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Databricks Associate-Developer-Apache-Spark-3.5 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Apache Spark Architecture and Components | 20% | - Spark architecture overview - Execution and deployment modes - Shuffling, actions, and broadcasting - Execution hierarchy and lazy evaluation - Fault tolerance and garbage collection |
| Using Pandas API on Apache Spark | 5% | - Key differences and limitations - Converting between Pandas and Spark structures - Overview of Pandas API on Spark |
| Structured Streaming | 10% | - Fault tolerance and state management - Streaming concepts and architecture - Output modes and triggers - Defining streaming queries |
| Troubleshooting and Tuning Apache Spark DataFrame API Applications | 10% | - Managing memory and resource usage - Identifying performance bottlenecks - Optimizing transformations and actions - Debugging and logging |
| Developing Apache Spark DataFrame API Applications | 30% | - Filtering, sorting, and aggregating data - Creating DataFrames and defining schemas - User-defined functions (UDFs) - Partitioning and bucketing data - Handling missing values and data quality - Joining and combining datasets - Reading and writing data in various formats - Selecting, renaming, and modifying columns |
| Using Spark Connect to Deploy Applications | 5% | - Spark Connect architecture - Running applications via Spark Connect - Connecting to remote Spark clusters |
| Using Spark SQL | 20% | - Using catalog and metadata APIs - Running SQL queries - Working with functions and expressions - Integrating Spark SQL with DataFrames |
Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions:
Given the code fragment:
import pyspark.pandas as ps
psdf = ps.DataFrame({'col1': [1, 2], 'col2': [3, 4]})
Which method is used to convert a Pandas API on Spark DataFrame (pyspark.pandas.DataFrame) into a standard PySpark DataFrame (pyspark.sql.DataFrame)?
- A. psdf.to_dataframe()
- B. psdf.to_pandas()
- C. psdf.to_pyspark()
- D. psdf.to_spark()
Correct Answer: D 🗳️
Explanation: Only visible for TestBraindump 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.filter(F.lit(min_price) > F.col("price")).count()
- B. result_df = prices_df.where(F.lit("price") > min_price).groupBy().count()
- C. result_df = prices_df.filter(F.col("price") > F.lit(min_price)).agg(F.count("*"))
- D. result_df = prices_df.withColumn("valid_price", when(col("price") > F.lit(min_price), True))
Correct Answer: C 🗳️
Explanation: Only visible for TestBraindump members. You can sign-up / login (it's free).
An MLOps engineer is building a Pandas UDF that applies a language model that translates English strings into Spanish. The initial code is loading the model on every call to the UDF, which is hurting the performance of the data pipeline.
The initial code is:
def in_spanish_inner(df: pd.Series) -> pd.Series:
model = get_translation_model(target_lang='es')
return df.apply(model)
in_spanish = sf.pandas_udf(in_spanish_inner, StringType())
How can the MLOps engineer change this code to reduce how many times the language model is loaded?
- A. Convert the Pandas UDF from a Series → Series UDF to an Iterator[Series] → Iterator[Series] UDF
- B. Convert the Pandas UDF to a PySpark UDF
- C. Convert the Pandas UDF from a Series → Series UDF to a Series → Scalar UDF
- D. Run the in_spanish_inner() function in a mapInPandas() function call
Correct Answer: A 🗳️
Explanation: Only visible for TestBraindump members. You can sign-up / login (it's free).
A developer is trying to join two tables, sales.purchases_fct and sales.customer_dim, using the following code:
fact_df = purch_df.join(cust_df, F.col('customer_id') == F.col('custid')) The developer has discovered that customers in the purchases_fct table that do not exist in the customer_dim table are being dropped from the joined table.
Which change should be made to the code to stop these customer records from being dropped?
- A. fact_df = purch_df.join(cust_df, F.col('customer_id') == F.col('custid'), 'right_outer')
- B. fact_df = purch_df.join(cust_df, F.col('customer_id') == F.col('custid'), 'left')
- C. fact_df = cust_df.join(purch_df, F.col('customer_id') == F.col('custid'))
- D. fact_df = purch_df.join(cust_df, F.col('cust_id') == F.col('customer_id'))
Correct Answer: B 🗳️
Explanation: Only visible for TestBraindump members. You can sign-up / login (it's free).
13 of 55.
A developer needs to produce a Python dictionary using data stored in a small Parquet table, which looks like this:
region_id
region_name
10
North
12
East
14
West
The resulting Python dictionary must contain a mapping of region_id to region_name, containing the smallest 3 region_id values.
Which code fragment meets the requirements?
- A. regions_dict = regions.select("region_id", "region_name").take(3)
- B. regions_dict = dict(regions.orderBy("region_id").limit(3).rdd.map(lambda x: (x.region_id, x.region_name)).collect())
- C. regions_dict = dict(regions.select("region_id", "region_name").rdd.collect())
- D. regions_dict = dict(regions.take(3))
Correct Answer: B 🗳️
Explanation: Only visible for TestBraindump members. You can sign-up / login (it's free).
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