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In the code block below, aggDF contains aggregations on a streaming DataFrame:
Which output mode at line 3 ensures that the entire result table is written to the console during each trigger execution?
A. aggregate
B. complete
C. replace
D. append
Answer: B
Explanation:
The correct output mode for streaming aggregations that need to output the full updated results at each trigger is "complete".
From the official documentation:
"complete: The entire updated result table will be output to the sink every time there is a trigger." This is ideal for aggregations, such as counts or averages grouped by a key, where the result table changes incrementally over time.
append: only outputs newly added rows
replace and aggregate: invalid values for output mode
NEW QUESTION # 85
A data engineer is building a Structured Streaming pipeline and wants the pipeline to recover from failures or intentional shutdowns by continuing where the pipeline left off.
How can this be achieved?
A. By configuring the optioncheckpointLocationduringreadStream
B. By configuring the optionrecoveryLocationduring the SparkSession initialization
C. By configuring the optionrecoveryLocationduringwriteStream
D. By configuring the optioncheckpointLocationduringwriteStream
Answer: D
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
To enable a Structured Streaming query to recover from failures or intentional shutdowns, it is essential to specify thecheckpointLocationoption during thewriteStreamoperation. This checkpoint location stores the progress information of the streaming query, allowing it to resume from where it left off.
According to the Databricks documentation:
"You must specify thecheckpointLocationoption before you run a streaming query, as in the following example:
option("checkpointLocation", "/path/to/checkpoint/dir")
toTable("catalog.schema.table")
- Databricks Documentation: Structured Streaming checkpoints
By setting thecheckpointLocationduringwriteStream, Spark can maintain state information and ensure exactly- once processing semantics, which are crucial for reliable streaming applications.
NEW QUESTION # 86
A Spark engineer must select an appropriate deployment mode for the Spark jobs.
What is the benefit of using cluster mode in Apache Spark™?
A. In cluster mode, the driver program runs on one of the worker nodes, allowing the application to fully utilize the distributed resources of the cluster.
B. In cluster mode, the driver is responsible for executing all tasks locally without distributing them across the worker nodes.
C. In cluster mode, resources are allocated from a resource manager on the cluster, enabling better performance and scalability for large jobs
D. In cluster mode, the driver runs on the client machine, which can limit the application's ability to handle large datasets efficiently.
Answer: A
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
In Apache Spark's cluster mode:
"The driver program runs on the cluster's worker node instead of the client's local machine. This allows the driver to be close to the data and other executors, reducing network overhead and improving fault tolerance for production jobs." (Source: Apache Spark documentation -Cluster Mode Overview) This deployment is ideal for production environments where the job is submitted from a gateway node, and Spark manages the driver lifecycle on the cluster itself.
Option A is partially true but less specific than D.
Option B is incorrect: the driver never executes all tasks; executors handle distributed tasks.
Option C describes client mode, not cluster mode.
NEW QUESTION # 87
The following code fragment results in an error:
@F.udf(T.IntegerType())
def simple_udf(t: str) -> str:
return answer * 3.14159
Which code fragment should be used instead?
A. @F.udf(T.IntegerType())
def simple_udf(t: float) -> float:
return t * 3.14159
B. @F.udf(T.IntegerType())
def simple_udf(t: int) -> int:
return t * 3.14159
C. @F.udf(T.DoubleType())
def simple_udf(t: int) -> int:
return t * 3.14159
D. @F.udf(T.DoubleType())
def simple_udf(t: float) -> float:
return t * 3.14159
Answer: D
Explanation:
Comprehensive and Detailed Explanation:
The original code has several issues:
It references a variable answer that is undefined.
The function is annotated to return a str, but the logic attempts numeric multiplication.
The UDF return type is declared as T.IntegerType() but the function performs a floating-point operation, which is incompatible.
Option B correctly:
Uses DoubleType to reflect the fact that the multiplication involves a float (3.14159).
Declares the input as float, which aligns with the multiplication.
Returns a float, which matches both the logic and the schema type annotation.
This structure aligns with how PySpark expects User Defined Functions (UDFs) to be declared:
"To define a UDF you must specify a Python function and provide the return type using the relevant Spark SQL type (e.g., DoubleType for float results)." Example from official documentation:
from pyspark.sql.functions import udf
from pyspark.sql.types import DoubleType
@udf(returnType=DoubleType())
def multiply_by_pi(x: float) -> float:
return x * 3.14159
This makes Option B the syntactically and semantically correct choice.
NEW QUESTION # 88
A data engineer wants to write a Spark job that creates a new managed table. If the table already exists, the job should fail and not modify anything.
Which save mode and method should be used?
A. save with mode Ignore
B. saveAsTable with mode Overwrite
C. save with mode ErrorIfExists
D. saveAsTable with mode ErrorIfExists
Answer: D
Explanation:
Comprehensive and Detailed Explanation:
The methodsaveAsTable()creates a new table and optionally fails if the table exists.
From Spark documentation:
"The mode 'ErrorIfExists' (default) will throw an error if the table already exists." Thus:
Option A is correct.
Option B (Overwrite) would overwrite existing data - not acceptable here.
Option C and D usesave(), which doesn't create a managed table with metadata in the metastore.
Final Answer: A
NEW QUESTION # 89
......
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