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Databricks Associate-Developer-Apache-Spark-3.5 Valid Test Syllabus - Reliable Associate-Developer-Apache-Spark-3.5 Test Duration
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Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions (Q74-Q79):
NEW QUESTION # 74
A developer is working with a pandas DataFrame containing user behavior data from a web application.
Which approach should be used for executing agroupByoperation in parallel across all workers in Apache Spark 3.5?
A)
Use the applylnPandas API
B)
C)
D)
- A. Use theapplyInPandasAPI:
df.groupby("user_id").applyInPandas(mean_func, schema="user_id long, value double").show()
- B. Use a Pandas UDF:
@pandas_udf("double")
def mean_func(value: pd.Series) -> float:
return value.mean()
df.groupby("user_id").agg(mean_func(df["value"])).show()
- C. Use themapInPandasAPI:
df.mapInPandas(mean_func, schema="user_id long, value double").show()
- D. Use a regular Spark UDF:
from pyspark.sql.functions import mean
df.groupBy("user_id").agg(mean("value")).show()
Answer: A
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
The correct approach to perform a parallelizedgroupByoperation across Spark worker nodes using Pandas API is viaapplyInPandas. This function enables grouped map operations using Pandas logic in a distributed Spark environment. It applies a user-defined function to each group of data represented as a Pandas DataFrame.
As per the Databricks documentation:
"applyInPandas()allows for vectorized operations on grouped data in Spark. It applies a user-defined function to each group of a DataFrame and outputs a new DataFrame. This is the recommended approach for using Pandas logic across grouped data with parallel execution." Option A is correct and achieves this parallel execution.
Option B (mapInPandas) applies to the entire DataFrame, not grouped operations.
Option C uses built-in aggregation functions, which are efficient but not customizable with Pandas logic.
Option D creates a scalar Pandas UDF which does not perform a group-wise transformation.
Therefore, to run agroupBywith parallel Pandas logic on Spark workers, Option A usingapplyInPandasis the only correct answer.
Reference: Apache Spark 3.5 Documentation # Pandas API on Spark # Grouped Map Pandas UDFs (applyInPandas)
NEW QUESTION # 75
Given this code:
.withWatermark("event_time","10 minutes")
.groupBy(window("event_time","15 minutes"))
.count()
What happens to data that arrives after the watermark threshold?
Options:
- A. Any data arriving more than 10 minutes after the watermark threshold will be ignored and not included in the aggregation.
- B. Records that arrive later than the watermark threshold (10 minutes) will automatically be included in the aggregation if they fall within the 15-minute window.
- C. The watermark ensures that late data arriving within 10 minutes of the latest event_time will be processed and included in the windowed aggregation.
- D. Data arriving more than 10 minutes after the latest watermark will still be included in the aggregation but will be placed into the next window.
Answer: A
Explanation:
According to Spark's watermarking rules:
"Records that are older than the watermark (event time < current watermark) are considered too late and are dropped." So, if a record'sevent_timeis earlier than (max event_time seen so far - 10 minutes), it is discarded.
Reference:Structured Streaming - Handling Late Data
NEW QUESTION # 76
A data engineer is working with a large JSON dataset containing order information. The dataset is stored in a distributed file system and needs to be loaded into a Spark DataFrame for analysis. The data engineer wants to ensure that the schema is correctly defined and that the data is read efficiently.
Which approach should the data scientist use to efficiently load the JSON data into a Spark DataFrame with a predefined schema?
- A. Use spark.read.json() to load the data, then use DataFrame.printSchema() to view the inferred schema, and finally use DataFrame.cast() to modify column types.
- B. Use spark.read.json() with the inferSchema option set to true
- C. Use spark.read.format("json").load() and then use DataFrame.withColumn() to cast each column to the desired data type.
- D. Define a StructType schema and use spark.read.schema(predefinedSchema).json() to load the data.
Answer: D
Explanation:
The most efficient and correct approach is to define a schema using StructType and pass it tospark.read.
schema(...).
This avoids schema inference overhead and ensures proper data types are enforced during read.
Example:
frompyspark.sql.typesimportStructType, StructField, StringType, DoubleType schema = StructType([ StructField("order_id", StringType(),True), StructField("amount", DoubleType(),True),
])
df = spark.read.schema(schema).json("path/to/json")
- Source:Databricks Guide - Read JSON with predefined schema
NEW QUESTION # 77
A data scientist at a financial services company is working with a Spark DataFrame containing transaction records. The DataFrame has millions of rows and includes columns fortransaction_id,account_number, transaction_amount, andtimestamp. Due to an issue with the source system, some transactions were accidentally recorded multiple times with identical information across all fields. The data scientist needs to remove rows with duplicates across all fields to ensure accurate financial reporting.
Which approach should the data scientist use to deduplicate the orders using PySpark?
- A. df = df.dropDuplicates(["transaction_amount"])
- B. df = df.groupBy("transaction_id").agg(F.first("account_number"), F.first("transaction_amount"), F.first ("timestamp"))
- C. df = df.filter(F.col("transaction_id").isNotNull())
- D. df = df.dropDuplicates()
Answer: D
Explanation:
dropDuplicates() with no column list removes duplicates based on all columns.
It's the most efficient and semantically correct way to deduplicate records that are completely identical across all fields.
From the PySpark documentation:
dropDuplicates(): Return a new DataFrame with duplicate rows removed, considering all columns if none are specified.
- Source:PySpark DataFrame.dropDuplicates() API
NEW QUESTION # 78
A data engineer is working on the DataFrame:
(Referring to the table image: it has columnsId,Name,count, andtimestamp.) Which code fragment should the engineer use to extract the unique values in theNamecolumn into an alphabetically ordered list?
- A. df.select("Name").orderBy(df["Name"].asc())
- B. df.select("Name").distinct()
- C. df.select("Name").distinct().orderBy(df["Name"].desc())
- D. df.select("Name").distinct().orderBy(df["Name"])
Answer: D
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
To extract unique values from a column and sort them alphabetically:
distinct()is required to remove duplicate values.
orderBy()is needed to sort the results alphabetically (ascending by default).
Correct code:
df.select("Name").distinct().orderBy(df["Name"])
This is directly aligned with standard DataFrame API usage in PySpark, as documented in the official Databricks Spark APIs. Option A is incorrect because it may not remove duplicates. Option C omits sorting.
Option D sorts in descending order, which doesn't meet the requirement for alphabetical (ascending) order.
NEW QUESTION # 79
......
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