Using Spark Connect

A developer is writing a Spark Connect application that must run a pandas UDF on a remote Databricks cluster. The code uses the spark.conf.set() API to pass a custom Python module to the executors. Which statement describes the correct behavior?

A developer is writing a Spark Connect application that needs to read a CSV file from cloud storage and then perform a groupBy aggregation. The developer wants to minimize data transfer between the client and the server. Which of the following approaches best achieves this?

A developer is writing a Spark Connect application that uses a Python UDF to transform a column. The UDF depends on a third-party Python library that is installed on the developer's laptop but not on the Databricks cluster. The developer runs the code and receives an error indicating the module cannot be found. What is the most appropriate fix?

A developer is troubleshooting a Spark Connect client that intermittently fails with connection errors to a Databricks cluster. Which two configuration practices help ensure stable connectivity? (Choose two.)

A data engineering team is migrating a client application to use Spark Connect. The application connects to a remote Databricks cluster. Which architectural component processes the client's DataFrame operations and executes them against the Spark cluster?

A developer is migrating a legacy PySpark application to use Spark Connect. Which architectural change is fundamental to how Spark Connect executes operations compared to traditional Spark sessions?

A data analyst uses Spark Connect to run a PySpark job against a Databricks cluster. They call `df.show()` to preview the DataFrame. Where does the actual computation for `show()` occur?

A developer wants to start using Spark Connect from a local Python environment to connect to an existing Databricks cluster. Which step is required to establish the connection?

A developer is using Spark Connect to connect to a Databricks cluster. They attempt to read a CSV file from a path that exists on the client machine's local disk. The operation fails with a file not found error. What is the most likely reason for this failure?

What is the primary benefit of using Spark Connect in a Databricks environment compared to traditional Spark clients?

A developer is troubleshooting a Spark Connect client that intermittently fails to create a session against a Databricks cluster. The cluster is configured to auto-terminate after 20 minutes of inactivity. The client script runs on a schedule every hour. What is the most likely cause of the intermittent session creation failures?

A developer writes a Spark Connect application that calls `df.cache()` on a large DataFrame, then performs several transformations and an action. The developer expects the cached data to persist on the client for reuse across sessions. Which statement describes what actually happens?

A developer is writing a Spark Connect application and wants to create a SparkSession that connects to a remote Databricks cluster. The developer has the cluster's connection string and an access token. Which method should be used to build the session?

A developer writes a Spark Connect client that creates a DataFrame, calls `df.collect()`, and then reuses the same DataFrame for a second `df.count()`. The cluster is remote. What happens on the second action?

A developer is building a Python application that connects to a Databricks cluster using Spark Connect. The application uses the `databricks-connect` package and is configured with the cluster ID and authentication credentials. During a test run, the developer calls `spark.sql("SELECT * FROM sales")` and then `df.show()`. What happens when the `show()` action is executed?

A data engineer is using Spark Connect to run a job on a Databricks cluster. They notice that when they call `df.count()`, the operation takes longer than expected. They suspect that the client is transferring data unnecessarily. Which statement best explains the data transfer behavior of `df.count()` in Spark Connect?

A data scientist is writing a Spark Connect application that requires custom user-defined functions (UDFs). How are UDFs handled when executing code through Spark Connect?

A developer is building a Spark Connect application that runs on a laptop and connects to a remote Databricks cluster. During development, the laptop loses network connectivity for a few minutes while a long-running DataFrame transformation is executing. The developer notices the local Python process raises a gRPC error and the job is no longer tracked. Which statement best explains this behavior?

A developer is writing a Spark Connect application that runs on a local laptop and connects to a Databricks cluster. The application defines a Python function and registers it with `spark.udf.register` for use inside a `select` expression. When the code runs, the function executes on the server. Which statement describes how the UDF is handled in this scenario?

An enterprise data engineering team is migrating legacy PySpark client applications to use Spark Connect to improve client stability and isolate resource consumption. A developer initializes the Spark session pointing to a remote cluster. Which specific mechanism does Spark Connect use to communicate execution plans between the client application and the server cluster?

A data engineer is using Spark Connect from a remote Python client to interact with a Databricks cluster. The engineer wants to understand which operations are executed on the server side versus the client side. Which two statements correctly describe this behavior? (Choose two.)

Which environment variable is mandatory to establish a connection to a Databricks cluster using Spark Connect in a local Python environment?

A developer uses a Spark Connect session to create a temporary view with `df.createOrReplaceTempView("sales_v")` and then runs `spark.sql("SELECT * FROM sales_v")` in the same session. What is the scope of that temporary view?

A developer runs a Spark Connect client session against a Databricks cluster with `spark.conf.set("spark.sql.shuffle.partitions", "400")`. The cluster is configured with 8 worker nodes. Which component actually applies the shuffle partition setting to the physical plan?

When using Spark Connect, how does the client handle the authentication process with the Databricks workspace?

An engineering team wants to execute PySpark queries locally from an Integrated Development Environment (IDE) while offloading all distributed compute and data processing to a remote Databricks cluster. Which Spark Connect component architecture makes this workflow possible?

A developer is using Spark Connect to connect to a Databricks cluster from a remote Python client. They need to run a custom Python function on a DataFrame column. They define the function and register it as a UDF using spark.udf.register(). After executing the job, they notice that the UDF fails with a ModuleNotFoundError for a library that is installed on their local machine but not on the cluster. What is the most likely cause and the appropriate solution?

A developer has a local Python script that connects to a Databricks cluster using Spark Connect and creates a DataFrame from a small list of tuples. They then call .collect() on the DataFrame and receive the results. Which statement accurately describes how the data and operations are processed in this scenario?

Refer to the exhibit. Traceback (most recent call last): File "app.py", line 12, in <module> df = spark.read.table("default.sales") File "/opt/spark/python/pyspark/sql/session.py", line 314, in table return DataFrame(self._client.execute_plan(parser.parse_table(name)))) File "/opt/spark/python/pyspark/sql/connect/client/core.py", line 112, in execute_plan(y+"sessionID"), grpc.RpcError: StatusCode.UNAVAILABLE An engineer attempts to run a PySpark script using Spark Connect but encounters the traceback shown above. What is the most likely root cause of this execution failure?

A developer is building a Spark Connect client application in Python that runs on a local workstation and connects to a remote Databricks cluster. The application must construct a DataFrame from a list of Python dictionaries without requiring the data to be uploaded to cloud storage first. Which approach should the developer use?

A data analyst wants to connect a local Python script to a Databricks cluster using Spark Connect. The workspace URL and a personal access token are available. Which client-side step is required to create the remote Spark session?

A data engineer is building a Spark Connect application and wants to attach a small lookup table to every task without a shuffle. The table is 20 MB and the cluster has default settings. Which approach should the engineer use?

You are troubleshooting a Spark Connect application that fails to connect to a Databricks cluster. The error message indicates an authentication failure. Which of the following is the most likely cause?

A data engineer is using Spark Connect from a local Python environment to connect to a Databricks cluster. They attempt to use the spark.sparkContext.broadcast() method to broadcast a large lookup dictionary for use in a UDF. The code fails. What is the most likely reason for this failure?

A developer is using Spark Connect to run a PySpark application against a remote Databricks cluster. The application calls df.cache() on a DataFrame that is used multiple times. Which statement accurately describes how caching behaves in this scenario?