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Databricks Certified Associate Developer for Apache Spark 3.0 Exam Sample Questions (Q155-Q160):
NEW QUESTION # 155
The code block displayed below contains an error. The code block should save DataFrame transactionsDf at path path as a parquet file, appending to any existing parquet file. Find the error.
Code block:
- A. transactionsDf.format("parquet").option("mode", "append").save(path)
- B. The code block is missing a bucketBy command that takes care of partitions.
- C. The mode option should be omitted so that the command uses the default mode.
- D. The code block is missing a reference to the DataFrameWriter.
- E. save() is evaluated lazily and needs to be followed by an action.
- F. Given that the DataFrame should be saved as parquet file, path is being passed to the wrong method.
Answer: D
Explanation:
Explanation
Correct code block:
transactionsDf.write.format("parquet").option("mode", "append").save(path)
NEW QUESTION # 156
The code block shown below should return an exact copy of DataFrame transactionsDf that does not include rows in which values in column storeId have the value 25. Choose the answer that correctly fills the blanks in the code block to accomplish this.
- A. transactionsDf.select(transactionsDf.storeId!=25)
- B. transactionsDf.where(transactionsDf.storeId!=25)
- C. transactionsDf.filter(transactionsDf.storeId==25)
- D. transactionsDf.remove(transactionsDf.storeId==25)
- E. transactionsDf.drop(transactionsDf.storeId==25)
Answer: B
Explanation:
Explanation
transactionsDf.where(transactionsDf.storeId!=25)
Correct. DataFrame.where() is an alias for the DataFrame.filter() method. Using this method, it is straightforward to filter out rows that do not have value 25 in column storeId.
transactionsDf.select(transactionsDf.storeId!=25)
Wrong. The select operator allows you to build DataFrames column-wise, but when using it as shown, it does not filter out rows.
transactionsDf.filter(transactionsDf.storeId==25)
Incorrect. Although the filter expression works for filtering rows, the == in the filtering condition is inappropriate. It should be != instead.
transactionsDf.drop(transactionsDf.storeId==25)
No. DataFrame.drop() is used to remove specific columns, but not rows, from the DataFrame.
transactionsDf.remove(transactionsDf.storeId==25)
False. There is no DataFrame.remove() operator in PySpark.
More info: pyspark.sql.DataFrame.where - PySpark 3.1.2 documentation
Static notebook | Dynamic notebook: See test 3
NEW QUESTION # 157
Which of the following is a characteristic of the cluster manager?
- A. The cluster manager transforms jobs into DAGs.
- B. The cluster manager receives input from the driver through the SparkContext.
- C. In client mode, the cluster manager runs on the edge node.
- D. Each cluster manager works on a single partition of data.
- E. The cluster manager does not exist in standalone mode.
Answer: B
Explanation:
Explanation
The cluster manager receives input from the driver through the SparkContext.
Correct. In order for the driver to contact the cluster manager, the driver launches a SparkContext. The driver then asks the cluster manager for resources to launch executors.
In client mode, the cluster manager runs on the edge node.
No. In client mode, the cluster manager is independent of the edge node and runs in the cluster.
The cluster manager does not exist in standalone mode.
Wrong, the cluster manager exists even in standalone mode. Remember, standalone mode is an easy means to deploy Spark across a whole cluster, with some limitations. For example, in standalone mode, no other frameworks can run in parallel with Spark. The cluster manager is part of Spark in standalone deployments however and helps launch and maintain resources across the cluster.
The cluster manager transforms jobs into DAGs.
No, transforming jobs into DAGs is the task of the Spark driver.
Each cluster manager works on a single partition of data.
No. Cluster managers do not work on partitions directly. Their job is to coordinate cluster resources so that they can be requested by and allocated to Spark drivers.
More info: Introduction to Core Spark Concepts * BigData
NEW QUESTION # 158
Which of the following code blocks returns a DataFrame that is an inner join of DataFrame itemsDf and DataFrame transactionsDf, on columns itemId and productId, respectively and in which every itemId just appears once?
- A. itemsDf.join(transactionsDf, itemsDf.itemId==transactionsDf.productId).dropDuplicates(["itemId"])
- B. itemsDf.join(transactionsDf, "itemsDf.itemId==transactionsDf.productId").distinct("itemId")
- C. itemsDf.join(transactionsDf, "itemsDf.itemId==transactionsDf.productId", how="inner").dropDuplicates(["itemId"])
- D. itemsDf.join(transactionsDf, itemsDf.itemId==transactionsDf.productId, how="inner").distinct(["itemId"])
- E. itemsDf.join(transactionsDf, itemsDf.itemId==transactionsDf.productId).dropDuplicates("itemId")
Answer: A
Explanation:
Explanation
Filtering out distinct rows based on columns is achieved with the dropDuplicates method, not the distinct method which does not take any arguments.
The second argument of the join() method only accepts strings if they are column names. The SQL-like statement "itemsDf.itemId==transactionsDf.productId" is therefore invalid.
In addition, it is not necessary to specify how="inner", since the default join type for the join command is already inner.
More info: pyspark.sql.DataFrame.join - PySpark 3.1.2 documentation
Static notebook | Dynamic notebook: See test 2
NEW QUESTION # 159
Which of the following statements about Spark's execution hierarchy is correct?
- A. In Spark's execution hierarchy, a job may reach over multiple stage boundaries.
- B. In Spark's execution hierarchy, manifests are one layer above jobs.
- C. In Spark's execution hierarchy, tasks are one layer above slots.
- D. In Spark's execution hierarchy, a stage comprises multiple jobs.
- E. In Spark's execution hierarchy, executors are the smallest unit.
Answer: A
Explanation:
Explanation
In Spark's execution hierarchy, a job may reach over multiple stage boundaries.
Correct. A job is a sequence of stages, and thus may reach over multiple stage boundaries.
In Spark's execution hierarchy, tasks are one layer above slots.
Incorrect. Slots are not a part of the execution hierarchy. Tasks are the lowest layer.
In Spark's execution hierarchy, a stage comprises multiple jobs.
No. It is the other way around - a job consists of one or multiple stages.
In Spark's execution hierarchy, executors are the smallest unit.
False. Executors are not a part of the execution hierarchy. Tasks are the smallest unit!
In Spark's execution hierarchy, manifests are one layer above jobs.
Wrong. Manifests are not a part of the Spark ecosystem.
NEW QUESTION # 160
......
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