf.filter(~col(‘supplier’).contains(‘X’)).select(‘supplier’).distinct()

itemsDf.filter(!col(‘supplier’).contains(‘X’)).select(col(‘supplier’)).unique()

NEW QUESTION 65
Which of the following describes characteristics of the Spark UI?

Via the Spark UI, workloads can be manually distributed across executors.

Via the Spark UI, stage execution speed can be modified.

The Scheduler tab shows how jobs that are run in parallel by multiple users are distributed across the cluster.

There is a place in the Spark UI that shows the property spark.executor.memory.

Some of the tabs in the Spark UI are named Jobs, Stages, Storage, DAGs, Executors, and SQL.

NEW QUESTION 66
Which of the following code blocks sorts DataFrame transactionsDf both by column storeId in ascending and by column productId in descending order, in this priority?

transactionsDf.sort(“storeId”, asc(“productId”))

transactionsDf.sort(col(storeId)).desc(col(productId))

transactionsDf.order_by(col(storeId), desc(col(productId)))

transactionsDf.sort(“storeId”, desc(“productId”))

transactionsDf.sort(“storeId”).sort(desc(“productId”))

NEW QUESTION 67
Which of the following describes Spark’s way of managing memory?

Spark uses a subset of the reserved system memory.

Storage memory is used for caching partitions derived from DataFrames.

As a general rule for garbage collection, Spark performs better on many small objects than few big objects.

Disabling serialization potentially greatly reduces the memory footprint of a Spark application.

Spark’s memory usage can be divided into three categories: Execution, transaction, and storage.

NEW QUESTION 68
The code block displayed below contains an error. The code block is intended to join DataFrame itemsDf with the larger DataFrame transactionsDf on column itemId. Find the error.
Code block:
transactionsDf.join(itemsDf, “itemId”, how=”broadcast”)

The syntax is wrong, how= should be removed from the code block.

The join method should be replaced by the broadcast method.

Spark will only perform the broadcast operation if this behavior has been enabled on the Spark cluster.

The larger DataFrame transactionsDf is being broadcasted, rather than the smaller DataFrame itemsDf.

broadcast is not a valid join type.

NEW QUESTION 69
Which of the following code blocks returns a single-column DataFrame of all entries in Python list throughputRates which contains only float-type values ?

spark.createDataFrame((throughputRates), FloatType)

spark.createDataFrame(throughputRates, FloatType)

spark.DataFrame(throughputRates, FloatType)

spark.createDataFrame(throughputRates)

spark.createDataFrame(throughputRates, FloatType())

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Free Associate-Developer-Apache-Spark braindumps download (Associate-Developer-Apache-Spark exam dumps Free Updated Jan 17, 2026) [Q46-Q69]

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