Exam Name: Databricks Certified Associate Developer for Apache Spark 3.5 - Python
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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 |
| Certificate Validity Period: | 2 years |
| Passing Score: | Not publicly disclosed |
| Exam Price: | $200 USD |
| Real Exam Qty: | 45 |
| Related Certifications: | Databricks Certified Professional Data Engineer Databricks Certified Data Engineer Associate |
| Exam Format: | Multiple Choice, Multiple Select |
| Available Languages: | English |
| Exam Duration: | 90 minutes |
| Sample Questions: | Databricks Associate-Developer-Apache-Spark-3.5 Sample Questions |
| Exam Way: | Online proctored exam via Kryterion Webassessor or authorized test center. |
| Pre Condition: | No formal prerequisites. Databricks recommends at least 6 months of hands-on Apache Spark experience and Python programming knowledge. |
| Official Syllabus URL: | https://www.databricks.com/learn/certification/apache-spark-developer-associate |
Databricks Associate-Developer-Apache-Spark-3.5 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Developing Apache Spark DataFrame API Applications | 30% | - DataFrame Operations
|
| Using Pandas API on Spark | 5% | - Pandas API
|
| Apache Spark Architecture and Components | 20% | - Spark Architecture
|
| Troubleshooting and Tuning | 10% | - Performance Optimization
|
| Using Spark Connect to Deploy Applications | 5% | - Spark Connect
|
| Using Spark SQL | 20% | - Spark SQL Operations
|
| Structured Streaming | 10% | - Streaming Applications
|
Associate-Developer-Apache-Spark-3.5 Exam FAQ: What Candidates Ask About Databricks Certified Associate Developer for Apache Spark 3.5 - Python
The Associate-Developer-Apache-Spark-3.5 exam, officially titled Databricks Certified Associate Developer for Apache Spark 3.5 - Python, is the Databricks exam you pass to earn the Databricks Certification certification, a credential at the Associate level. Passing it confirms the skills defined in the official exam outline, and it is associated with related credentials such as Databricks Certified Professional Data Engineer, Databricks Certified Data Engineer Associate.
The Associate-Developer-Apache-Spark-3.5 exam includes 45 questions and gives you 90 minutes to finish them. That is a tight pace per question, so get used to reading each stem once, flagging anything uncertain, and moving on rather than getting stuck. Running timed practice tests in the Fast2test desktop or online test engine is the most reliable way to build that rhythm before exam day.
The passing score for the Associate-Developer-Apache-Spark-3.5 exam is Not publicly disclosed, and the official registration fee is $200 USD. Keep in mind that a failed attempt means paying $200 USD again in full to retake it, so book your slot only when your scores on Fast2test practice tests sit consistently above the passing line.
Databricks lists the following prerequisites or eligibility notes for the Associate-Developer-Apache-Spark-3.5 exam: No formal prerequisites. Databricks recommends at least 6 months of hands-on Apache Spark experience and Python programming knowledge.. Requirements can change over time, so confirm the details on the official exam page at Databricks's official site before you register.
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The Associate-Developer-Apache-Spark-3.5 exam blueprint is divided into 7 domains, starting with Apache Spark Architecture and Components (20%); Using Pandas API on Spark (5%); Structured Streaming (10%). For the complete domain-by-domain breakdown, see the Exam Topics section above — it lists every topic the current outline covers.
Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions:
12 of 55.
A data scientist has been investigating user profile data to build features for their model. After some exploratory data analysis, the data scientist identified that some records in the user profiles contain NULL values in too many fields to be useful.
The schema of the user profile table looks like this:
user_id STRING,
username STRING,
date_of_birth DATE,
country STRING,
created_at TIMESTAMP
The data scientist decided that if any record contains a NULL value in any field, they want to remove that record from the output before further processing.
Which block of Spark code can be used to achieve these requirements?
- A. filtered_users = raw_users.dropna(how="any")
- B. filtered_users = raw_users.na.drop("any")
- C. filtered_users = raw_users.na.drop("all")
- D. filtered_users = raw_users.dropna(how="all")
Correct Answer: A 🗳️
Explanation: Only visible for Fast2test members. You can sign-up / login (it's free).
A Spark developer wants to improve the performance of an existing PySpark UDF that runs a hash function that is not available in the standard Spark functions library. The existing UDF code is:
import hashlib
import pyspark.sql.functions as sf
from pyspark.sql.types import StringType
def shake_256(raw):
return hashlib.shake_256(raw.encode()).hexdigest(20)
shake_256_udf = sf.udf(shake_256, StringType())
The developer wants to replace this existing UDF with a Pandas UDF to improve performance. The developer changes the definition of shake_256_udf to this:CopyEdit shake_256_udf = sf.pandas_udf(shake_256, StringType()) However, the developer receives the error:
What should the signature of the shake_256() function be changed to in order to fix this error?
- A. def shake_256(raw: str) -> str:
- B. def shake_256(df: pd.Series) -> pd.Series:
- C. def shake_256(df: Iterator[pd.Series]) -> Iterator[pd.Series]:
- D. def shake_256(df: pd.Series) -> str:
Correct Answer: B 🗳️
Explanation: Only visible for Fast2test members. You can sign-up / login (it's free).
A data engineer needs to write a DataFrame df to a Parquet file, partitioned by the column country, and overwrite any existing data at the destination path.
Which code should the data engineer use to accomplish this task in Apache Spark?
- A. df.write.mode("overwrite").parquet("/data/output")
- B. df.write.partitionBy("country").parquet("/data/output")
- C. df.write.mode("overwrite").partitionBy("country").parquet("/data/output")
- D. df.write.mode("append").partitionBy("country").parquet("/data/output")
Correct Answer: C 🗳️
Explanation: Only visible for Fast2test members. You can sign-up / login (it's free).
A data scientist is working on a large dataset in Apache Spark using PySpark. The data scientist has a DataFrame df with columns user_id, product_id, and purchase_amount and needs to perform some operations on this data efficiently.
Which sequence of operations results in transformations that require a shuffle followed by transformations that do not?
- A. df.withColumn("purchase_date", current_date()).where("total_purchase > 50")
- B. df.filter(df.purchase_amount > 100).groupBy("user_id").sum("purchase_amount")
- C. df.groupBy("user_id").agg(sum("purchase_amount").alias("total_purchase")).repartition(10)
- D. df.withColumn("discount", df.purchase_amount * 0.1).select("discount")
Correct Answer: C 🗳️
Explanation: Only visible for Fast2test members. You can sign-up / login (it's free).
5 of 55.
What is the relationship between jobs, stages, and tasks during execution in Apache Spark?
- A. A job contains multiple tasks, and each task contains multiple stages.
- B. A stage contains multiple tasks, and each task contains multiple jobs.
- C. A stage contains multiple jobs, and each job contains multiple tasks.
- D. A job contains multiple stages, and each stage contains multiple tasks.
Correct Answer: D 🗳️
Explanation: Only visible for Fast2test members. You can sign-up / login (it's free).
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