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Databricks Certified Associate Developer for Apache Spark 3.0 Exam Sample Questions (Q174-Q179):

NEW QUESTION # 174
Which of the following code blocks selects all rows from DataFrame transactionsDf in which column productId is zero or smaller or equal to 3?

A. transactionsDf.filter(col("productId")==3 | col("productId")<1)
B. transactionsDf.where("productId"=3).or("productId"<1))
C. transactionsDf.filter(productId==3 or productId<1)
D. transactionsDf.filter((col("productId")==3) or (col("productId")<1))
E. transactionsDf.filter((col("productId")==3) | (col("productId")<1))

Answer: E

Explanation:
Explanation
This question targets your knowledge about how to chain filtering conditions. Each filtering condition should be in parentheses. The correct operator for "or" is the pipe character (|) and not the word or. Another operator of concern is the equality operator. For the purpose of comparison, equality is expressed as two equal signs (==).
Static notebook | Dynamic notebook: See test 2

NEW QUESTION # 175
Which of the following statements about lazy evaluation is incorrect?

A. Spark will fail a job only during execution, but not during definition.
B. Lineages allow Spark to coalesce transformations into stages
C. Execution is triggered by transformations.
D. Accumulators do not change the lazy evaluation model of Spark.
E. Predicate pushdown is a feature resulting from lazy evaluation.

Answer: C

Explanation:
Explanation
Execution is triggered by transformations.
Correct. Execution is triggered by actions only, not by transformations.
Lineages allow Spark to coalesce transformations into stages.
Incorrect. In Spark, lineage means a recording of transformations. This lineage enables lazy evaluation in Spark.
Predicate pushdown is a feature resulting from lazy evaluation.
Wrong. Predicate pushdown means that, for example, Spark will execute filters as early in the process as possible so that it deals with the least possible amount of data in subsequent transformations, resulting in a performance improvements.
Accumulators do not change the lazy evaluation model of Spark.
Incorrect. In Spark, accumulators are only updated when the query that refers to the is actually executed. In other words, they are not updated if the query is not (yet) executed due to lazy evaluation.
Spark will fail a job only during execution, but not during definition.
Wrong. During definition, due to lazy evaluation, the job is not executed and thus certain errors, for example reading from a non-existing file, cannot be caught. To be caught, the job needs to be executed, for example through an action.

NEW QUESTION # 176
Which of the following code blocks returns about 150 randomly selected rows from the 1000-row DataFrame transactionsDf, assuming that any row can appear more than once in the returned DataFrame?

A. transactionsDf.sample(0.85, 8429)
B. transactionsDf.sample(True, 0.15, 8261)
C. transactionsDf.resample(0.15, False, 3142)
D. transactionsDf.sample(0.15)
E. transactionsDf.sample(0.15, False, 3142)

Answer: B

Explanation:
Explanation
Answering this question correctly depends on whether you understand the arguments to the DataFrame.sample() method (link to the documentation below). The arguments are as follows:
DataFrame.sample(withReplacement=None, fraction=None, seed=None).
The first argument withReplacement specified whether a row can be drawn from the DataFrame multiple times. By default, this option is disabled in Spark. But we have to enable it here, since the question asks for a row being able to appear more than once. So, we need to pass True for this argument.
About replacement: "Replacement" is easiest explained with the example of removing random items from a box. When you remove those "with replacement" it means that after you have taken an item out of the box, you put it back inside. So, essentially, if you would randomly take 10 items out of a box with 100 items, there is a chance you take the same item twice or more times. "Without replacement" means that you would not put the item back into the box after removing it. So, every time you remove an item from the box, there is one less item in the box and you can never take the same item twice.
The second argument to the withReplacement method is fraction. This referes to the fraction of items that should be returned. In the question we are asked for 150 out of 1000 items - a fraction of 0.15.
The last argument is a random seed. A random seed makes a randomized processed repeatable. This means that if you would re-run the same sample() operation with the same random seed, you would get the same rows returned from the sample() command. There is no behavior around the random seed specified in the question. The varying random seeds are only there to confuse you!
More info: pyspark.sql.DataFrame.sample - PySpark 3.1.1 documentation
Static notebook | Dynamic notebook: See test 1

NEW QUESTION # 177
The code block displayed below contains an error. The code block should return DataFrame transactionsDf, but with the column storeId renamed to storeNumber. Find the error.
Code block:
transactionsDf.withColumn("storeNumber", "storeId")

A. Argument "storeId" should be the first and argument "storeNumber" should be the second argument to the withColumn method.
B. Instead of withColumn, the withColumnRenamed method should be used.
C. Instead of withColumn, the withColumnRenamed method should be used and argument "storeId" should be the first and argument "storeNumber" should be the second argument to that method.
D. Arguments "storeNumber" and "storeId" each need to be wrapped in a col() operator.
E. The withColumn operator should be replaced with the copyDataFrame operator.

Answer: C

Explanation:
Explanation
Correct code block:
transactionsDf.withColumnRenamed("storeId", "storeNumber")
More info: pyspark.sql.DataFrame.withColumnRenamed - PySpark 3.1.1 documentation Static notebook | Dynamic notebook: See test 1

NEW QUESTION # 178
Which of the following describes Spark's Adaptive Query Execution?

A. Adaptive Query Execution applies to all kinds of queries.
B. Adaptive Query Execution features include dynamically coalescing shuffle partitions, dynamically injecting scan filters, and dynamically optimizing skew joins.
C. Adaptive Query Execution features are dynamically switching join strategies and dynamically optimizing skew joins.
D. Adaptive Query Execution reoptimizes queries at execution points.
E. Adaptive Query Execution is enabled in Spark by default.

Answer: C

Explanation:
Explanation
Adaptive Query Execution features include dynamically coalescing shuffle partitions, dynamically injecting scan filters, and dynamically optimizing skew joins.
This is almost correct. All of these features, except for dynamically injecting scan filters, are part of Adaptive Query Execution. Dynamically injecting scan filters for join operations to limit the amount of data to be considered in a query is part of Dynamic Partition Pruning and not of Adaptive Query Execution.
Adaptive Query Execution reoptimizes queries at execution points.
No, Adaptive Query Execution reoptimizes queries at materialization points.
Adaptive Query Execution is enabled in Spark by default.
No, Adaptive Query Execution is disabled in Spark needs to be enabled through the spark.sql.adaptive.enabled property.
Adaptive Query Execution applies to all kinds of queries.
No, Adaptive Query Execution applies only to queries that are not streaming queries and that contain at least one exchange (typically expressed through a join, aggregate, or window operator) or one subquery.
More info: How to Speed up SQL Queries with Adaptive Query Execution, Learning Spark, 2nd Edition, Chapter 12 (https://bit.ly/3tOh8M1)

NEW QUESTION # 179
......

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