Comprehensive and Detailed Explanation From Exact Extract:
When using theMEMORY_AND_DISKstorage level, Spark attempts to cache as much of the DataFrame in memory as possible. If the DataFrame does not fit entirely in memory, Spark will store the remaining partitions on disk. This allows processing to continue, albeit with a performance overhead due to disk I/O.
As per the Spark documentation:
"MEMORY_AND_DISK: It stores partitions that do not fit in memory on disk and keeps the rest in memory.
This can be useful when working with datasets that are larger than the available memory."
- Perficient Blogs: Spark - StorageLevel
This behavior ensures that Spark can handle datasets larger than the available memory by spilling excess data to disk, thus preventing job failures due to memory constraints.

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

Comprehensive and Detailed Explanation:
The approx_percentile function in Spark is a performance-optimized alternative to percentile. It takes an optional accuracy parameter:
approx_percentile(column, percentage, accuracy)
Higher accuracy values # more precise results, but increased memory/computation.
Lower values # faster but less accurate.
From the documentation:
"Increasing the accuracy improves precision but increases memory usage." Final Answer: D

Comprehensive and Detailed Explanation From Exact Extract:
In Spark's client mode, the driver runs on the local machine that submitted the job. If that machine is resource- constrained (e.g., low memory), performance degrades.
From the Spark documentation:
"In cluster mode, the driver runs inside the cluster, benefiting from cluster resources and scalability." Option A is incorrect - executors do not help the driver directly.
Option B might help short-term but does not scale.
Option C is correct - switching to cluster mode moves the driver to the cluster.
Option D (local mode) is for development/testing, not production.
Final Answer: C

Comprehensive and Detailed Explanation From Exact Extract:
The correct way to load specific columns from an ORC file is to first load the file using.load()and then apply.
select()on the resulting DataFrame. This is valid with.read.format("orc")or the shortcut.read.orc().
df = spark.read.format("orc").load("/file/test_data.orc").select("col1","col2") Why others are incorrect:
Aperforms selection after filtering, but doesn't match the intention to minimize memory at load.
Bincorrectly tries to use.select()before.load(), which is invalid.
Cuses a non-existent.selected()method.
Dcorrectly loads and then selects.
Reference:Apache Spark SQL API - ORC Format