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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”)))

Explanation
Recognizing that the UDF specification requires a return type (unless it is a string, which is the default) is important for solving this question. In addition, you should make sure that the generated UDF (evaluateTestSuccessUDF) and not the Python function (evaluateTestSuccess) is applied to column storeId.
More info: pyspark.sql.functions.udf – PySpark 3.1.2 documentation
Static notebook | Dynamic notebook: See test 2

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.

Explanation
In cluster mode, the driver runs on the master node, while in client mode, the driver runs on a virtual machine in the cloud.
This is wrong, since execution modes do not specify whether workloads are run in the cloud or on-premise.
In cluster mode, each node will launch its own executor, while in client mode, executors will exclusively run on the client machine.
Wrong, since in both cases executors run on worker nodes.
In cluster mode, the driver runs on the edge node, while the client mode runs the driver in a worker node.
Wrong – in cluster mode, the driver runs on a worker node. In client mode, the driver runs 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.
No. In both modes, the cluster manager is typically on a separate node – not on the same host as the driver. It only runs on the same host as the driver in local execution mode.
More info: Learning Spark, 2nd Edition, Chapter 1, and Spark: The Definitive Guide, Chapter 15. ()

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()

Explanation
The DataFrame.summary() command is very practical for quickly calculating statistics of a DataFrame. You need to call .show() to display the results of the calculation. By default, the command calculates various statistics (see documentation linked below), including standard deviation and minimum.
Note that the answer that lists many options in the summary() parentheses does not include the minimum, which is asked for in the question.
Answer options that include agg() do not work here as shown, since DataFrame.agg() expects more complex, column-specific instructions on how to aggregate values.
More info:
– pyspark.sql.DataFrame.summary – PySpark 3.1.2 documentation
– pyspark.sql.DataFrame.agg – PySpark 3.1.2 documentation
Static notebook | Dynamic notebook: See test 3

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”)

Explanation
spark.udf.register(“LIMIT_FCN”, to_limit)
spark.sql(“SELECT transactionId, LIMIT_FCN(predError) AS result FROM transactionsDf”) Correct! First, you have to register to_limit as UDF to use it in a sql statement. Then, you can use it under the LIMIT_FCN name, correctly naming the resulting column result.
spark.udf.register(to_limit, “LIMIT_FCN”)
spark.sql(“SELECT transactionId, LIMIT_FCN(predError) AS result FROM transactionsDf”) No. In this answer, the arguments to spark.udf.register are flipped.
spark.udf.register(“LIMIT_FCN”, to_limit)
spark.sql(“SELECT transactionId, to_limit(predError) AS result FROM transactionsDf”) Wrong, this answer does not use the registered LIMIT_FCN in the sql statement, but tries to access the to_limit method directly. This will fail, since Spark cannot access it.
spark.sql(“SELECT transactionId, udf(to_limit(predError)) AS result FROM transactionsDf”) Incorrect, there is no udf method in Spark’s SQL.
spark.udf.register(“LIMIT_FCN”, to_limit)
spark.sql(“SELECT transactionId, LIMIT_FCN(predError) FROM transactionsDf AS result”) False. In this answer, the column that results from applying the UDF is not correctly renamed to result.
Static notebook | Dynamic notebook: See test 3

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”].

Explanation
Correct code block:
spark.createDataFrame([(“red”,), (“blue”,), (“green”,)], [“color”])
The createDataFrame syntax is not exactly straightforward, but luckily the documentation (linked below) provides several examples on how to use it. It also shows an example very similar to the code block presented here which should help you answer this question correctly.
More info: pyspark.sql.SparkSession.createDataFrame – PySpark 3.1.2 documentation Static notebook | Dynamic notebook: See test 2

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”)

Explanation
Correct code block:
transactionsDf.withColumn(“cos”, round(cos(degrees(transactionsDf.value)),2)) This question is especially confusing because col, “cos” are so similar. Similar-looking answer options can also appear in the exam and, just like in this question, you need to pay attention to the details to identify what the correct answer option is.
The first answer option to throw out is the one that starts with withColumnRenamed: The question NO:
speaks specifically of adding a column. The withColumnRenamed operator only renames an existing column, however, so you cannot use it here.
Next, you will have to decide what should be in gap 2, the first argument of transactionsDf.withColumn().
Looking at the documentation (linked below), you can find out that the first argument of withColumn actually needs to be a string with the name of the column to be added. So, any answer that includes col(“cos”) as the option for gap 2 can be disregarded.
This leaves you with two possible answers. The real difference between these two answers is where the cos and degree methods are, either in gaps 3 and 4, or vice-versa. From the question you can find out that the new column should have “the values in column value converted to degrees and having the cosine of those converted values taken”. This prescribes you a clear order of operations: First, you convert values from column value to degrees and then you take the cosine of those values. So, the inner parenthesis (gap 4) should contain the degree method and then, logically, gap 3 holds the cos method. This leaves you with just one possible correct answer.
More info: pyspark.sql.DataFrame.withColumn – PySpark 3.1.2 documentation Static notebook | Dynamic notebook: See test 3

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().

Explanation
Correct code block:
transactionsDf.printSchema()
This is more of a knowledge question that you should just memorize or look up in the provided documentation during the exam. You can get more info about DataFrame.printSchema() in the documentation (link below). However – it is a plain simple method without any arguments.
One answer points to an alternative of printing the schema: You could also use print(transactionsDf.schema).
This will give you readable, but not nicely formatted, description of the schema.
More info: pyspark.sql.DataFrame.printSchema – PySpark 3.1.1 documentation Static notebook | Dynamic notebook: See test 1

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.

Explanation
Correct code block:
transactionsDf.repartition(14, “storeId”, “transactionDate”).count()
Since we do not know how many partitions DataFrame transactionsDf has, we cannot safely use coalesce, since it would not make any change if the current number of partitions is smaller than 14.
So, we need to use repartition.
In the Spark documentation, the call structure for repartition is shown like this:
DataFrame.repartition(numPartitions, *cols). The * operator means that any argument after numPartitions will be interpreted as column. Therefore, the brackets need to be removed.
Finally, the question specifies that after the execution the DataFrame should be divided. So, indirectly this question is asking us to append an action to the code block. Since .select() is a transformation. the only possible choice here is .count().
More info: pyspark.sql.DataFrame.repartition – PySpark 3.1.1 documentation Static notebook | Dynamic notebook: See test 1

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

Explanation
Correct code block:
transactionsDf.repartition(1).write.option(“sep”, “\t”).option(“nullValue”, “n/a”).csv(csvPath) It is important here to understand that the question specifically asks for writing the DataFrame as a single CSV file. This should trigger you to think about partitions. By default, every partition is written as a separate file, so you need to include repatition(1) into your call. coalesce(1) works here, too!
Secondly, the question is very much an invitation to search through the parameters in the Spark documentation that work with DataFrameWriter.csv (link below). You will also need to know that you need an option() statement to apply these parameters.
The final concern is about the general call structure. Once you have called accessed write of your DataFrame, options follow and then you write the DataFrame with csv. Instead of csv(csvPath), you could also use save(csvPath, format=’csv’) here.
More info: pyspark.sql.DataFrameWriter.csv – PySpark 3.1.1 documentation Static notebook | Dynamic notebook: See test 1

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.

Explanation
The executed physical plan depends on a cost optimization from a previous stage.
Correct! Spark considers multiple physical plans on which it performs a cost analysis and selects the final physical plan in accordance with the lowest-cost outcome of that analysis. That final physical plan is then executed by Spark.
Spark uses the catalog to resolve the optimized logical plan.
No. Spark uses the catalog to resolve the unresolved logical plan, but not the optimized logical plan. Once the unresolved logical plan is resolved, it is then optimized using the Catalyst Optimizer.
The optimized logical plan is the input for physical planning.
The catalog assigns specific resources to the physical plan.
No. The catalog stores metadata, such as a list of names of columns, data types, functions, and databases.
Spark consults the catalog for resolving the references in a logical plan at the beginning of the conversion of the query into an execution plan. The result is then an optimized logical plan.
Depending on whether DataFrame API or SQL API are used, the physical plan may differ.
Wrong – the physical plan is independent of which API was used. And this is one of the great strengths of Spark!
The catalog assigns specific resources to the optimized memory plan.
There is no specific “memory plan” on the journey of a Spark computation.
More info: Spark’s Logical and Physical plans … When, Why, How and Beyond. | by Laurent Leturgez | datalex | Medium

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.

Explanation
Correct code block:
transactionsDf.filter(col(‘predError’).isin([3, 6])).count()
The isin method is the correct one to use here – the in method does not exist for the Column object.
More info: pyspark.sql.Column.isin – PySpark 3.1.2 documentation

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.

Explanation
Tasks run in parallel via slots.
Correct. Given the assumption, an executor then has one or more “slots”, defined by the equation spark.executor.cores / spark.task.cpus. With the executor’s resources divided into slots, each task takes up a slot and multiple tasks can be executed in parallel.
Slot is another name for executor.
No, a slot is part of an executor.
An executor runs on a single core.
No, an executor can occupy multiple cores. This is set by the spark.executor.cores option.
There must be more slots than tasks.
No. Slots just process tasks. One could imagine a scenario where there was just a single slot for multiple tasks, processing one task at a time. Granted – this is the opposite of what Spark should be used for, which is distributed data processing over multiple cores and machines, performing many tasks in parallel.
There must be less executors than tasks.
No, there is no such requirement.
More info: Spark Architecture | Distributed Systems Architecture (https://bit.ly/3x4MZZt)

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.

Explanation
Tasks get assigned to the executors by the driver.
Correct! Or, in other words: Executors take the tasks that they were assigned to by the driver, run them over partitions, and report the their outcomes back to the driver.
Tasks transform jobs into DAGs.
No, this statement disrespects the order of elements in the Spark hierarchy. The Spark driver transforms jobs into DAGs. Each job consists of one or more stages. Each stage contains one or more tasks.
A task is a collection of rows.
Wrong. A partition is a collection of rows. Tasks have little to do with a collection of rows. If anything, a task processes a specific partition.
A task is a command sent from the driver to the executors in response to a transformation.
Incorrect. The Spark driver does not send anything to the executors in response to a transformation, since transformations are evaluated lazily. So, the Spark driver would send tasks to executors only in response to actions.
A task is a collection of slots.
No. Executors have one or more slots to process tasks and each slot can be assigned a task.

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