My Review On Databricks Associate-Developer-Apache-Spark Exam Questions

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How to become Databricks Associate Developer Apache Spark certified

To create applications that run on the Databricks platform. You'll learn how to use the Spark shell to interact with the data stored in Databricks, the Databricks DataFrame API, and the Databricks Query Language (DQL). You'll also gain exposure to a variety of machine learning and data mining algorithms that can be used to solve real-world problems.
The Databricks Associate Developer Apache Spark certification is a great opportunity to get hands-on experience with the most popular big data platform. It's a two-day certification course that teaches you how to use the latest version of Apache Spark. Databricks Associate Developer Apache Spark exam dumps are the key of success.

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Databricks Certified Associate Developer for Apache Spark 3.0 Exam Sample Questions (Q123-Q128):

NEW QUESTION # 123
Which of the following is not a feature of Adaptive Query Execution?

A. Split skewed partitions into smaller partitions to avoid differences in partition processing time.
B. Reroute a query in case of an executor failure.
C. Coalesce partitions to accelerate data processing.
D. Replace a sort merge join with a broadcast join, where appropriate.
E. Collect runtime statistics during query execution.

Answer: B

Explanation:
Explanation
Reroute a query in case of an executor failure.
Correct. Although this feature exists in Spark, it is not a feature of Adaptive Query Execution. The cluster manager keeps track of executors and will work together with the driver to launch an executor and assign the workload of the failed executor to it (see also link below).
Replace a sort merge join with a broadcast join, where appropriate.
No, this is a feature of Adaptive Query Execution.
Coalesce partitions to accelerate data processing.
Wrong, Adaptive Query Execution does this.
Collect runtime statistics during query execution.
Incorrect, Adaptive Query Execution (AQE) collects these statistics to adjust query plans. This feedback loop is an essential part of accelerating queries via AQE.
Split skewed partitions into smaller partitions to avoid differences in partition processing time.
No, this is indeed a feature of Adaptive Query Execution. Find more information in the Databricks blog post linked below.
More info: Learning Spark, 2nd Edition, Chapter 12, On which way does RDD of spark finish fault-tolerance?
- Stack Overflow, How to Speed up SQL Queries with Adaptive Query Execution

NEW QUESTION # 124
The code block shown below should add column transactionDateForm to DataFrame transactionsDf. The column should express the unix-format timestamps in column transactionDate as string type like Apr 26 (Sunday). Choose the answer that correctly fills the blanks in the code block to accomplish this.
transactionsDf.__1__(__2__, from_unixtime(__3__, __4__))

A. 1. withColumn
2. "transactionDateForm"
3. "transactionDate"
4. "MMM d (EEEE)"
B. 1. select
2. "transactionDate"
3. "transactionDateForm"
4. "MMM d (EEEE)"
C. 1. withColumn
2. "transactionDateForm"
3. "MMM d (EEEE)"
4. "transactionDate"
D. 1. withColumnRenamed
2. "transactionDate"
3. "transactionDateForm"
4. "MM d (EEE)"
E. 1. withColumn
2. "transactionDateForm"
3. "transactionDate"
4. "MM d (EEE)"

Answer: A

Explanation:
Explanation
Correct code block:
transactionsDf.withColumn("transactionDateForm", from_unixtime("transactionDate", "MMM d (EEEE)")) The question specifically asks about "adding" a column. In the context of all presented answers, DataFrame.withColumn() is the correct command for this. In theory, DataFrame.select() could also be used for this purpose, if all existing columns are selected and a new one is added.
DataFrame.withColumnRenamed() is not the appropriate command, since it can only rename existing columns, but cannot add a new column or change the value of a column.
Once DataFrame.withColumn() is chosen, you can read in the documentation (see below) that the first input argument to the method should be the column name of the new column.
The final difficulty is the date format. The question indicates that the date format Apr 26 (Sunday) is desired. The answers give "MMM d (EEEE)" and "MM d (EEE)" as options. It can be hard to know the details of the date format that is used in Spark. Specifically, knowing the differences between MMM and MM is probably not something you deal with every day. But, there is an easy way to remember the difference: M (one letter) is usually the shortest form: 4 for April. MM includes padding: 04 for April. MMM (three letters) is the three-letter month abbreviation: Apr for April. And MMMM is the longest possible form: April. Knowing this four-letter sequence helps you select the correct option here.
More info: pyspark.sql.DataFrame.withColumn - PySpark 3.1.2 documentation Static notebook | Dynamic notebook: See test 3

NEW QUESTION # 125
Which of the following describes a valid concern about partitioning?

A. Decreasing the number of partitions reduces the overall runtime of narrow transformations if there are more executors available than partitions.
B. No data is exchanged between executors when coalesce() is run.
C. Short partition processing times are indicative of low skew.
D. The coalesce() method should be used to increase the number of partitions.
E. A shuffle operation returns 200 partitions if not explicitly set.

Answer: E

Explanation:
Explanation
A shuffle operation returns 200 partitions if not explicitly set.
Correct. 200 is the default value for the Spark property spark.sql.shuffle.partitions. This property determines how many partitions Spark uses when shuffling data for joins or aggregations.
The coalesce() method should be used to increase the number of partitions.
Incorrect. The coalesce() method can only be used to decrease the number of partitions.
Decreasing the number of partitions reduces the overall runtime of narrow transformations if there are more executors available than partitions.
No. For narrow transformations, fewer partitions usually result in a longer overall runtime, if more executors are available than partitions.
A narrow transformation does not include a shuffle, so no data need to be exchanged between executors.
Shuffles are expensive and can be a bottleneck for executing Spark workloads.
Narrow transformations, however, are executed on a per-partition basis, blocking one executor per partition.
So, it matters how many executors are available to perform work in parallel relative to the number of partitions. If the number of executors is greater than the number of partitions, then some executors are idle while other process the partitions. On the flip side, if the number of executors is smaller than the number of partitions, the entire operation can only be finished after some executors have processed multiple partitions, one after the other. To minimize the overall runtime, one would want to have the number of partitions equal to the number of executors (but not more).
So, for the scenario at hand, increasing the number of partitions reduces the overall runtime of narrow transformations if there are more executors available than partitions.
No data is exchanged between executors when coalesce() is run.
No. While coalesce() avoids a full shuffle, it may still cause a partial shuffle, resulting in data exchange between executors.
Short partition processing times are indicative of low skew.
Incorrect. Data skew means that data is distributed unevenly over the partitions of a dataset. Low skew therefore means that data is distributed evenly.
Partition processing time, the time that executors take to process partitions, can be indicative of skew if some executors take a long time to process a partition, but others do not. However, a short processing time is not per se indicative a low skew: It may simply be short because the partition is small.
A situation indicative of low skew may be when all executors finish processing their partitions in the same timeframe. High skew may be indicated by some executors taking much longer to finish their partitions than others. But the answer does not make any comparison - so by itself it does not provide enough information to make any assessment about skew.
More info: Spark Repartition & Coalesce - Explained and Performance Tuning - Spark 3.1.2 Documentation

NEW QUESTION # 126
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. transactionsDf.persist()
B. transactionsDf.cache()
C. transactionsDf.clear_persist()
D. from pyspark import StorageLevel
transactionsDf.cache(StorageLevel.MEMORY_ONLY)
E. transactionsDf.storage_level('MEMORY_ONLY')
F. from pyspark import StorageLevel
transactionsDf.persist(StorageLevel.MEMORY_ONLY)

Answer: F

Explanation:
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)

A. itemsDf.filter(~col('supplier').contains('X')).select('supplier').distinct()
B. itemsDf.filter(col(supplier).not_contains('X')).select(supplier).distinct()
C. itemsDf.filter(not(col('supplier').contains('X'))).select('supplier').unique()
D. itemsDf.filter(!col('supplier').contains('X')).select(col('supplier')).unique()
E. itemsDf.select(~col('supplier').contains('X')).distinct()

Answer: A

Explanation:
Explanation
Output of correct code block:
+-------------------+
| supplier|
+-------------------+
|Sports Company Inc.|
+-------------------+
Key to managing this question is understand which operator to use to do the opposite of an operation
- the ~ (not) operator. In addition, you should know that there is no unique() method.
Static notebook | Dynamic notebook: See test 1

NEW QUESTION # 128
......

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