EXAM ASSOCIATE-DEVELOPER-APACHE-SPARK-3.5 CRAM QUESTIONS & EXAM ASSOCIATE-DEVELOPER-APACHE-SPARK-3.5 CERTIFICATION COST

Exam Associate-Developer-Apache-Spark-3.5 Cram Questions & Exam Associate-Developer-Apache-Spark-3.5 Certification Cost

Exam Associate-Developer-Apache-Spark-3.5 Cram Questions & Exam Associate-Developer-Apache-Spark-3.5 Certification Cost

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Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions (Q26-Q31):

NEW QUESTION # 26
A data scientist wants each record in the DataFrame to contain:
The first attempt at the code does read the text files but each record contains a single line. This code is shown below:

The entire contents of a file
The full file path
The issue: reading line-by-line rather than full text per file.
Code:
corpus = spark.read.text("/datasets/raw_txt/*")
.select('*','_metadata.file_path')
Which change will ensure one record per file?
Options:

  • A. Add the option wholetext=False to the text() function
  • B. Add the option wholetext=True to the text() function
  • C. Add the option lineSep='n' to the text() function
  • D. Add the option lineSep=", " to the text() function

Answer: B

Explanation:
To read each file as a single record, use:
spark.read.text(path, wholetext=True)
This ensures that Spark reads the entire file contents into one row.
Reference:Spark read.text() with wholetext


NEW QUESTION # 27
In the code block below,aggDFcontains 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. complete
  • B. aggregate
  • C. append
  • D. replace

Answer: A

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
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
Reference: Spark Structured Streaming Programming Guide # Output Modes


NEW QUESTION # 28
Given a DataFramedfthat has 10 partitions, after running the code:
result = df.coalesce(20)
How many partitions will the result DataFrame have?

  • A. 0
  • B. 1
  • C. 2
  • D. Same number as the cluster executors

Answer: B

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
The.coalesce(numPartitions)function is used to reduce the number of partitions in a DataFrame. It does not increase the number of partitions. If the specified number of partitions is greater than the current number, it will not have any effect.
From the official Spark documentation:
"coalesce() results in a narrow dependency, e.g. if you go from 1000 partitions to 100 partitions, there will not be a shuffle, instead each of the 100 new partitions will claim one or more of the current partitions." However, if you try to increase partitions using coalesce (e.g., from 10 to 20), the number of partitions remains unchanged.
Hence,df.coalesce(20)will still return a DataFrame with 10 partitions.
Reference: Apache Spark 3.5 Programming Guide # RDD and DataFrame Operations # coalesce()


NEW QUESTION # 29
A developer notices that all the post-shuffle partitions in a dataset are smaller than the value set forspark.sql.
adaptive.maxShuffledHashJoinLocalMapThreshold.
Which type of join will Adaptive Query Execution (AQE) choose in this case?

  • A. A Cartesian join
  • B. A sort-merge join
  • C. A shuffled hash join
  • D. A broadcast nested loop join

Answer: C

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
Adaptive Query Execution (AQE) dynamically selects join strategies based on actual data sizes at runtime. If the size of post-shuffle partitions is below the threshold set by:
spark.sql.adaptive.maxShuffledHashJoinLocalMapThreshold
then Spark prefers to use a shuffled hash join.
From the Spark documentation:
"AQE selects a shuffled hash join when the size of post-shuffle data is small enough to fit within the configured threshold, avoiding more expensive sort-merge joins." Therefore:
A is wrong - Cartesian joins are only used with no join condition.
B is correct - this is the optimized join for small partitioned shuffle data under AQE.
C and D are used under other scenarios but not for this case.
Final Answer: B


NEW QUESTION # 30
A data engineer is working ona Streaming DataFrame streaming_df with the given streaming data:

Which operation is supported with streaming_df?

  • A. streaming_df.filter(col("count") < 30).show()
  • B. streaming_df.orderBy("timestamp").limit(4)
  • C. streaming_df.select(countDistinct("Name"))
  • D. streaming_df.groupby("Id").count()

Answer: D

Explanation:
Comprehensive and Detailed
Explanation:
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


NEW QUESTION # 31
......

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