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[Hardware] Free PDF 2026 Reliable Databricks Associate-Developer-Apache-Spark-3.5 Cheap Dum

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【Hardware】 Free PDF 2026 Reliable Databricks Associate-Developer-Apache-Spark-3.5 Cheap Dum

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Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions (Q58-Q63):NEW QUESTION # 58
4 of 55.
A developer is working on a Spark application that processes a large dataset using SQL queries. Despite having a large cluster, the developer notices that the job is underutilizing the available resources. Executors remain idle for most of the time, and logs reveal that the number of tasks per stage is very low. The developer suspects that this is causing suboptimal cluster performance.
Which action should the developer take to improve cluster utilization?
  • A. Increase the value of spark.sql.shuffle.partitions
  • B. Reduce the value of spark.sql.shuffle.partitions
  • C. Enable dynamic resource allocation to scale resources as needed
  • D. Increase the size of the dataset to create more partitions
Answer: A
Explanation:
In Spark SQL and DataFrame operations, the configuration parameter spark.sql.shuffle.partitions defines the number of partitions created during shuffle operations such as join, groupBy, and distinct.
The default value (in Spark 3.5) is 200.
If this number is too low, Spark creates fewer tasks, leading to idle executors and poor cluster utilization.
Increasing this value allows Spark to create more tasks that can run in parallel across executors, effectively using more cluster resources.
Correct approach:
spark.conf.set("spark.sql.shuffle.partitions", 400)
This increases the parallelism level of shuffle stages and improves overall resource utilization.
Why the other options are incorrect:
B: Reducing partitions further would decrease parallelism and worsen the underutilization issue.
C: Dynamic resource allocation scales executors up or down based on workload, but it doesn't fix low task parallelism caused by insufficient shuffle partitions.
D: Increasing dataset size is not a tuning solution and doesn't address task-level under-parallelization.
Reference (Databricks Apache Spark 3.5 - Python / Study Guide):
Spark SQL Configuration: spark.sql.shuffle.partitions - controls the number of shuffle partitions.
Databricks Exam Guide (June 2025): Section "Troubleshooting and Tuning Apache Spark DataFrame API Applications" - tuning strategies, partitioning, and optimizing cluster utilization.

NEW QUESTION # 59
27 of 55.
A data engineer needs to add all the rows from one table to all the rows from another, but not all the columns in the first table exist in the second table.
The error message is:
AnalysisException: UNION can only be performed on tables with the same number of columns.
The existing code is:
au_df.union(nz_df)
The DataFrame au_df has one extra column that does not exist in the DataFrame nz_df, but otherwise both DataFrames have the same column names and data types.
What should the data engineer fix in the code to ensure the combined DataFrame can be produced as expected?
  • A. df = au_df.unionByName(nz_df, allowMissingColumns=False)
  • B. df = au_df.union(nz_df, allowMissingColumns=True)
  • C. df = au_df.unionAll(nz_df)
  • D. df = au_df.unionByName(nz_df, allowMissingColumns=True)
Answer: D
Explanation:
When two DataFrames have different column sets, the normal union() or unionAll() functions fail unless both have exactly the same columns in the same order.
Solution: Use unionByName() with allowMissingColumns=True.
This aligns columns by name and automatically adds missing columns with null values.
Correct syntax:
combined_df = au_df.unionByName(nz_df, allowMissingColumns=True)
This ensures the union works even if one DataFrame has extra or missing columns.
Why the other options are incorrect:
B: unionAll() is deprecated; also requires identical schemas.
C: With allowMissingColumns=False, Spark still throws a mismatch error.
D: union() doesn't accept the allowMissingColumns argument.
Reference:
PySpark API - DataFrame.unionByName() with allowMissingColumns option.
Databricks Exam Guide (June 2025): Section "Developing Apache Spark DataFrame/DataSet API Applications" - combining DataFrames and schema alignment.

NEW QUESTION # 60
What is a feature of Spark Connect?
  • A. It supports only PySpark applications
  • B. It supports DataStreamReader, DataStreamWriter, StreamingQuery, and Streaming APIs
  • C. It has built-in authentication
  • D. Supports DataFrame, Functions, Column, SparkContext PySpark APIs
Answer: B
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
Spark Connect is a client-server architecture introduced in Apache Spark 3.4, designed to decouple the client from the Spark driver, enabling remote connectivity to Spark clusters.
According to the Spark 3.5.5 documentation:
"Majority of the Streaming API is supported, including DataStreamReader, DataStreamWriter, StreamingQuery and StreamingQueryListener." This indicates that Spark Connect supports key components of Structured Streaming, allowing for robust streaming data processing capabilities.
Regarding other options:
B).While Spark Connect supports DataFrame, Functions, and Column APIs, it does not support SparkContext and RDD APIs.
C).Spark Connect supports multiple languages, including PySpark and Scala, not just PySpark.
D).Spark Connect does not have built-in authentication but is designed to work seamlessly with existing authentication infrastructures.

NEW QUESTION # 61
Which command overwrites an existing JSON file when writing a DataFrame?
  • A. df.write.json("path/to/file", overwrite=True)
  • B. df.write.format("json").save("path/to/file", mode="overwrite")
  • C. df.write.overwrite.json("path/to/file")
  • D. df.write.mode("overwrite").json("path/to/file")
Answer: D
Explanation:
The correct way to overwrite an existing file using the DataFrameWriter is:
df.write.mode("overwrite").json("path/to/file")
Option D is also technically valid, but Option A is the most concise and idiomatic PySpark syntax.
ReferenceySpark DataFrameWriter API

NEW QUESTION # 62
37 of 55.
A data scientist is working with a Spark DataFrame called customerDF that contains customer information.
The DataFrame has a column named email with customer email addresses.
The data scientist needs to split this column into username and domain parts.
Which code snippet splits the email column into username and domain columns?
  • A. customerDF = customerDF.withColumn("domain", col("email").split("@")[1])
  • B. customerDF = customerDF.withColumn("username", regexp_replace(col("email"), "@", ""))
  • C. customerDF = customerDF.select("email").alias("username", "domain")
  • D. customerDF = customerDF
    .withColumn("username", split(col("email"), "@").getItem(0))
    .withColumn("domain", split(col("email"), "@").getItem(1))
Answer: D
Explanation:
The split() function in PySpark splits strings into an array based on a given delimiter.
Then, .getItem(index) extracts a specific element from the array.
Correct usage:
from pyspark.sql.functions import split, col
customerDF = customerDF
.withColumn("username", split(col("email"), "@").getItem(0))
.withColumn("domain", split(col("email"), "@").getItem(1))
This creates two new columns derived from the email field:
"username" → text before @
"domain" → text after @
Why the other options are incorrect:
B: regexp_replace only replaces text; does not split into multiple columns.
C: .select() cannot alias multiple derived columns like this.
D: Column objects are not native Python strings; cannot use standard .split().
Reference:
PySpark SQL Functions - split() and getItem().
Databricks Exam Guide (June 2025): Section "Developing Apache Spark DataFrame/DataSet API Applications" - manipulating and splitting column data.

NEW QUESTION # 63
......
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