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Databricks Associate-Developer-Apache-Spark Exam Overview:
| Certification Vendor: | Databricks |
|---|---|
| Exam Name: | Databricks Certified Associate Developer for Apache Spark 3.0 Exam |
| Exam Number: | Associate-Developer-Apache-Spark |
| Exam Price: | $200 USD |
| Exam Format: | Multiple Select, Multiple Choice |
| Exam Duration: | 120 minutes |
| Passing Score: | 70% |
| Available Languages: | English |
| Certificate Validity Period: | 2 years |
| Real Exam Qty: | 60 |
| Related Certifications: | Databricks Certified Associate Data Engineer Databricks Certified Professional Data Engineer |
| Recommended Training: | Databricks Academy - Apache Spark for Developers Databricks Exam Guide |
| Exam Registration: | Databricks Certification Portal |
| Sample Questions: |  |
| Exam Way: | Online proctored or in-person at authorized test centers |
| Pre Condition: | Basic programming experience in Python or Scala; working knowledge of SQL; recommended 6+ months of hands-on Spark experience |
| Official Syllabus URL: | https://www.databricks.com/learn/certification/apache-spark-developer-associate |
Databricks Associate-Developer-Apache-Spark Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Apache Spark Architecture and Components | 28% | - Lazy evaluation, transformations vs actions - Fault tolerance and shuffle operations - Spark execution model and hierarchy - Driver and executor architecture |
| Using Spark SQL | 12% | - Querying data with Spark SQL - Supported file formats and data sources - Working with views and tables |
| Structured Streaming | 8% | - Streaming concepts and operations - Output modes and triggers |
| Troubleshooting and Tuning | 8% | - Query plans and execution analysis - Performance optimization |
| Developing DataFrame API Applications | 44% | - Reading/writing data and partitioning - Column and row manipulation - Filtering, sorting, aggregation - Handling missing data and duplicates - User-defined functions (UDFs) |
Common Questions About the Databricks Associate-Developer-Apache-Spark Exam
What is the Databricks Certified Associate Developer for Apache Spark 3.0 exam all about?
The Associate-Developer-Apache-Spark exam is the official Databricks exam behind the Databricks Certified Associate Developer for Apache Spark 3.0 certification, validating the skills measured by the Databricks Certified Associate Developer for Apache Spark 3.0 credential. It sits at the Associate level of the Databricks certification program. It also connects to Databricks Certified Professional Data Engineer, Databricks Certified Associate Data Engineer, so the knowledge you build here carries over to those tracks as well.
How many questions are on the Associate-Developer-Apache-Spark exam, and how much time do I get?
The Associate-Developer-Apache-Spark exam contains 60 questions to be completed within 120 minutes. Before exam day, divide the available time by the question count to work out a comfortable per-question pace, and mark any item that eats into it so you can return later instead of getting stuck. Timed sessions in the Getcertkey test engines make that pacing automatic — run at least two full-length mock exams under the clock so time pressure never becomes the reason you drop points.
What score do I need to pass the Associate-Developer-Apache-Spark exam, and what does it cost?
The passing score for the Associate-Developer-Apache-Spark exam is 70%, and the official registration fee is $200 USD. Retakes are not discounted — every new attempt means paying the full fee again — so it pays to measure yourself before you book. Work through the 179 practice questions on Getcertkey, sit a timed practice test, and schedule your exam only when your scores are consistently comfortable. That simple habit is the cheapest exam strategy there is.
Are there any prerequisites for the Associate-Developer-Apache-Spark exam?
Basic programming experience in Python or Scala; working knowledge of SQL; recommended 6+ months of hands-on Spark experience Requirements can change when Databricks revises its certification program, so confirm the current eligibility rules on the official exam page before you register.
How do I register for the Associate-Developer-Apache-Spark exam?
You can book the Databricks Certified Associate Developer for Apache Spark 3.0 exam through the official registration channels below:
As for delivery, the exam is offered in the following format: Online proctored or in-person at authorized test centers. Choose the option that suits you best when you book your seat.
What official training is recommended for the Associate-Developer-Apache-Spark exam?
Databricks recommends the following training resources for the Databricks Certified Associate Developer for Apache Spark 3.0 exam:
Official courses build the foundation; the 179 practice questions from Getcertkey then show you how that knowledge is examined, so the two work best together.
Can I try the Associate-Developer-Apache-Spark practice questions before I buy?
Yes. Getcertkey provides a free Associate-Developer-Apache-Spark PDF demo so you can review the question style and answer quality before purchasing. Every purchase also includes 365 days of free updates — if Databricks revises the exam during that period, the updated material reaches you at no cost. Once the free-update year ends, you can extend your update service at a 50% discount.
What if I fail the Associate-Developer-Apache-Spark exam, and how is my order delivered?
Every Databricks Certified Associate Developer for Apache Spark 3.0 purchase on Getcertkey is covered by a 100% money-back guarantee with clear conditions: if you take the corresponding exam within 60 days of your purchase and do not pass, you can claim a full refund by submitting a scanned copy of your exam enrollment slip and your official score report as a PDF within two days of the exam date; claims are processed within seven days of submission. The guarantee does not apply to exams taken within three days of purchase, to material that was downloaded but never used in an exam attempt, or to free products and expired orders, and the candidate name must match the payer name. If you would rather not take a refund, you can instead exchange your purchase for two free exam preparation products of equal value and keep the update service on your original product.
Delivery is instant: your download is sent to your email within one minute of payment, with no limit on how many computers you may install the material on. If nothing arrives within two hours, check your spam folder and contact customer service for help.
What topics are covered in the Associate-Developer-Apache-Spark exam?
The Databricks Certified Associate Developer for Apache Spark 3.0 exam blueprint is organized into 5 domains. The first three are:
- Apache Spark Architecture and Components — 28% of the exam
- Developing DataFrame API Applications — 44% of the exam
- Structured Streaming — 8% of the exam
For the complete domain-by-domain breakdown, scroll up to the full exam topics outline above and use it to plan how you distribute your study time.
Databricks Certified Associate Developer for Apache Spark 3.0 Sample Questions:
Question 1
The code block displayed below contains an error. The code block should return a DataFrame in which column predErrorAdded contains the results of Python function add_2_if_geq_3 as applied to numeric and nullable column predError in DataFrame transactionsDf. Find the error.
Code block:
1.def add_2_if_geq_3(x):
2. if x is None:
3. return x
4. elif x >= 3:
5. return x+2
6. return x
7.
8.add_2_if_geq_3_udf = udf(add_2_if_geq_3)
9.
10.transactionsDf.withColumnRenamed("predErrorAdded", add_2_if_geq_3_udf(col("predError")))
A. The operator used to adding the column does not add column predErrorAdded to the DataFrame.
B. Instead of col("predError"), the actual DataFrame with the column needs to be passed, like so transactionsDf.predError.
C. The udf() method does not declare a return type.
D. UDFs are only available through the SQL API, but not in the Python API as shown in the code block.
E. The Python function is unable to handle null values, resulting in the code block crashing on execution.
Question 2
Which of the following code blocks returns the number of unique values in column storeId of DataFrame transactionsDf?
A. transactionsDf.dropDuplicates().agg(count("storeId"))
B. transactionsDf.select("storeId").dropDuplicates().count()
C. transactionsDf.distinct().select("storeId").count()
D. transactionsDf.select(distinct("storeId")).count()
E. transactionsDf.select(count("storeId")).dropDuplicates()
Question 3
The code block displayed below contains an error. The code block should count the number of rows that have a predError of either 3 or 6. Find the error.
Code block:
transactionsDf.filter(col('predError').in([3, 6])).count()
A. The number of rows cannot be determined with the count() operator.
B. The method used on column predError is incorrect.
C. Numbers 3 and 6 need to be passed as string variables.
D. Instead of a list, the values need to be passed as single arguments to the in operator.
E. Instead of filter, the select method should be used.
Question 4
Which of the following code blocks uses a schema fileSchema to read a parquet file at location filePath into a DataFrame?
A. spark.read().schema(fileSchema).format(parquet).load(filePath)
B. spark.read.schema(fileSchema).format("parquet").load(filePath)
C. spark.read.schema(fileSchema).open(filePath)
D. spark.read.schema("fileSchema").format("parquet").load(filePath)
E. spark.read().schema(fileSchema).parquet(filePath)
Question 5
Which of the following code blocks creates a new DataFrame with two columns season and wind_speed_ms where column season is of data type string and column wind_speed_ms is of data type double?
A. CharType()), T.StructField("season", T.DoubleType())]))
B. spark.createDataFrame({"season": ["winter","summer"], "wind_speed_ms": [4.5, 7.5]})
C. spark.DataFrame({"season": ["winter","summer"], "wind_speed_ms": [4.5, 7.5]})
D. 1. from pyspark.sql import types as T
2. spark.createDataFrame((("summer", 4.5), ("winter", 7.5)), T.StructType([T.StructField("season",
E. spark.newDataFrame([("summer", 4.5), ("winter", 7.5)], ["season", "wind_speed_ms"])
F. spark.createDataFrame([("summer", 4.5), ("winter", 7.5)], ["season", "wind_speed_ms"])
Solutions:
| Question 1 Answer: A | Question 2 Answer: B | Question 3 Answer: B | Question 4 Answer: B | Question 5 Answer: F |
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