In Apache Spark's cluster mode:
"The driver program runs on the cluster's worker node instead of the client's local machine. This allows the driver to be close to the data and other executors, reducing network overhead and improving fault tolerance for production jobs." (Source: Apache Spark documentation - Cluster Mode Overview)
"The driver program runs on the cluster's worker node instead of the client's local machine. This allows the driver to be close to the data and other executors, reducing network overhead and improving fault tolerance for production jobs." (Source: Apache Spark documentation - Cluster Mode Overview) This deployment is ideal for production environments where the job is submitted from a gateway node, and Spark manages the driver lifecycle on the cluster itself.
Option A is partially true but less specific than D.
Option B is incorrect: the driver never executes all tasks; executors handle distributed tasks.
Option C describes client mode, not cluster mode.

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

Spark Connect enables a decoupled client-server architecture, allowing remote clients to run Spark code via gRPC.
However, as of Spark 3.5, Spark Connect supports DataFrame and SQL APIs, but not RDD APIs.
Limitation:
Applications that rely heavily on RDD-based transformations or actions cannot be migrated directly to Spark Connect.
These APIs require tight driver integration, which Spark Connect intentionally decouples.
Thus, complete Spark API compatibility is not yet achieved - this is the key adoption blocker.
Why the other options are incorrect:
A: Debugging is possible through IDE integration and logs on the client side.
B: Spark Connect actually supports upgradable clients independent of the driver - this is an advantage, not a limitation.
D: Spark Connect provides strong isolation between the client and driver processes.
Reference:
Spark 3.5 Documentation - Spark Connect architecture and supported APIs.
Databricks Exam Guide (June 2025): Section "Using Spark Connect to Deploy Applications" - Spark Connect limitations (no RDD API support).

Spark's best practice is to estimate partition count based on data volume and a reasonable partition size - typically 128 MB to 256 MB per partition.
With 1 TB of data: 1 TB / 128 MB ≈ ~8000 partitions
This ensures that tasks are distributed across available CPUs for parallelism and that each task processes an optimal volume of data.
Option A (equal to cores) may result in partitions that are too large.
Option B (fixed 200) is arbitrary and may underutilize the cluster.
Option C (nodes) gives too few partitions (10), limiting parallelism.

If each of the 10 nodes runs 4 executors, and each executor is assigned 4 CPU cores:
Executors per node = 4
Cores per executor = 4
Total executors = 4 * 10 = 40
Total cores = 40 executors * 4 cores = 160 cores
However, Spark uses 1 core for overhead on each node when managing multiple executors. Thus, the practical allocation is:
Total usable executors = 4 executors/node × 10 nodes = 40
Total cores = 4 cores × 40 executors = 160
Answe r : A - but the question asks specifically about "CPU cores used by the application," assuming all However, if you are considering 4 executors/node × 4 cores = 16 cores per node, across 10 nodes, that's 160.
Final Answe r: A