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Databricks Certified Associate Developer for Apache Spark 3.0 Practice Test

DCAD test Format | Course Contents | Course Outline | test Syllabus | test Objectives

Exam Details for DCAD Databricks Certified Associate Developer for Apache Spark 3.0:

Number of Questions: The test consists of approximately 60 multiple-choice and multiple-select questions.

Time Limit: The total time allocated for the test is 90 minutes (1 hour and 30 minutes).

Passing Score: To pass the exam, you must achieve a minimum score of 70%.

Exam Format: The test is conducted online and is proctored. You will be required to answer the questions within the allocated time frame.

Course Outline:

1. Spark Basics:
- Understanding Apache Spark architecture and components
- Working with RDDs (Resilient Distributed Datasets)
- Transformations and actions in Spark

2. Spark SQL:
- Working with structured data using Spark SQL
- Writing and executing SQL queries in Spark
- DataFrame operations and optimizations

3. Spark Streaming:
- Real-time data processing with Spark Streaming
- Windowed operations and time-based transformations
- Integration with external systems and sources

4. Spark Machine Learning (MLlib):
- Introduction to machine learning with Spark MLlib
- Feature extraction and transformation in Spark MLlib
- Model training and evaluation using Spark MLlib

5. Spark Graph Processing (GraphX):
- Working with graph data in Spark using GraphX
- Graph processing algorithms and operations
- Analyzing and visualizing graph data in Spark

6. Spark Performance Tuning and Optimization:
- Identifying and resolving performance bottlenecks in Spark applications
- Spark configuration and tuning techniques
- Optimization strategies for Spark data processing

Exam Objectives:

1. Understand the fundamentals of Apache Spark and its components.
2. Perform data processing and transformations using RDDs.
3. Utilize Spark SQL for structured data processing and querying.
4. Implement real-time data processing using Spark Streaming.
5. Apply machine learning techniques with Spark MLlib.
6. Analyze and process graph data using Spark GraphX.
7. Optimize and tune Spark applications for improved performance.

Exam Syllabus:

The test syllabus covers the following topics:

1. Spark Basics
- Apache Spark architecture and components
- RDDs (Resilient Distributed Datasets)
- Transformations and actions in Spark

2. Spark SQL
- Spark SQL and structured data processing
- SQL queries and DataFrame operations
- Spark SQL optimizations

3. Spark Streaming
- Real-time data processing with Spark Streaming
- Windowed operations and time-based transformations
- Integration with external systems

4. Spark Machine Learning (MLlib)
- Introduction to machine learning with Spark MLlib
- Feature extraction and transformation
- Model training and evaluation

5. Spark Graph Processing (GraphX)
- Graph data processing in Spark using GraphX
- Graph algorithms and operations
- Graph analysis and visualization

6. Spark Performance Tuning and Optimization
- Performance bottlenecks and optimization techniques
- Spark configuration and tuning
- Optimization strategies for data processing

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killexams.com Databricks DCAD
Databricks Certified Associate Developer for Apache Spark 3.0
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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?
1. transactionsDf.dropna("any")
2. transactionsDf.dropna(thresh=4)
3. transactionsDf.drop.na("",2)
4. transactionsDf.dropna(thresh=2)
5. 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 test 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
1. 1, 8
2. 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?
1. itemsDf.persist(StorageLevel.MEMORY_ONLY)
2. itemsDf.cache(StorageLevel.MEMORY_AND_DISK)
3. itemsDf.store()
4. itemsDf.cache()
5. itemsDf.write.option(�destination�, �memory�).save()
Answer: D Explanation:
The key to solving this QUESTION NO: is knowing (or memorizing 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?
1. from pyspark import StorageLevel transactionsDf.cache(StorageLevel.MEMORY_ONLY)
2. transactionsDf.cache()
3. transactionsDf.storage_level(�MEMORY_ONLY�)
4. transactionsDf.persist()
5. transactionsDf.clear_persist()
6. 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 test 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?
1. A task is a command sent from the driver to the executors in response to a transformation.
2. Tasks transform jobs into DAGs.
3. A task is a collection of slots.
4. A task is a collection of rows.
5. 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?
1. spark.mode("parquet").read("/FileStore/imports.parquet")
2. spark.read.path("/FileStore/imports.parquet", source="parquet")
3. spark.read().parquet("/FileStore/imports.parquet")
4. spark.read.parquet("/FileStore/imports.parquet")
5. 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
1. 1, 8
2. 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:
transactionsDf.join(broadcast(itemsDf), "transactionId", "left_semi")
This QUESTION NO: is extremely difficult and exceeds the difficulty of questions in the test 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
1. 1, 8
2. 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.
More info: Data Types � Spark 3.1.1 Documentation (https://bit.ly/3aAPKJT)

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Databricks Certified Associate Developer for Apache Spark 3.0 Practice Test

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