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- Total Questions: 135
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Free demo, instant delivery, a year of free updates, and a written refund policy: ActualTestsIT gives 2026 Associate-Developer-Apache-Spark-3.5 candidates a fast, accountable route to Databricks Certified Associate Developer for Apache Spark 3.5 - Python certification.
Databricks Associate-Developer-Apache-Spark-3.5 Exam Overview:
| Certification Vendor: | Databricks |
|---|---|
| Exam Name: | Databricks Certified Associate Developer for Apache Spark 3.5 - Python |
| Exam Number: | Associate-Developer-Apache-Spark-3.5-Python |
| Passing Score: | 70% |
| Certificate Validity Period: | 2 years |
| Related Certifications: | Databricks Certified Data Engineer Professional Databricks Certified Data Engineer Associate |
| Available Languages: | English |
| Real Exam Qty: | 45-60 |
| Exam Price: | $200 USD |
| Exam Format: | Multiple choice, Multiple select |
| Exam Duration: | 90 minutes |
| Recommended Training: | Databricks Academy - Apache Spark Training Apache Spark Documentation |
| Exam Registration: | Databricks Certification Portal |
| Sample Questions: | DOWNLOAD DEMO |
| Exam Way: | Online proctored exam |
| Pre Condition: | Basic knowledge of Python programming and SQL is recommended. Familiarity with Apache Spark fundamentals is strongly suggested. |
| Official Syllabus URL: | https://www.databricks.com/learn/certification |
Databricks Associate-Developer-Apache-Spark-3.5 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: DataFrame API with PySpark | - Transformations and actions - DataFrame creation and schema management - Built-in functions and expressions |
| Topic 2: Spark SQL | - Window functions and aggregations - SQL queries on DataFrames and tables |
| Topic 3: Apache Spark Fundamentals | - Spark architecture and execution model - RDD vs DataFrame vs Dataset concepts |
| Topic 4: Structured Streaming Basics | - Windowed aggregations in streaming - Streaming DataFrames |
| Topic 5: Data Ingestion and Storage | - Delta Lake basics - Reading and writing data (Parquet, JSON, CSV) |
| Topic 6: Data Processing and Performance | - Caching and persistence strategies - Optimization techniques - Joins and data partitioning |
Databricks Associate-Developer-Apache-Spark-3.5 Exam: Essential Q&A
Databricks Certified Associate Developer for Apache Spark 3.5 - Python is an official Databricks exam, registered under exam code Associate-Developer-Apache-Spark-3.5. Passing it earns the Databricks Certified Associate Developer for Apache Spark 3.5 - Python certification at the Associate level. It also links to Databricks Certified Data Engineer Associate, Databricks Certified Data Engineer Professional. As an internationally recognized capacity standard, this credential speaks for your ability wherever your career takes you.
The Databricks Certified Associate Developer for Apache Spark 3.5 - Python syllabus comprises 6 official domains. The heaviest include Data Ingestion and Storage, Structured Streaming Basics, and Data Processing and Performance. The complete outline is above on this page; a short, efficient study plan starts with knowing exactly where the marks live.
The Databricks Certified Associate Developer for Apache Spark 3.5 - Python exam packs 45-60 questions into 90 minutes. Short-term preparation works best when every session is realistic, so run timed simulations in the ActualTestsIT engine, keep your flag-and-return rhythm tight, and let repeated full-length runs build the pace the clock demands.
Databricks Certified Associate Developer for Apache Spark 3.5 - Python requires 70% to pass, with an official registration fee of $200 USD. Retakes bill the full $200 USD again, so speed should never mean gambling. Compress your preparation with the ActualTestsIT practice tests, and book once your scores clear the requirement reliably.
Basic knowledge of Python programming and SQL is recommended. Familiarity with Apache Spark fundamentals is strongly suggested.
Policies change, so verify the current requirements before registering on the official exam page.
Databricks Certified Associate Developer for Apache Spark 3.5 - Python registration goes through the official channels below.
For your schedule planning: the exam is delivered Online proctored exam.
Databricks recommends the following training for Databricks Certified Associate Developer for Apache Spark 3.5 - Python candidates.
On a short preparation timeline, combine any training with the 135 practice questions in the ActualTestsIT Associate-Developer-Apache-Spark-3.5 package to convert learning into scoring ability fast.
Yes to both. ActualTestsIT provides a free demo of the Databricks Certified Associate Developer for Apache Spark 3.5 - Python questions so you can verify the quality first, and after purchase the newest practice material is free for one year from the date of your order. When that year ends, extending the update service costs 50% of the regular price.
Your purchase carries a 100% money-back guarantee with defined conditions. Take the Databricks Certified Associate Developer for Apache Spark 3.5 - Python exam within 60 days of purchase; if you fail, you may claim a full refund, provided the exam matches your product. Attempts within 3 days of purchase are ineligible, as are downloaded-but-unused products, free materials, and expired orders; the candidate name must match the payer name. Submit a scanned enrollment slip and the official Score Report PDF within 2 days of the exam, and claims are processed within 7 days. Alternatively, exchange for two other exam products of equal value, free, with the update service on your original purchase retained.
Delivery is instant: files unlock for download at payment and are emailed within one minute. If nothing arrives within 2 hours, check spam and contact customer service. Installation is unlimited across your computers.
Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions:
A data engineer wants to create an external table from a JSON file located at /data/input.json with the following requirements:
Create an external table named users
Automatically infer schema
Merge records with differing schemas
Which code snippet should the engineer use?
Options:
- A. CREATE TABLE users USING json OPTIONS (path '/data/input.json')
- B. CREATE EXTERNAL TABLE users USING json OPTIONS (path '/data/input.json')
- C. CREATE EXTERNAL TABLE users USING json OPTIONS (path '/data/input.json', mergeSchema 'true')
- D. CREATE EXTERNAL TABLE users USING json OPTIONS (path '/data/input.json', schemaMerge 'true')
Correct Answer: C 🗳️
Explanation: Only visible for ActualTestsIT members. You can sign-up / login (it's free).
An engineer has a large ORC file located at /file/test_data.orc and wants to read only specific columns to reduce memory usage.
Which code fragment will select the columns, i.e., col1, col2, during the reading process?
- A. spark.read.orc("/file/test_data.orc").filter("col1 = 'value' ").select("col2")
- B. spark.read.format("orc").select("col1", "col2").load("/file/test_data.orc")
- C. spark.read.format("orc").load("/file/test_data.orc").select("col1", "col2")
- D. spark.read.orc("/file/test_data.orc").selected("col1", "col2")
Correct Answer: C 🗳️
Explanation: Only visible for ActualTestsIT members. You can sign-up / login (it's free).
A DataFrame df has columns name, age, and salary. The developer needs to sort the DataFrame by age in ascending order and salary in descending order.
Which code snippet meets the requirement of the developer?
- A. df.orderBy("age", "salary", ascending=[True, False]).show()
- B. df.sort("age", "salary", ascending=[True, True]).show()
- C. df.orderBy(col("age").asc(), col("salary").asc()).show()
- D. df.sort("age", "salary", ascending=[False, True]).show()
Correct Answer: A 🗳️
Explanation: Only visible for ActualTestsIT members. You can sign-up / login (it's free).
A data engineer observes that an upstream streaming source sends duplicate records, where duplicates share the same key and have at most a 30-minute difference in event_timestamp. The engineer adds:
dropDuplicatesWithinWatermark("event_timestamp", "30 minutes")
What is the result?
- A. It removes all duplicates regardless of when they arrive
- B. It removes duplicates that arrive within the 30-minute window specified by the watermark
- C. It is not able to handle deduplication in this scenario
- D. It accepts watermarks in seconds and the code results in an error
Correct Answer: B 🗳️
Explanation: Only visible for ActualTestsIT members. You can sign-up / login (it's free).
40 of 55.
A developer wants to refactor older Spark code to take advantage of built-in functions introduced in Spark 3.5.
The original code:
from pyspark.sql import functions as F
min_price = 110.50
result_df = prices_df.filter(F.col("price") > min_price).agg(F.count("*")) Which code block should the developer use to refactor the code?
- A. result_df = prices_df.where(F.lit("price") > min_price).groupBy().count()
- B. result_df = prices_df.filter(F.col("price") > F.lit(min_price)).agg(F.count("*"))
- C. result_df = prices_df.withColumn("valid_price", when(col("price") > F.lit(min_price), True))
- D. result_df = prices_df.filter(F.lit(min_price) > F.col("price")).count()
Correct Answer: B 🗳️
Explanation: Only visible for ActualTestsIT members. You can sign-up / login (it's free).
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