Databricks Updated Databricks-Certified-Associate-Developer-for-Apache-Spark-3.5 Exam Questions and Answers by ivan

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

The correct answer is B because it uses the new function count_if, introduced in Spark 3.5.0, which simplifies conditional counting within aggregations.

F.count_if(condition) counts the number of rows that meet the specified boolean condition.

In this example, it directly counts how many times spot_price >= min_price evaluates to true, replacing the older verbose combination of when/otherwise and filtering or summing.

Official Spark 3.5.0 documentation notes the addition of count_if to simplify this kind of logic:

“Added count_if aggregate function to count only the rows where a boolean condition holds (SPARK-43773).”

Why other options are incorrect or outdated:

A uses a legacy-style method of adding a flag column (when().otherwise()), which is verbose compared to count_if.

C performs a simple min/max aggregation—useful but unrelated to conditional array operations or the updated functionality.

D incorrectly applies .filter() after .agg() which will cause an error, and misuses string "min_price" rather than the variable.

Therefore, B is the only option leveraging new functionality from Spark 3.5.0 correctly and efficiently.

df.cache() is shorthand for df.persist(StorageLevel.MEMORY_AND_DISK)

df.persist() allows specifying any storage level such as MEMORY_ONLY, DISK_ONLY, MEMORY_AND_DISK_SER, etc.

By default, persist() uses MEMORY_AND_DISK, unless specified otherwise.

When running in local mode (e.g., local[4]), the number inside the brackets defines how many threads Spark will use.

Using local[*] ensures Spark uses all available CPU cores for parallelism.

Example:

spark-submit --master local[*]

Dynamic allocation and executor memory apply to cluster-based deployments, not local mode.