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NEW QUESTION 42 Which of the following describes the conversion of a computational query into an execution plan in Spark?

• A. Depending on whether DataFrame API or SQL API are used, the physical plan may differ.
• B. The executed physical plan depends on a cost optimization from a previous stage.
• C. The catalog assigns specific resources to the physical plan.
• D. The catalog assigns specific resources to the optimized memory plan.
• E. Spark uses the catalog to resolve the optimized logical plan.

Answer: B Explanation: Explanation The executed physical plan depends on a cost optimization from a previous stage. Correct! Spark considers multiple physical plans on which it performs a cost analysis and selects the final physical plan in accordance with the lowest-cost outcome of that analysis. That final physical plan is then executed by Spark. Spark uses the catalog to resolve the optimized logical plan. No. Spark uses the catalog to resolve the unresolved logical plan, but not the optimized logical plan. Once the unresolved logical plan is resolved, it is then optimized using the Catalyst Optimizer. The optimized logical plan is the input for physical planning. The catalog assigns specific resources to the physical plan. No. The catalog stores metadata, such as a list of names of columns, data types, functions, and databases. Spark consults the catalog for resolving the references in a logical plan at the beginning of the conversion of the query into an execution plan. The result is then an optimized logical plan. Depending on whether DataFrame API or SQL API are used, the physical plan may differ. Wrong - the physical plan is independent of which API was used. And this is one of the great strengths of Spark! The catalog assigns specific resources to the optimized memory plan. There is no specific "memory plan" on the journey of a Spark computation. More info: Spark's Logical and Physical plans ... When, Why, How and Beyond. | by Laurent Leturgez | datalex | Medium   NEW QUESTION 43 Which of the following statements about executors is correct, assuming that one can consider each of the JVMs working as executors as a pool of task execution slots?

• A. There must be more slots than tasks.
• B. Tasks run in parallel via slots.
• C. An executor runs on a single core.
• D. Slot is another name for executor.
• E. There must be less executors than tasks.

Answer: B Explanation: Explanation Tasks run in parallel via slots. Correct. Given the assumption, an executor then has one or more "slots", defined by the equation spark.executor.cores / spark.task.cpus. With the executor's resources divided into slots, each task takes up a slot and multiple tasks can be executed in parallel. Slot is another name for executor. No, a slot is part of an executor. An executor runs on a single core. No, an executor can occupy multiple cores. This is set by the spark.executor.cores option. There must be more slots than tasks. No. Slots just process tasks. One could imagine a scenario where there was just a single slot for multiple tasks, processing one task at a time. Granted - this is the opposite of what Spark should be used for, which is distributed data processing over multiple cores and machines, performing many tasks in parallel. There must be less executors than tasks. No, there is no such requirement. More info: Spark Architecture | Distributed Systems Architecture (https://bit.ly/3x4MZZt)   NEW QUESTION 44 The code block displayed below contains an error. The code block should return a DataFrame in which column predErrorAdded contains the results of Python function add2ifgeq3 as applied to numeric and nullable column predError in DataFrame transactionsDf. Find the error. Code block: 1.def add2ifgeq3(x): 2. if x is None: 3. return x 4. elif x >= 3: 5. return x+2 6. return x 7. 8.add2ifgeq3udf = udf(add2ifgeq3) 9. 10.transactionsDf.withColumnRenamed("predErrorAdded", add2ifgeq3udf(col("predError")))

• A. Instead of col("predError"), the actual DataFrame with the column needs to be passed, like so transactionsDf.predError.
• B. UDFs are only available through the SQL API, but not in the Python API as shown in the code block.
• C. The udf() method does not declare a return type.
• D. The operator used to adding the column does not add column predErrorAdded to the DataFrame.
• E. The Python function is unable to handle null values, resulting in the code block crashing on execution.

Answer: D Explanation: Explanation Correct code block: def add2ifgeq3(x): if x is None: return x elif x >= 3: return x+2 return x add2ifgeq3udf = udf(add2ifgeq3) transactionsDf.withColumn("predErrorAdded", add2ifgeq3udf(col("predError"))).show() Instead of withColumnRenamed, you should use the withColumn operator. The udf() method does not declare a return type. It is fine that the udf() method does not declare a return type, this is not a required argument. However, the default return type is StringType. This may not be the ideal return type for numeric, nullable data - but the code will run without specified return type nevertheless. The Python function is unable to handle null values, resulting in the code block crashing on execution. The Python function is able to handle null values, this is what the statement if x is None does. UDFs are only available through the SQL API, but not in the Python API as shown in the code block. No, they are available through the Python API. The code in the code block that concerns UDFs is correct. Instead of col("predError"), the actual DataFrame with the column needs to be passed, like so transactionsDf.predError. You may choose to use the transactionsDf.predError syntax, but the col("predError") syntax is fine.   NEW QUESTION 45 ...... What's more, part of that Prep4sureGuide Associate-Developer-Apache-Spark dumps now are free: https://drive.google.com/open?id=1j7bFgm3RIlTQJl3Pux3onMI0paCqPZ2q