Databricks-Certified-Associate-Developer-for-Apache-Spark-3.0 Exam Dumps - Databricks Certification Questions and Answers

Explanation

Correct code block:

transactionsDf.select((col("storeId").between(20, 30)) & (col("productId")==2))

Although this QUESTION NO: may make you think that it asks for a filter or where statement, it does not. It asks explicity to return a column with booleans – this should point you to the select

statement.

Another trick here is the rarely used between() method. It exists and resolves to ((storeId >= 20) AND (storeId <= 30)) in SQL. geq() and leq() do not exist.

Another riddle here is how to chain the two conditions. The only valid answer here is &. Operators like && or and are not valid. Other boolean operators that would be valid in Spark are | and.

Static notebook | Dynamic notebook: See test 1, QUESTION NO: 42 (Databricks import instructions)

Explanation

Correct code block:

transactionsDf.sample(withReplacement=False, fraction=0.15).select(avg('predError'))

You should remember that getting a random subset of rows means sampling. This, in turn should point you to the DataFrame.sample() method. Once you know this, you can look up the correct

order of arguments in the documentation (link below).

Lastly, you have to decide whether to use filter, where or select. where is just an alias for filter(). filter() is not the correct method to use here, since it would only allow you to filter rows based on

some condition. However, the QUESTION NO: asks to return only the average prediction error. You can control the columns that a query returns with the select() method – so this is the correct

method to use here.

More info: pyspark.sql.DataFrame.sample — PySpark 3.1.2 documentation

Static notebook | Dynamic notebook: See test 2, QUESTION NO: 28 (Databricks import instructions)

Explanation

Although the QUESTION NO: suggests using a method called unique() here, that method does not actually exist in PySpark. In PySpark, it is called distinct(). But then, this method is not the right

one to use here, since with distinct() we could filter out unique values in a specific column.

However, we want to return the entire rows here. So the trick is to use dropDuplicates with the subset keyword parameter. In the documentation for dropDuplicates, the examples show that subset

should be used with a list. And this is exactly the key to solving this question: The productId column needs to be fed into the subset argument in a list, even though it is just a single column.

More info: pyspark.sql.DataFrame.dropDuplicates — PySpark 3.1.1 documentation

Static notebook | Dynamic notebook: See test 1, QUESTION NO: 45 (Databricks import instructions)

Explanation

Standalone mode uses only a single executor per worker per application.

This is correct and a limitation of Spark's standalone mode.

Standalone mode is a viable solution for clusters that run multiple frameworks.

Incorrect. A limitation of standalone mode is that Apache Spark must be the only framework running on the cluster. If you would want to run multiple frameworks on the same cluster in parallel, for

example Apache Spark and Apache Flink, you would consider the YARN deployment mode.

Standalone mode uses a single JVM to run Spark driver and executor processes.

No, this is what local mode does.

Standalone mode is how Spark runs on YARN and Mesos clusters.

No. YARN and Mesos modes are two deployment modes that are different from standalone mode. These modes allow Spark to run alongside other frameworks on a cluster. When Spark is run in

standalone mode, only the Spark framework can run on the cluster.

Standalone mode means that the cluster does not contain the driver.

Incorrect, the cluster does not contain the driver in client mode, but in standalone mode the driver runs on a node in the cluster.

More info: Learning Spark, 2nd Edition, Chapter 1