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As the name suggests, it is a special exam that is designed to help the candidates who want to get the job as an Associate Developer in the company, Databricks. The exam is conducted by the company itself and the candidates can register themselves for the exam. The candidates have to prepare for the exam with the help of the given syllabus and the study material. The candidate should have a good knowledge of the concepts related to the big data and the candidates should have a good knowledge of the programming language like Java, Python and R. The candidates can also check the sample papers and the past papers to know about the level of difficulty. Databricks Associate Developer Apache Spark exam dumps will help you prepare for this exam. Apache Spark is a powerful open source data processing engine that provides a unified platform for data analytics, machine learning, and streaming applications. Spark is used to process massive datasets to find patterns and trends in the data, as well as perform data transformations, analyses, and visualizations. The big data industry is growing rapidly, and companies of all sizes are increasingly adopting Spark to analyze their large datasets. In this article, we will discuss about Databricks Associate Developer Apache Spark Exam and how it can help you to become an expert in the world of Big Data.

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Databricks Certified Associate Developer for Apache Spark 3.0 Exam Sample Questions (Q114-Q119):

NEW QUESTION # 114
Which of the following code blocks creates a new one-column, two-row DataFrame dfDates with column date of type timestamp?

• A. 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"))
• B. 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"))
• C. 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"))
• D. 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"))
• E. 1.dfDates = spark.createDataFrame([("23/01/2022 11:28:12",),("24/01/2022 10:58:34",)], ["date"])

Answer: D Explanation:
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 totimestamp 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", totimestamp("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", totimestamp("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", todatetime("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 # 115
Which of the following code blocks returns a copy of DataFrame transactionsDf in which column productId has been renamed to productNumber?

• A. transactionsDf.withColumnRenamed("productId", "productNumber")
• B. transactionsDf.withColumnRenamed("productNumber", "productId")
• C. transactionsDf.withColumn("productId", "productNumber")
• D. transactionsDf.withColumnRenamed(col(productId), col(productNumber))
• E. transactionsDf.withColumnRenamed(productId, productNumber)

Answer: A Explanation:
Explanation
More info: pyspark.sql.DataFrame.withColumnRenamed - PySpark 3.1.2 documentation Static notebook | Dynamic notebook: See test 2
NEW QUESTION # 116
Which of the following code blocks returns a DataFrame that has all columns of DataFrame transactionsDf and an additional column predErrorSquared which is the squared value of column predError in DataFrame transactionsDf?

• A. transactionsDf.withColumn("predError", pow(col("predErrorSquared"), 2))
• B. transactionsDf.withColumn("predErrorSquared", pow(col("predError"), lit(2)))
• C. transactionsDf.withColumn("predErrorSquared", pow(predError, lit(2)))
• D. transactionsDf.withColumnRenamed("predErrorSquared", pow(predError, 2))
• E. transactionsDf.withColumn("predErrorSquared", "predError"**2)

Answer: B Explanation:
Explanation
While only one of these code blocks works, the DataFrame API is pretty flexible when it comes to accepting columns into the pow() method. The following code blocks would also work:
transactionsDf.withColumn("predErrorSquared", pow("predError", 2))
transactionsDf.withColumn("predErrorSquared", pow("predError", lit(2))) Static notebook | Dynamic notebook: See test 1 (https://flrs.github.io/sparkpracticetestscode/#1/26.html ,
https://bit.ly/sparkpracticeexamsimport_instructions)
NEW QUESTION # 117
Which of the following describes a shuffle?

• A. A shuffle is a process that compares data across executors.
• B. A shuffle is a Spark operation that results from DataFrame.coalesce().
• C. A shuffle is a process that compares data across partitions.
• D. A shuffle is a process that is executed during a broadcast hash join.
• E. A shuffle is a process that allocates partitions to executors.

Answer: C Explanation:
Explanation
A shuffle is a Spark operation that results from DataFrame.coalesce().
No. DataFrame.coalesce() does not result in a shuffle.
A shuffle is a process that allocates partitions to executors.
This is incorrect.
A shuffle is a process that is executed during a broadcast hash join.
No, broadcast hash joins avoid shuffles and yield performance benefits if at least one of the two tables is small in size (<= 10 MB by default). Broadcast hash joins can avoid shuffles because instead of exchanging partitions between executors, they broadcast a small table to all executors that then perform the rest of the join operation locally.
A shuffle is a process that compares data across executors.
No, in a shuffle, data is compared across partitions, and not executors.
More info: Spark Repartition & Coalesce - Explained (https://bit.ly/32KF7zS)
NEW QUESTION # 118
The code block displayed below contains an error. The code block should trigger Spark to cache DataFrame transactionsDf in executor memory where available, writing to disk where insufficient executor memory is available, in a fault-tolerant way. Find the error.
Code block:
transactionsDf.persist(StorageLevel.MEMORYANDDISK)

• A. The code block uses the wrong operator for caching.
• B. Data caching capabilities can be accessed through the spark object, but not through the DataFrame API.
• C. The DataFrameWriter needs to be invoked.
• D. Caching is not supported in Spark, data are always recomputed.
• E. The storage level is inappropriate for fault-tolerant storage.

Answer: E Explanation:
Explanation
The storage level is inappropriate for fault-tolerant storage.
Correct. Typically, when thinking about fault tolerance and storage levels, you would want to store redundant copies of the dataset. This can be achieved by using a storage level such as StorageLevel.MEMORYANDDISK_2.
The code block uses the wrong command for caching.
Wrong. In this case, DataFrame.persist() needs to be used, since this operator supports passing a storage level.
DataFrame.cache() does not support passing a storage level.
Caching is not supported in Spark, data are always recomputed.
Incorrect. Caching is an important component of Spark, since it can help to accelerate Spark programs to great extent. Caching is often a good idea for datasets that need to be accessed repeatedly.
Data caching capabilities can be accessed through the spark object, but not through the DataFrame API.
No. Caching is either accessed through DataFrame.cache() or DataFrame.persist().
The DataFrameWriter needs to be invoked.
Wrong. The DataFrameWriter can be accessed via DataFrame.write and is used to write data to external data stores, mostly on disk. Here, we find keywords such as "cache" and "executor memory" that point us away from using external data stores. We aim to save data to memory to accelerate the reading process, since reading from disk is comparatively slower. The DataFrameWriter does not write to memory, so we cannot use it here.
More info: Best practices for caching in Spark SQL | by David Vrba | Towards Data Science
NEW QUESTION # 119
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