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Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions (Q71-Q76):

NEW QUESTION # 71
A data engineer writes the following code to join two DataFramesdf1anddf2:
df1 = spark.read.csv("sales_data.csv") # ~10 GB
df2 = spark.read.csv("product_data.csv") # ~8 MB
result = df1.join(df2, df1.product_id == df2.product_id)

Which join strategy will Spark use?

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

Answer: A

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
The default broadcast join threshold in Spark is:
spark.sql.autoBroadcastJoinThreshold = 10MB
Sincedf2is only 8 MB (less than 10 MB), Spark will automatically apply a broadcast join without requiring explicit hints.
From the Spark documentation:
"If one side of the join is smaller than the broadcast threshold, Spark will automatically broadcast it to all executors." A is incorrect because Spark does support auto broadcast even with static plans.
B is correct: Spark will automatically broadcast df2.
C and D are incorrect because Spark's default logic handles this optimization.
Final Answer: B


NEW QUESTION # 72
A data scientist of an e-commerce company is working with user data obtained from its subscriber database and has stored the data in a DataFrame df_user. Before further processing the data, the data scientist wants to create another DataFrame df_user_non_pii and store only the non-PII columns in this DataFrame. The PII columns in df_user are first_name, last_name, email, and birthdate.
Which code snippet can be used to meet this requirement?

  • A. df_user_non_pii = df_user.dropfields("first_name, last_name, email, birthdate")
  • B. df_user_non_pii = df_user.drop("first_name", "last_name", "email", "birthdate")
  • C. df_user_non_pii = df_user.dropfields("first_name", "last_name", "email", "birthdate")
  • D. df_user_non_pii = df_user.drop("first_name", "last_name", "email", "birthdate")

Answer: B

Explanation:
Comprehensive and Detailed Explanation:
To remove specific columns from a PySpark DataFrame, the drop() method is used. This method returns a new DataFrame without the specified columns. The correct syntax for dropping multiple columns is to pass each column name as a separate argument to the drop() method.
Correct Usage:
df_user_non_pii = df_user.drop("first_name", "last_name", "email", "birthdate") This line of code will return a new DataFrame df_user_non_pii that excludes the specified PII columns.
Explanation of Options:
A).Correct. Uses the drop() method with multiple column names passed as separate arguments, which is the standard and correct usage in PySpark.
B).Although it appears similar to Option A, if the column names are not enclosed in quotes or if there's a syntax error (e.g., missing quotes or incorrect variable names), it would result in an error. However, as written, it's identical to Option A and thus also correct.
C).Incorrect. The dropfields() method is not a method of the DataFrame class in PySpark. It's used with StructType columns to drop fields from nested structures, not top-level DataFrame columns.
D).Incorrect. Passing a single string with comma-separated column names to dropfields() is not valid syntax in PySpark.
References:
PySpark Documentation:DataFrame.drop
Stack Overflow Discussion:How to delete columns in PySpark DataFrame


NEW QUESTION # 73
A data engineer is building a Structured Streaming pipeline and wants the pipeline to recover from failures or intentional shutdowns by continuing where the pipeline left off.
How can this be achieved?

  • A. By configuring the optionrecoveryLocationduring the SparkSession initialization
  • B. By configuring the optioncheckpointLocationduringwriteStream
  • C. By configuring the optioncheckpointLocationduringreadStream
  • D. By configuring the optionrecoveryLocationduringwriteStream

Answer: B

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
To enable a Structured Streaming query to recover from failures or intentional shutdowns, it is essential to specify thecheckpointLocationoption during thewriteStreamoperation. This checkpoint location stores the progress information of the streaming query, allowing it to resume from where it left off.
According to the Databricks documentation:
"You must specify thecheckpointLocationoption before you run a streaming query, as in the following example:
option("checkpointLocation", "/path/to/checkpoint/dir")
toTable("catalog.schema.table")
- Databricks Documentation: Structured Streaming checkpoints
By setting thecheckpointLocationduringwriteStream, Spark can maintain state information and ensure exactly- once processing semantics, which are crucial for reliable streaming applications.


NEW QUESTION # 74
A developer wants to test Spark Connect with an existing Spark application.
What are the two alternative ways the developer can start a local Spark Connect server without changing their existing application code? (Choose 2 answers)

  • A. Ensure the Spark propertyspark.connect.grpc.binding.portis set to 15002 in the application code
  • B. Execute their pyspark shell with the option--remote "https://localhost"
  • C. Set the environment variableSPARK_REMOTE="sc://localhost"before starting the pyspark shell
  • D. Execute their pyspark shell with the option--remote "sc://localhost"
  • E. Add.remote("sc://localhost")to their SparkSession.builder calls in their Spark code

Answer: C,D

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
Spark Connect enables decoupling of the client and Spark driver processes, allowing remote access. Spark supports configuring the remote Spark Connect server in multiple ways:
From Databricks and Spark documentation:
Option B (--remote "sc://localhost") is a valid command-line argument for thepysparkshell to connect using Spark Connect.
Option C (settingSPARK_REMOTEenvironment variable) is also a supported method to configure the remote endpoint.
Option A is incorrect because Spark Connect uses thesc://protocol, nothttps://.
Option D requires modifying the code, which the question explicitly avoids.
Option E configures the port on the server side but doesn't start a client connection.
Final Answers: B and C


NEW QUESTION # 75
A data engineer is working ona Streaming DataFrame streaming_df with the given streaming data:

Which operation is supported with streaming_df?

  • A. streaming_df.orderBy("timestamp").limit(4)
  • B. streaming_df.filter(col("count") < 30).show()
  • C. streaming_df.groupby("Id").count()
  • D. streaming_df.select(countDistinct("Name"))

Answer: C

Explanation:
Comprehensive and Detailed
Explanation:
In Structured Streaming, only a limited subset of operations is supported due to the nature of unbounded data.
Operations like sorting (orderBy) and global aggregation (countDistinct) require a full view of the dataset, which is not possible with streaming data unless specific watermarks or windows are defined.
Review of Each Option:
A). select(countDistinct("Name"))
Not allowed - Global aggregation like countDistinct() requires the full dataset and is not supported directly in streaming without watermark and windowing logic.
Reference: Databricks Structured Streaming Guide - Unsupported Operations.
B). groupby("Id").count()Supported - Streaming aggregations over a key (like groupBy("Id")) are supported.
Spark maintains intermediate state for each key.Reference: Databricks Docs # Aggregations in Structured Streaming (https://docs.databricks.com/structured-streaming/aggregation.html)
C). orderBy("timestamp").limit(4)Not allowed - Sorting and limiting require a full view of the stream (which is infinite), so this is unsupported in streaming DataFrames.Reference: Spark Structured Streaming - Unsupported Operations (ordering without watermark/window not allowed).
D). filter(col("count") < 30).show()Not allowed - show() is a blocking operation used for debugging batch DataFrames; it's not allowed on streaming DataFrames.Reference: Structured Streaming Programming Guide
- Output operations like show() are not supported.
Reference Extract from Official Guide:
"Operations like orderBy, limit, show, and countDistinct are not supported in Structured Streaming because they require the full dataset to compute a result. Use groupBy(...).agg(...) instead for incremental aggregations."- Databricks Structured Streaming Programming Guide


NEW QUESTION # 76
......

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