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In what order should the below lines of code be run in order to read a JSON file at the file path filePath into a DataFrame with the specified schema schema? Lines of code: 1. .json(filePath, schema = schema) 2. .storesDF 3. .spark \ 4. .read() \ 5. .read \ 6. .json(filePath, format = schema)
Answer: C
Which of the following storage levels should be used to store as much data as possible in memory on two cluster nodes while storing any data that does not fit in memory on disk to be read in when needed?
Answer: D
Which of the following Spark properties is used to configure the maximum size of an automatically broadcasted DataFrame when performing a join?
Answer: B
Which of the following Spark properties is used to configure whether skewed partitions are automatically detected and subdivided into smaller partitions when joining two DataFrames together?
Answer: A
A The Spark property used to configure whether skewed partitions are automatically detected and subdivided into smaller partitions when joining two DataFrames together is `spark.sql.adaptive.skewJoin.enabled`. This feature dynamically handles skew in sort-merge join by splitting (and replicating if needed) skewed tasks into roughly evenly sized tasks. It takes effect when both `spark.sql.adaptive.enabled` and `spark.sql.adaptive.skewJoin.enabled` configurations are enabled.
Source: Adapted from community discussion — thanab
Which of the following statements about the Spark DataFrame is true?
Answer: D
Which of the following operations can be used to return a new DataFrame from DataFrame storesDF without columns that are specified by name?
Answer: C
Which of the following code blocks returns a DataFrame containing only the rows from DataFrame storesDF where the value in column sqft is less than or equal to 25,000?
Answer: D
The code block shown below should return a new DataFrame from DataFrame storesDF where column storeId is of the type string. Choose the response that correctly fills in the numbered blanks within the code block to complete this task. Code block: storesDF.__1__("storeId", __2__("storeId").__3__(__4__)
Answer: A
Which of the following code blocks returns a new DataFrame from DataFrame storesDF where column modality is the constant string "PHYSICAL"? Assume DataFrame storesDF is the only defined language variable.
Answer: C
The default value of spark.sql.shuffle.partitions is 200. Which of the following describes what that means?
Answer: E
E. By default, DataFrames will be split into 200 unique partitions when data is being shuffled. The spark.sql.shuffle.partitions configuration parameter determines the number of partitions that are used when shuffling data for joins or aggregations. The default value is 200, which means that by default, when a shuffle operation occurs, the data will be divided into 200 partitions. This allows the tasks to be distributed across the cluster and processed in parallel, improving performance. However, the optimal number of shuffle partitions depends on the specific details of your cluster and data. If the number is too small, then each partition will be large, and the tasks may take a long time to run. If the number is too large, then there will be many small tasks, and the overhead of scheduling and processing all these tasks can degrade performance. Therefore, tuning this parameter to match your specific use case can help optimize the performance of your Spark applications.
Source: Adapted from community discussion — sumand
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SPARK-DEV practice, question 141. 219 questions available.
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