SMASHING ASSOCIATE-DEVELOPER-APACHE-SPARK-3.5 GUIDE MATERIALS: DATABRICKS CERTIFIED ASSOCIATE DEVELOPER FOR APACHE SPARK 3.5 - PYTHON DELIVER YOU UNIQUE EXAM BRAINDUMPS - PDFVCE

Smashing Associate-Developer-Apache-Spark-3.5 Guide Materials: Databricks Certified Associate Developer for Apache Spark 3.5 - Python Deliver You Unique Exam Braindumps - PDFVCE

Smashing Associate-Developer-Apache-Spark-3.5 Guide Materials: Databricks Certified Associate Developer for Apache Spark 3.5 - Python Deliver You Unique Exam Braindumps - PDFVCE

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Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions (Q48-Q53):

NEW QUESTION # 48
A data engineer wants to write a Spark job that creates a new managed table. If the table already exists, the job should fail and not modify anything.
Which save mode and method should be used?

  • A. saveAsTable with mode ErrorIfExists
  • B. saveAsTable with mode Overwrite
  • C. save with mode ErrorIfExists
  • D. save with mode Ignore

Answer: A

Explanation:
Comprehensive and Detailed Explanation:
The methodsaveAsTable()creates a new table and optionally fails if the table exists.
From Spark documentation:
"The mode 'ErrorIfExists' (default) will throw an error if the table already exists." Thus:
Option A is correct.
Option B (Overwrite) would overwrite existing data - not acceptable here.
Option C and D usesave(), which doesn't create a managed table with metadata in the metastore.
Final Answer: A


NEW QUESTION # 49
The following code fragment results in an error:

Which code fragment should be used instead?

  • A.
  • B.
  • C.
  • D.

Answer: C


NEW QUESTION # 50
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 a regular Spark UDF:
    from pyspark.sql.functions import mean
    df.groupBy("user_id").agg(mean("value")).show()
  • B. Use theapplyInPandasAPI:
    df.groupby("user_id").applyInPandas(mean_func, schema="user_id long, value double").show()
  • C. 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()
  • D. Use themapInPandasAPI:
    df.mapInPandas(mean_func, schema="user_id long, value double").show()

Answer: B

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 # 51
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 accepts watermarks in seconds and the code results in an error
  • B. It removes duplicates that arrive within the 30-minute window specified by the watermark
  • C. It is not able to handle deduplication in this scenario
  • D. It removes all duplicates regardless of when they arrive

Answer: B

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 # 52
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. Define a StructType schema and use spark.read.schema(predefinedSchema).json() to load the data.
  • B. 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.
  • C. Use spark.read.format("json").load() and then use DataFrame.withColumn() to cast each column to the desired data type.
  • D. Use spark.read.json() with the inferSchema option set to true

Answer: A

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 # 53
......

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