Associate-Developer-Apache-Spark-3.5시험준비공부 - Associate-Developer-Apache-Spark-3.5시험패스가능덤프자료

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지난 몇년동안 IT산업의 지속적인 발전과 성장을 통해Databricks 인증Associate-Developer-Apache-Spark-3.5시험은 IT인증시험중의 이정표로 되어 많은 인기를 누리고 있습니다. IT인증시험을ITDumpsKR덤프로 준비해야만 하는 이유는ITDumpsKR덤프는 IT업계전문가들이 실제시험문제를 연구하여 시험문제에 대비하여 예상문제를 제작했다는 점에 있습니다.

최신 Databricks Certification Associate-Developer-Apache-Spark-3.5 무료샘플문제 (Q97-Q102):

질문 # 97
What is a feature of Spark Connect?

정답：B

설명：
Comprehensive and Detailed Explanation From Exact Extract:
Spark Connect is a client-server architecture introduced in Apache Spark 3.4, designed to decouple the client from the Spark driver, enabling remote connectivity to Spark clusters.
According to the Spark 3.5.5 documentation:
"Majority of the Streaming API is supported, including DataStreamReader, DataStreamWriter, StreamingQuery and StreamingQueryListener." This indicates that Spark Connect supports key components of Structured Streaming, allowing for robust streaming data processing capabilities.
Regarding other options:
B).While Spark Connect supports DataFrame, Functions, and Column APIs, it does not support SparkContext and RDD APIs.
C).Spark Connect supports multiple languages, including PySpark and Scala, not just PySpark.
D).Spark Connect does not have built-in authentication but is designed to work seamlessly with existing authentication infrastructures.


질문 # 98
A data engineer is reviewing a Spark application that applies several transformations to a DataFrame but notices that the job does not start executing immediately.
Which two characteristics of Apache Spark's execution model explain this behavior?
Choose 2 answers:

정답：A,B

설명：
Comprehensive and Detailed Explanation From Exact Extract:
Apache Spark employs a lazy evaluation model for transformations. This means that when transformations (e.
g.,map(),filter()) are applied to a DataFrame, Spark does not execute them immediately. Instead, it builds a logical plan (lineage) of transformations to be applied.
Execution is deferred until an action (e.g.,collect(),count(),save()) is called. At that point, Spark's Catalyst optimizer analyzes the logical plan, optimizes it, and then executes the physical plan to produce the result.
This lazy evaluation strategy allows Spark to optimize the execution plan, minimize data shuffling, and improve overall performance by reducing unnecessary computations.


질문 # 99
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?

정답：C

설명：
To enable a Structured Streaming query to recover from failures or intentional shutdowns, it is essential to specify the checkpointLocation option during the writeStream operation. 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 the checkpointLocation option 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 the checkpointLocation during writeStream, Spark can maintain state information and ensure exactly-once processing semantics, which are crucial for reliable streaming applications.


질문 # 100
A developer runs:

What is the result?
Options:

정답：A

설명：
The partitionBy() method in Spark organizes output into subdirectories based on unique combinations of the specified columns:
e.g.
/path/to/output/color=red/fruit=apple/part-0000.parquet
/path/to/output/color=green/fruit=banana/part-0001.parquet
This improves query performance via partition pruning.
It does not consolidate into a single file.
Null values are allowed in partitions.
It does not "append" unless .mode("append") is used.


질문 # 101
A data engineer is working on a Streaming DataFrame streaming_df with the given streaming data:

Which operation is supported with streamingdf ?

정답：D

설명：
In Structured Streaming, only a limited subset of operations is supported due to the nature of unbounded data. Operations like sorting (orderBy) and global aggregation (countDistinct) require a full view of the dataset, which is not possible with streaming data unless specific watermarks or windows are defined.
Review of Each Option:
A: select(countDistinct("Name"))
Not allowed - Global aggregation like countDistinct() requires the full dataset and is not supported directly in streaming without watermark and windowing logic.
Reference: Databricks Structured Streaming Guide - Unsupported Operations.
B: groupby("Id").count()
Supported - Streaming aggregations over a key (like groupBy("Id")) are supported. Spark maintains intermediate state for each key.
Reference: Databricks Docs → Aggregations in Structured Streaming (https://docs.databricks.com/structured-streaming/aggregation.html) C . orderBy("timestamp").limit(4)
Not allowed - Sorting and limiting require a full view of the stream (which is infinite), so this is unsupported in streaming DataFrames.
Reference: Spark Structured Streaming - Unsupported Operations (ordering without watermark/window not allowed).
D: filter(col("count") < 30).show()
Not allowed - show() is a blocking operation used for debugging batch DataFrames; it's not allowed on streaming DataFrames.
Reference: Structured Streaming Programming Guide - Output operations like show() are not supported.
Reference Extract from Official Guide:
"Operations like orderBy, limit, show, and countDistinct are not supported in Structured Streaming because they require the full dataset to compute a result. Use groupBy(...).agg(...) instead for incremental aggregations."
- Databricks Structured Streaming Programming Guide


질문 # 102
......

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