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Databricks Associate-Developer-Apache-Spark-3.5 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Using Spark SQL | 20% | - Using catalog and metadata APIs - Running SQL queries - Working with functions and expressions - Integrating Spark SQL with DataFrames |
| Developing Apache Spark DataFrame API Applications | 30% | - Creating DataFrames and defining schemas - Reading and writing data in various formats - Joining and combining datasets - Partitioning and bucketing data - Selecting, renaming, and modifying columns - User-defined functions (UDFs) - Filtering, sorting, and aggregating data - Handling missing values and data quality |
| Troubleshooting and Tuning Apache Spark DataFrame API Applications | 10% | - Optimizing transformations and actions - Debugging and logging - Managing memory and resource usage - Identifying performance bottlenecks |
| Using Spark Connect to Deploy Applications | 5% | - Running applications via Spark Connect - Spark Connect architecture - Connecting to remote Spark clusters |
| Using Pandas API on Apache Spark | 5% | - Key differences and limitations - Overview of Pandas API on Spark - Converting between Pandas and Spark structures |
| Structured Streaming | 10% | - Output modes and triggers - Fault tolerance and state management - Streaming concepts and architecture - Defining streaming queries |
| Apache Spark Architecture and Components | 20% | - Fault tolerance and garbage collection - Shuffling, actions, and broadcasting - Execution hierarchy and lazy evaluation - Spark architecture overview - Execution and deployment modes |
What ExamCost Offers Every Associate-Developer-Apache-Spark-3.5 Candidate
At present, the Associate-Developer-Apache-Spark-3.5 exam's passing score is 70%, and registration costs $200 USD. Since failing means registering again at full price, thorough preparation is the economical choice — and Databricks can revise both figures, so confirm them officially before booking.
The Databricks Certified Associate Developer for Apache Spark 3.5 - Python blueprint is built around these main domains:
- Using Spark Connect to Deploy Applications (5%)
- Structured Streaming (10%)
- Using Spark SQL (20%)
Additional domains appear in the full official outline — our bank covers them all.
Honored in writing. If you fail the corresponding exam within 60 days of purchase, send us a scanned copy of your enrollment slip and your official Score Report PDF within two days of the exam date; verified claims are refunded in full within seven days. Exclusions: exams taken within three days of purchase, candidate names that differ from the payer, and free or expired products. You may also exchange your product for two others of equal value at no charge.
Databricks lists these official training options:
Combine structured training with engine-based practice to cover both theory and test-taking rhythm.
By a professional group with dedicated contribution — experienced engineers and educators in this industry who compile every Associate-Developer-Apache-Spark-3.5 question and verify every answer across the Databricks Certified Associate Developer for Apache Spark 3.5 - Python objectives. Maintenance is continuous: as new technology standards and knowledge emerge, revisions follow, and each updated version is sent to your mailbox immediately, free for 365 days, with a 50% renewal discount for further partnership. Privacy is protected by principle — your information is classified and never sold or shared, so no one will call you to sell something after our cooperation. And preferential terms — bundle discounts on the three versions, irregular coupons, little gifts — thank you for your trust in return.
Databricks publishes these prerequisites for the Databricks Certified Associate Developer for Apache Spark 3.5 - Python: No formal prerequisites; recommended 6+ months hands-on experience with PySpark and DataFrame API.
Verify the current requirements on the official certification page.
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Per the latest exam information, the Associate-Developer-Apache-Spark-3.5 exam contains 45 questions with 90 minutes minutes to complete them. The PC Test Engine lets you simulate a full test event under these conditions to check your progress.
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Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions:
18 of 55.
An engineer has two DataFrames - df1 (small) and df2 (large). To optimize the join, the engineer uses a broadcast join:
from pyspark.sql.functions import broadcast
df_result = df2.join(broadcast(df1), on="id", how="inner")
What is the purpose of using broadcast() in this scenario?
- A. It increases the partition size for df1 and df2.
- B. It ensures that the join happens only when the id values are identical.
- C. It reduces the number of shuffle operations by replicating the smaller DataFrame to all nodes.
- D. It filters the id values before performing the join.
Correct Answer: C 🗳️
Explanation: Only visible for ExamCost members. You can sign-up / login (it's free).
1 of 55. A data scientist wants to ingest a directory full of plain text files so that each record in the output DataFrame contains the entire contents of a single file and the full path of the file the text was read from.
The first attempt does read the text files, but each record contains a single line. This code is shown below:
txt_path = "/datasets/raw_txt/*"
df = spark.read.text(txt_path) # one row per line by default
df = df.withColumn("file_path", input_file_name()) # add full path
Which code change can be implemented in a DataFrame that meets the data scientist's requirements?
- A. Add the option lineSep=", " to the text() function.
- B. Add the option wholetext=False to the text() function.
- C. Add the option wholetext to the text() function.
- D. Add the option lineSep to the text() function.
Correct Answer: C 🗳️
Explanation: Only visible for ExamCost members. You can sign-up / login (it's free).
A Data Analyst needs to retrieve employees with 5 or more years of tenure.
Which code snippet filters and shows the list?
- A. employees_df.filter(employees_df.tenure >= 5).collect()
- B. filter(employees_df.tenure >= 5)
- C. employees_df.where(employees_df.tenure >= 5)
- D. employees_df.filter(employees_df.tenure >= 5).show()
Correct Answer: D 🗳️
Explanation: Only visible for ExamCost members. You can sign-up / login (it's free).
Given this view definition:
df.createOrReplaceTempView("users_vw")
Which approach can be used to query the users_vw view after the session is terminated?
Options:
- A. Persist the users_vw data as a table
- B. Query the users_vw using Spark
- C. Save the users_vw definition and query using Spark
- D. Recreate the users_vw and query the data using Spark
Correct Answer: A 🗳️
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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.withColumn("username", substring_index(col("email"), "@", 1)) \
.withColumn("domain", substring_index(col("email"), "@", -1)) - B. customerDF.withColumn("username", split(col("email"), "@").getItem(0)) \
.withColumn("domain", split(col("email"), "@").getItem(1)) - C. customerDF.select(
col("email").substr(0, 5).alias("username"),
col("email").substr(-5).alias("domain")
) - D. customerDF.select(
regexp_replace(col("email"), "@", "").alias("username"),
regexp_replace(col("email"), "@", "").alias("domain")
)
Correct Answer: B 🗳️
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Viewing questions 1-5 out of 135 questions.
