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Databricks Associate-Developer-Apache-Spark-3.5 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Structured Streaming | 10% | - Streaming concepts and architecture - Defining streaming queries - Output modes and triggers - Fault tolerance and state management |
| Troubleshooting and Tuning Apache Spark DataFrame API Applications | 10% | - Managing memory and resource usage - Optimizing transformations and actions - Identifying performance bottlenecks - Debugging and logging |
| Using Spark SQL | 20% | - Integrating Spark SQL with DataFrames - Using catalog and metadata APIs - Working with functions and expressions - Running SQL queries |
| Using Pandas API on Apache Spark | 5% | - Converting between Pandas and Spark structures - Key differences and limitations - Overview of Pandas API on Spark |
| Apache Spark Architecture and Components | 20% | - Fault tolerance and garbage collection - Execution and deployment modes - Execution hierarchy and lazy evaluation - Shuffling, actions, and broadcasting - Spark architecture overview |
| Developing Apache Spark DataFrame API Applications | 30% | - Partitioning and bucketing data - Filtering, sorting, and aggregating data - Reading and writing data in various formats - Selecting, renaming, and modifying columns - User-defined functions (UDFs) - Joining and combining datasets - Handling missing values and data quality - Creating DataFrames and defining schemas |
| Using Spark Connect to Deploy Applications | 5% | - Spark Connect architecture - Running applications via Spark Connect - Connecting to remote Spark clusters |
Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions:
10 of 55.
What is the benefit of using Pandas API on Spark for data transformations?
- A. It is available only with Python, thereby reducing the learning curve.
- B. It computes results immediately using eager execution.
- C. It runs on a single node only, utilizing memory efficiently.
- D. It executes queries faster using all the available cores in the cluster as well as provides Pandas's rich set of features.
Correct Answer: D 🗳️
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48 of 55.
A data engineer needs to join multiple DataFrames and has written the following code:
from pyspark.sql.functions import broadcast
data1 = [(1, "A"), (2, "B")]
data2 = [(1, "X"), (2, "Y")]
data3 = [(1, "M"), (2, "N")]
df1 = spark.createDataFrame(data1, ["id", "val1"])
df2 = spark.createDataFrame(data2, ["id", "val2"])
df3 = spark.createDataFrame(data3, ["id", "val3"])
df_joined = df1.join(broadcast(df2), "id", "inner") \
.join(broadcast(df3), "id", "inner")
What will be the output of this code?
- A. The code will fail because only one broadcast join can be performed at a time.
- B. The code will result in an error because broadcast() must be called before the joins, not inline.
- C. The code will fail because the second join condition (df2.id == df3.id) is incorrect.
- D. The code will work correctly and perform two broadcast joins simultaneously to join df1 with df2, and then the result with df3.
Correct Answer: D 🗳️
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A data analyst wants to add a column date derived from a timestamp column.
Options:
- A. dates_df.withColumn("date", f.date_format("timestamp", "yyyy-MM-dd")).show()
- B. dates_df.withColumn("date", f.unix_timestamp("timestamp")).show()
- C. dates_df.withColumn("date", f.to_date("timestamp")).show()
- D. dates_df.withColumn("date", f.from_unixtime("timestamp")).show()
Correct Answer: C 🗳️
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The following code fragment results in an error:
@F.udf(T.IntegerType())
def simple_udf(t: str) -> str:
return answer * 3.14159
Which code fragment should be used instead?
- A. @F.udf(T.IntegerType())
def simple_udf(t: int) -> int:
return t * 3.14159 - B. @F.udf(T.IntegerType())
def simple_udf(t: float) -> float:
return t * 3.14159 - C. @F.udf(T.DoubleType())
def simple_udf(t: float) -> float:
return t * 3.14159 - D. @F.udf(T.DoubleType())
def simple_udf(t: int) -> int:
return t * 3.14159
Correct Answer: C 🗳️
Explanation: Only visible for ExamsTorrent 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 🗳️
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