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100% Pass 2025 Updated Databricks Associate-Developer-Apache-Spark-3.5: Exam Dumps Databricks Certified Associate Developer for Apache Spark 3.5 - Python Zip

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Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions (Q60-Q65):

NEW QUESTION # 60
A developer wants to refactor some older Spark code to leverage built-in functions introduced in Spark 3.5.0.
The existing code performs array manipulations manually. Which of the following code snippets utilizes new built-in functions in Spark 3.5.0 for array operations?

A. result_df = prices_df
.agg(F.min("spot_price"), F.max("spot_price"))
B. result_df = prices_df
.agg(F.count_if(F.col("spot_price") >= F.lit(min_price)))
C. result_df = prices_df
.withColumn("valid_price", F.when(F.col("spot_price") > F.lit(min_price), 1).otherwise(0))
D. result_df = prices_df
.agg(F.count("spot_price").alias("spot_price"))
.filter(F.col("spot_price") > F.lit("min_price"))

Answer: B

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
The correct answer isBbecause it uses the new function count_if, introduced in Spark 3.5.0, which simplifies conditional counting within aggregations.
* F.count_if(condition) counts the number of rows that meet the specified boolean condition.
* In this example, it directly counts how many times spot_price >= min_price evaluates to true, replacing the older verbose combination of when/otherwise and filtering or summing.
Official Spark 3.5.0 documentation notes the addition of count_if to simplify this kind of logic:
"Added count_if aggregate function to count only the rows where a boolean condition holds (SPARK-
43773)."
Why other options are incorrect or outdated:
* Auses a legacy-style method of adding a flag column (when().otherwise()), which is verbose compared to count_if.
* Cperforms a simple min/max aggregation-useful but unrelated to conditional array operations or the updated functionality.
* Dincorrectly applies .filter() after .agg() which will cause an error, and misuses string "min_price" rather than the variable.
Therefore,Bis the only option leveraging new functionality from Spark 3.5.0 correctly and efficiently.

NEW QUESTION # 61
What is the risk associated with this operation when converting a large Pandas API on Spark DataFrame back to a Pandas DataFrame?

A. The conversion will automatically distribute the data across worker nodes
B. The operation will fail if the Pandas DataFrame exceeds 1000 rows
C. Data will be lost during conversion
D. The operation will load all data into the driver's memory, potentially causing memory overflow

Answer: D

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
When you convert a largepyspark.pandas(aka Pandas API on Spark) DataFrame to a local Pandas DataFrame using.toPandas(), Spark collects all partitions to the driver.
From the Spark documentation:
"Be careful when converting large datasets to Pandas. The entire dataset will be pulled into the driver's memory." Thus, for large datasets, this can cause memory overflow or out-of-memory errors on the driver.
Final Answer: D

NEW QUESTION # 62
A Data Analyst is working on the DataFramesensor_df, which contains two columns:
Which code fragment returns a DataFrame that splits therecordcolumn into separate columns and has one array item per row?
A)

A. exploded_df = exploded_df.select("record_datetime", "record_exploded")
B. exploded_df = sensor_df.withColumn("record_exploded", explode("record")) exploded_df = exploded_df.select("record_datetime", "sensor_id", "status", "health")
C. exploded_df = exploded_df.select(
"record_datetime",
"record_exploded.sensor_id",
"record_exploded.status",
"record_exploded.health"
)
exploded_df = sensor_df.withColumn("record_exploded", explode("record"))
D. exploded_df = exploded_df.select(
"record_datetime",
"record_exploded.sensor_id",
"record_exploded.status",
"record_exploded.health"
)
exploded_df = sensor_df.withColumn("record_exploded", explode("record"))

Answer: C

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
To flatten an array of structs into individual rows and access fields within each struct, you must:
Useexplode()to expand the array so each struct becomes its own row.
Access the struct fields via dot notation (e.g.,record_exploded.sensor_id).
Option C does exactly that:
First, explode therecordarray column into a new columnrecord_exploded.
Then, access fields of the struct using the dot syntax inselect.
This is standard practice in PySpark for nested data transformation.
Final Answer: C

NEW QUESTION # 63
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
.sort("market_time")
.write
.format("parquet")
.mode("overwrite")
.saveAsTable("output.market_events")
B. final_df
.orderBy("market_time")
.write
.format("parquet")
.mode("overwrite")
.saveAsTable("output.market_events")
C. final_df
.sort("market_time")
.coalesce(1)
.write
.format("parquet")
.mode("overwrite")
.saveAsTable("output.market_events")
D. final_df
.sortWithinPartitions("market_time")
.write
.format("parquet")
.mode("overwrite")
.saveAsTable("output.market_events")

Answer: D

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 # 64
A data engineer observes that an upstream streaming source sends duplicate records, where duplicates share the same key and have at most a 30-minute difference inevent_timestamp. The engineer adds:
dropDuplicatesWithinWatermark("event_timestamp", "30 minutes")
What is the result?

A. It is not able to handle deduplication in this scenario
B. It accepts watermarks in seconds and the code results in an error
C. It removes all duplicates regardless of when they arrive
D. It removes duplicates that arrive within the 30-minute window specified by the watermark

Answer: D

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
The methoddropDuplicatesWithinWatermark()in Structured Streaming drops duplicate records based on a specified column and watermark window. The watermark defines the threshold for how late data is considered valid.
From the Spark documentation:
"dropDuplicatesWithinWatermark removes duplicates that occur within the event-time watermark window." In this case, Spark will retain the first occurrence and drop subsequent records within the 30-minute watermark window.
Final Answer: B

NEW QUESTION # 65
......

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