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DumpStillValid Databricks Associate-Developer-Apache-Spark-3.5 Questions PDF Format

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Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions (Q34-Q39):

NEW QUESTION # 34
A developer needs to produce a Python dictionary using data stored in a small Parquet table, which looks like this:

The resulting Python dictionary must contain a mapping of region-> region id containing the smallest 3 region_idvalues.
Which code fragment meets the requirements?
A)

B)

C)

D)

The resulting Python dictionary must contain a mapping ofregion -> region_idfor the smallest
3region_idvalues.
Which code fragment meets the requirements?

A. regions = dict(
regions_df
.select('region_id', 'region')
.limit(3)
.collect()
)
B. regions = dict(
regions_df
.select('region_id', 'region')
.sort('region_id')
.take(3)
)
C. regions = dict(
regions_df
.select('region', 'region_id')
.sort('region_id')
.take(3)
)
D. regions = dict(
regions_df
.select('region', 'region_id')
.sort(desc('region_id'))
.take(3)
)

Answer: C

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
The question requires creating a dictionary where keys areregionvalues and values are the correspondingregion_idintegers. Furthermore, it asks to retrieve only the smallest 3region_idvalues.
Key observations:
select('region', 'region_id')puts the column order as expected bydict()- where the first column becomes the key and the second the value.
sort('region_id')ensures sorting in ascending order so the smallest IDs are first.
take(3)retrieves exactly 3 rows.
Wrapping the result indict(...)correctly builds the required Python dictionary:{ 'AFRICA': 0, 'AMERICA': 1,
'ASIA': 2 }.
Incorrect options:
Option B flips the order toregion_idfirst, resulting in a dictionary with integer keys - not what's asked.
Option C uses.limit(3)without sorting, which leads to non-deterministic rows based on partition layout.
Option D sorts in descending order, giving the largest rather than smallestregion_ids.
Hence, Option A meets all the requirements precisely.

NEW QUESTION # 35
A data engineer needs to write a Streaming DataFrame as Parquet files.
Given the code:

Which code fragment should be inserted to meet the requirement?
A)

B)

C)

D)

Which code fragment should be inserted to meet the requirement?

A. .format("parquet")
.option("path", "path/to/destination/dir")
B. .format("parquet")
.option("location", "path/to/destination/dir")
C. .option("format", "parquet")
.option("location", "path/to/destination/dir")
D. CopyEdit
.option("format", "parquet")
.option("destination", "path/to/destination/dir")

Answer: A

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
To write a structured streaming DataFrame to Parquet files, the correct way to specify the format and output directory is:
writeStream
format("parquet")
option("path", "path/to/destination/dir")
According to Spark documentation:
"When writing to file-based sinks (like Parquet), you must specify the path using the .option("path", ...) method. Unlike batch writes, .save() is not supported." Option A incorrectly uses.option("location", ...)(invalid for Parquet sink).
Option B incorrectly sets the format via.option("format", ...), which is not the correct method.
Option C repeats the same issue.
Option D is correct:.format("parquet")+.option("path", ...)is the required syntax.
Final Answer: D

NEW QUESTION # 36
A data engineer has been asked to produce a Parquet table which is overwritten every day with the latest data.
The downstream consumer of this Parquet table has a hard requirement that the data in this table is produced with all records sorted by themarket_timefield.
Which line of Spark code will produce a Parquet table that meets these requirements?

A. final_df
.sortWithinPartitions("market_time")
.write
.format("parquet")
.mode("overwrite")
.saveAsTable("output.market_events")
B. final_df
.sort("market_time")
.coalesce(1)
.write
.format("parquet")
.mode("overwrite")
.saveAsTable("output.market_events")
C. final_df
.orderBy("market_time")
.write
.format("parquet")
.mode("overwrite")
.saveAsTable("output.market_events")
D. final_df
.sort("market_time")
.write
.format("parquet")
.mode("overwrite")
.saveAsTable("output.market_events")

Answer: A

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
To ensure that data written out to disk is sorted, it is important to consider how Spark writes data when saving to Parquet tables. The methods.sort()or.orderBy()apply a global sort but do not guarantee that the sorting will persist in the final output files unless certain conditions are met (e.g. a single partition via.coalesce(1)- which is not scalable).
Instead, the proper method in distributed Spark processing to ensure rows are sorted within their respective partitions when written out is:
sortWithinPartitions("column_name")
According to Apache Spark documentation:
"sortWithinPartitions()ensures each partition is sorted by the specified columns. This is useful for downstream systems that require sorted files." This method works efficiently in distributed settings, avoids the performance bottleneck of global sorting (as in.orderBy()or.sort()), and guarantees each output partition has sorted records - which meets the requirement of consistently sorted data.
Thus:
Option A and B do not guarantee the persisted file contents are sorted.
Option C introduces a bottleneck via.coalesce(1)(single partition).
Option D correctly applies sorting within partitions and is scalable.
Reference: Databricks & Apache Spark 3.5 Documentation # DataFrame API # sortWithinPartitions()

NEW QUESTION # 37
A Spark application suffers from too many small tasks due to excessive partitioning. How can this be fixed without a full shuffle?
Options:

A. Use the sortBy() transformation to reorganize the data
B. Use the coalesce() transformation with a lower number of partitions
C. Use the repartition() transformation with a lower number of partitions
D. Use the distinct() transformation to combine similar partitions

Answer: B

Explanation:
coalesce(n) reduces the number of partitions without triggering a full shuffle, unlike repartition().
This is ideal when reducing partition count, especially during write operations.
Reference:Spark API - coalesce

NEW QUESTION # 38
An engineer has two DataFrames: df1 (small) and df2 (large). A broadcast join is used:
python
CopyEdit
frompyspark.sql.functionsimportbroadcast
result = df2.join(broadcast(df1), on='id', how='inner')
What is the purpose of using broadcast() in this scenario?
Options:

A. It filters the id values before performing the join.
B. It reduces the number of shuffle operations by replicating the smaller DataFrame to all nodes.
C. It ensures that the join happens only when the id values are identical.
D. It increases the partition size for df1 and df2.

Answer: B

Explanation:
broadcast(df1) tells Spark to send the small DataFrame (df1) to all worker nodes.
This eliminates the need for shuffling df1 during the join.
Broadcast joins are optimized for scenarios with one large and one small table.
Reference:Spark SQL Performance Tuning Guide - Broadcast Joins

NEW QUESTION # 39
......

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David Harris David Harris

Biography

DumpStillValid Databricks Associate-Developer-Apache-Spark-3.5 Questions PDF Format

Preparation Associate-Developer-Apache-Spark-3.5 Store - Valid Associate-Developer-Apache-Spark-3.5 Test Vce

Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions (Q34-Q39):

Associate-Developer-Apache-Spark-3.5 Discount Code｜High Pass Rate｜100%

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