Associate-Developer-Apache-Spark-3.5題庫最新資訊 - Associate-Developer-Apache-Spark-3.5最新考題

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問題 #97
A developer is trying to join two tables, sales.purchases_fct and sales.customer_dim, using the following code:

fact_df = purch_df.join(cust_df, F.col('customer_id') == F.col('custid')) The developer has discovered that customers in the purchases_fct table that do not exist in the customer_dim table are being dropped from the joined table.
Which change should be made to the code to stop these customer records from being dropped?

A. fact_df = purch_df.join(cust_df, F.col('cust_id') == F.col('customer_id'))
B. fact_df = purch_df.join(cust_df, F.col('customer_id') == F.col('custid'), 'left')
C. fact_df = cust_df.join(purch_df, F.col('customer_id') == F.col('custid'))
D. fact_df = purch_df.join(cust_df, F.col('customer_id') == F.col('custid'), 'right_outer')

答案：B

解題說明：
In Spark, the default join type is an inner join, which returns only the rows with matching keys in both DataFrames. To retain all records from the left DataFrame (purch_df) and include matching records from the right DataFrame (cust_df), a left outer join should be used.
By specifying the join type as 'left', the modified code ensures that all records from purch_df are preserved, and matching records from cust_df are included. Records in purch_df without a corresponding match in cust_df will have null values for the columns from cust_df.
This approach is consistent with standard SQL join operations and is supported in PySpark's DataFrame API.

問題 #98
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.json() with the inferSchema option set to true
D. Use spark.read.format("json").load() and then use DataFrame.withColumn() to cast each column to the desired data type.

答案：A

解題說明：
The most efficient and correct approach is to define a schema using StructType and pass it to spark.read.schema(...).
This avoids schema inference overhead and ensures proper data types are enforced during read.
Example:
from pyspark.sql.types import StructType, 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

問題 #99
47 of 55.
A data engineer has written the following code to join two DataFrames df1 and df2:
df1 = spark.read.csv("sales_data.csv")
df2 = spark.read.csv("product_data.csv")
df_joined = df1.join(df2, df1.product_id == df2.product_id)
The DataFrame df1 contains ~10 GB of sales data, and df2 contains ~8 MB of product data.
Which join strategy will Spark use?

A. Shuffle join, as the size difference between df1 and df2 is too large for a broadcast join to work efficiently.
B. Broadcast join, as df2 is smaller than the default broadcast threshold.
C. Shuffle join, because AQE is not enabled, and Spark uses a static query plan.
D. Shuffle join because no broadcast hints were provided.

答案：B

解題說明：
Spark automatically uses a broadcast hash join when one side of the join is small enough to fit within the broadcast threshold.
Default threshold:
spark.sql.autoBroadcastJoinThreshold = 10MB (as of Spark 3.5)
Since df2 is 8 MB, Spark automatically broadcasts it to all executors. This avoids a shuffle on the large dataset (df1) and speeds up the join.
Why the other options are incorrect:
A: 8 MB < 10 MB threshold → broadcast join is efficient.
B: AQE is not required for broadcast joins; it's a static optimization.
C: Broadcast hints are optional - Spark infers automatically.
Reference:
Databricks Exam Guide (June 2025): Section "Troubleshooting and Tuning Apache Spark DataFrame API Applications" - broadcast joins and optimization.
Spark SQL Join Strategies - Broadcast hash join and shuffle join thresholds.

問題 #100
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 load all data into the driver's memory, potentially causing memory overflow
C. The operation will fail if the Pandas DataFrame exceeds 1000 rows
D. Data will be lost during conversion

答案：B

解題說明：
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

問題 #101
A data engineer is working on the DataFrame:

(Referring to the table image: it has columns Id, Name, count, and timestamp.) Which code fragment should the engineer use to extract the unique values in the Name column into an alphabetically ordered list?

A. df.select("Name").distinct()
B. df.select("Name").distinct().orderBy(df["Name"].desc())
C. df.select("Name").orderBy(df["Name"].asc())
D. df.select("Name").distinct().orderBy(df["Name"])

答案：D

解題說明：
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.

問題 #102
......

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Associate-Developer-Apache-Spark-3.5題庫最新資訊 - Associate-Developer-Apache-Spark-3.5最新考題

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已通過驗證Associate-Developer-Apache-Spark-3.5題庫最新資訊和資格考試領導者及100％合格率Associate-Developer-Apache-Spark-3.5最新考題

最新的 Databricks Certification Associate-Developer-Apache-Spark-3.5 免費考試真題 (Q97-Q102):

值得信賴的Associate-Developer-Apache-Spark-3.5題庫最新資訊 |第一次嘗試輕鬆學習並通過考試和最佳的Associate-Developer-Apache-Spark-3.5：Databricks Certified Associate Developer for Apache Spark 3.5 - Python

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