DCAD : Databricks Certified Associate Developer for Apache Spark 3.0 Exam

DCAD Dumps
DCAD Braindumps
DCAD Real Questions
DCAD Practice Test
DCAD Actual Questions


Databricks
DCAD
Databricks Certified Associate Developer for Apache
Spark 3.0
https://killexams.com/pass4sure/exam-detail/DCAD
Question: 386
Which of the following code blocks removes all rows in the 6-column DataFrame transactionsDf that have missing
data in at least 3 columns?
A. transactionsDf.dropna("any")
B. transactionsDf.dropna(thresh=4)
C. transactionsDf.drop.na("",2)
D. transactionsDf.dropna(thresh=2)
E. transactionsDf.dropna("",4)
Answer: B
Explanation:
transactionsDf.dropna(thresh=4)
Correct. Note that by only working with the thresh keyword argument, the first how keyword argument is ignored.
Also, figuring out which value to set for thresh can be difficult, especially when
under pressure in the exam. Here, I recommend you use the notes to create a "simulation" of what different values for
thresh would do to a DataFrame. Here is an explanatory image why thresh=4 is
the correct answer to the question:
transactionsDf.dropna(thresh=2)
Almost right. See the comment about thresh for the correct answer above. transactionsDf.dropna("any")
No, this would remove all rows that have at least one missing value.
transactionsDf.drop.na("",2)
No, drop.na is not a proper DataFrame method.
transactionsDf.dropna("",4)
No, this does not work and will throw an error in Spark because Spark cannot understand the first argument.
More info: pyspark.sql.DataFrame.dropna - PySpark 3.1.1 documentation (https://bit.ly/2QZpiCp)
Static notebook | Dynamic notebook: See test 1,
Question: 387
"left_semi"
Answer: C
Explanation:
Correct code block:
transactionsDf.join(broadcast(itemsDf), "transactionId", "left_semi")
This QUESTION NO: is extremely difficult and exceeds the difficulty of questions in the exam by far.
A first indication of what is asked from you here is the remark that "the query should be executed in an optimized
way". You also have qualitative information about the size of itemsDf and transactionsDf. Given that itemsDf is "very
small" and that the execution should be optimized, you should consider instructing Spark to perform a broadcast join,
broadcasting the "very small" DataFrame itemsDf to all executors. You can explicitly suggest this to Spark via
wrapping itemsDf into a broadcast() operator. One answer option does not include this operator, so you can disregard
it. Another answer option wraps the broadcast() operator around transactionsDf – the bigger of the two DataFrames.
This answer option does not make sense in the optimization context and can likewise be disregarded.
When thinking about the broadcast() operator, you may also remember that it is a method of pyspark.sql.functions. One
answer option, however, resolves to itemsDf.broadcast([…]). The DataFrame
class has no broadcast() method, so this answer option can be eliminated as well.
All two remaining answer options resolve to transactionsDf.join([…]) in the first 2 gaps, so you will have to figure out
the details of the join now. You can pick between an outer and a left semi join. An outer join would include columns
from both DataFrames, where a left semi join only includes columns from the "left" table, here transactionsDf, just as
asked for by the question. So, the correct answer is the one that uses the left_semi join.
Question: 388
Which of the elements that are labeled with a circle and a number contain an error or are misrepresented?
A. 1, 10
B. 1, 8
C. 10
D. 7, 9, 10
E. 1, 4, 6, 9
Answer: B
Explanation:
1: Correct C This should just read "API" or "DataFrame API". The DataFrame is not part of the SQL API. To make a
DataFrame accessible via SQL, you first need to create a DataFrame view. That view can then be accessed via SQL.
4: Although "K_38_INU" looks odd, it is a completely valid name for a DataFrame column.
6: No, StringType is a correct type.
7: Although a StringType may not be the most efficient way to store a phone number, there is nothing fundamentally
wrong with using this type here.
8: Correct C TreeType is not a type that Spark supports.
9: No, Spark DataFrames support ArrayType variables. In this case, the variable would represent a sequence of
elements with type LongType, which is also a valid type for Spark DataFrames.
10: There is nothing wrong with this row.
More info: Data Types – Spark 3.1.1 Documentation (https://bit.ly/3aAPKJT)
Question: 389
Which of the following code blocks stores DataFrame itemsDf in executor memory and, if insufficient memory is
available, serializes it and saves it to disk?
A. itemsDf.persist(StorageLevel.MEMORY_ONLY)
B. itemsDf.cache(StorageLevel.MEMORY_AND_DISK)
C. itemsDf.store()
D. itemsDf.cache()
E. itemsDf.write.option(‘destination’, ‘memory’).save()
Answer: D
Explanation:
The key to solving this QUESTION NO: is knowing (or reading in the documentation) that, by default, cache() stores
values to memory and writes any partitions for which there is insufficient memory
to disk. persist() can achieve the exact same behavior, however not with the StorageLevel.MEMORY_ONLY option
listed here. It is also worth noting that cache() does not have any arguments.
If you have troubles finding the storage level information in the documentation, please also see this student Q&A
thread that sheds some light here.
Static notebook | Dynamic notebook: See test 2,
Question: 390
Which of the following code blocks can be used to save DataFrame transactionsDf to memory only, recalculating
partitions that do not fit in memory when they are needed?
A. from pyspark import StorageLevel transactionsDf.cache(StorageLevel.MEMORY_ONLY)
B. transactionsDf.cache()
C. transactionsDf.storage_level(‘MEMORY_ONLY’)
D. transactionsDf.persist()
E. transactionsDf.clear_persist()
F. from pyspark import StorageLevel transactionsDf.persist(StorageLevel.MEMORY_ONLY)
Answer: F
Explanation:
from pyspark import StorageLevel transactionsDf.persist(StorageLevel.MEMORY_ONLY) Correct. Note that the
storage level MEMORY_ONLY means that all partitions that do not fit into memory will be recomputed when they are
needed. transactionsDf.cache()
This is wrong because the default storage level of DataFrame.cache() is
MEMORY_AND_DISK, meaning that partitions that do not fit into memory are stored on disk.
transactionsDf.persist()
This is wrong because the default storage level of DataFrame.persist() is
MEMORY_AND_DISK.
transactionsDf.clear_persist()
Incorrect, since clear_persist() is not a method of DataFrame.
transactionsDf.storage_level(‘MEMORY_ONLY’)
Wrong. storage_level is not a method of DataFrame.
More info: RDD Programming Guide – Spark 3.0.0 Documentation, pyspark.sql.DataFrame.persist - PySpark 3.0.0
documentation (https://bit.ly/3sxHLVC , https://bit.ly/3j2N6B9)
Question: 391
"left_semi"
Answer: C
Explanation:
Correct code block:
transactionsDf.join(broadcast(itemsDf), "transactionId", "left_semi")
This QUESTION NO: is extremely difficult and exceeds the difficulty of questions in the exam by far.
A first indication of what is asked from you here is the remark that "the query should be executed in an optimized
way". You also have qualitative information about the size of itemsDf and transactionsDf. Given that itemsDf is "very
small" and that the execution should be optimized, you should consider instructing Spark to perform a broadcast join,
broadcasting the "very small" DataFrame itemsDf to all executors. You can explicitly suggest this to Spark via
wrapping itemsDf into a broadcast() operator. One answer option does not include this operator, so you can disregard
it. Another answer option wraps the broadcast() operator around transactionsDf – the bigger of the two DataFrames.
This answer option does not make sense in the optimization context and can likewise be disregarded.
When thinking about the broadcast() operator, you may also remember that it is a method of pyspark.sql.functions. One
answer option, however, resolves to itemsDf.broadcast([…]). The DataFrame
class has no broadcast() method, so this answer option can be eliminated as well.
All two remaining answer options resolve to transactionsDf.join([…]) in the first 2 gaps, so you will have to figure out
the details of the join now. You can pick between an outer and a left semi join. An outer join would include columns
from both DataFrames, where a left semi join only includes columns from the "left" table, here transactionsDf, just as
asked for by the question. So, the correct answer is the one that uses the left_semi join.
Question: 392
Which of the following describes tasks?
A. A task is a command sent from the driver to the executors in response to a transformation.
B. Tasks transform jobs into DAGs.
C. A task is a collection of slots.
D. A task is a collection of rows.
E. Tasks get assigned to the executors by the driver.
Answer: E
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.
Question: 393
Which of the following code blocks reads in parquet file /FileStore/imports.parquet as a
DataFrame?
A. spark.mode("parquet").read("/FileStore/imports.parquet")
B. spark.read.path("/FileStore/imports.parquet", source="parquet")
C. spark.read().parquet("/FileStore/imports.parquet")
D. spark.read.parquet("/FileStore/imports.parquet")
E. spark.read().format(‘parquet’).open("/FileStore/imports.parquet")
Answer: D
Explanation:
Static notebook | Dynamic notebook: See test 1,
Question: 394
Which of the elements that are labeled with a circle and a number contain an error or are misrepresented?
A. 1, 10
B. 1, 8
C. 10
D. 7, 9, 10
E. 1, 4, 6, 9
Answer: B
Explanation:
1: Correct C This should just read "API" or "DataFrame API". The DataFrame is not part of the SQL API. To make a
DataFrame accessible via SQL, you first need to create a DataFrame view. That view can then be accessed via SQL.
4: Although "K_38_INU" looks odd, it is a completely valid name for a DataFrame column.
6: No, StringType is a correct type.
7: Although a StringType may not be the most efficient way to store a phone number, there is nothing fundamentally
wrong with using this type here.
8: Correct C TreeType is not a type that Spark supports.
9: No, Spark DataFrames support ArrayType variables. In this case, the variable would represent a sequence of
elements with type LongType, which is also a valid type for Spark DataFrames.
10: There is nothing wrong with this row.
More info: Data Types – Spark 3.1.1 Documentation (https://bit.ly/3aAPKJT)
Question: 395
"left_semi"
Answer: C
Explanation:
Correct code block:
transactionsDf.join(broadcast(itemsDf), "transactionId", "left_semi")
This QUESTION NO: is extremely difficult and exceeds the difficulty of questions in the exam by far.
A first indication of what is asked from you here is the remark that "the query should be executed in an optimized
way". You also have qualitative information about the size of itemsDf and transactionsDf. Given that itemsDf is "very
small" and that the execution should be optimized, you should consider instructing Spark to perform a broadcast join,
broadcasting the "very small" DataFrame itemsDf to all executors. You can explicitly suggest this to Spark via
wrapping itemsDf into a broadcast() operator. One answer option does not include this operator, so you can disregard
it. Another answer option wraps the broadcast() operator around transactionsDf – the bigger of the two DataFrames.
This answer option does not make sense in the optimization context and can likewise be disregarded.
When thinking about the broadcast() operator, you may also remember that it is a method of pyspark.sql.functions. One
answer option, however, resolves to itemsDf.broadcast([…]). The DataFrame
class has no broadcast() method, so this answer option can be eliminated as well.
All two remaining answer options resolve to transactionsDf.join([…]) in the first 2 gaps, so you will have to figure out
the details of the join now. You can pick between an outer and a left semi join. An outer join would include columns
from both DataFrames, where a left semi join only includes columns from the "left" table, here transactionsDf, just as
asked for by the question. So, the correct answer is the one that uses the left_semi join.
Question: 396
Which of the elements that are labeled with a circle and a number contain an error or are misrepresented?
A. 1, 10
B. 1, 8
C. 10
D. 7, 9, 10
E. 1, 4, 6, 9
Answer: B
Explanation:
1: Correct C This should just read "API" or "DataFrame API". The DataFrame is not part of the SQL API. To make a
DataFrame accessible via SQL, you first need to create a DataFrame view. That view can then be accessed via SQL.
4: Although "K_38_INU" looks odd, it is a completely valid name for a DataFrame column.
6: No, StringType is a correct type.
7: Although a StringType may not be the most efficient way to store a phone number, there is nothing fundamentally
wrong with using this type here.
8: Correct C TreeType is not a type that Spark supports.
9: No, Spark DataFrames support ArrayType variables. In this case, the variable would represent a sequence of
elements with type LongType, which is also a valid type for Spark DataFrames.
10: There is nothing wrong with this row.
More info: Data Types – Spark 3.1.1 Documentation (https://bit.ly/3aAPKJT)

User: Natesha***** I used to be wary of online practice tests due to the misinformation and lack of proper study materials. However, Killexams.com provided truly valid questions and answers that helped me get through my exam. The first time I trusted loose online material, and I failed. But with the Killexams.com dcad exam simulator, I passed the exam, and that is the only evidence I need. Thank you, Killexams.com team.

User: Moses***** I would like to thank killexams.com for helping me pass the databricks certified associate developer for apache spark 3.0 exam with their mock exams. They were extremely beneficial, and I would definitely recommend them to anyone preparing for the databricks certified associate developer for apache spark 3.0 exam.

User: Nataliya***** During my exam preparation, the preparation kit has proven to be very useful, especially since I can sometimes go blank during exams. This is particularly challenging during timed exams like the DCAD exam, which is why I wanted to avoid failing at all costs. Thanks to the bundle I purchased, I was able to pass with flying colors and achieve a score of 100%. The kit had everything I needed to know, and all the hours I spent studying, cramming, and taking notes paid off in the end.

User: Pihu***** Thanks to killexams.com, I have received my DCAD certificate. I have used their services for all my certifications and have always been satisfied. Their exam simulator is a great reference guide, and I am now at a professional level in my field. Thanks, killexams.com!

User: James***** I purchased the dcad brain dump test from killexams.com as soon as I heard they had the most recent updates. The exam looked very fresh, and their guide and turnaround time were terrific. I was able to pass on my first try, thanks to their excellent resources.

---