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Databricks Associate-Developer-Apache-Spark-3.5 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Troubleshooting and Tuning | 10% | - Performance Optimization
|
| Using Spark Connect to Deploy Applications | 5% | - Spark Connect
|
| Apache Spark Architecture and Components | 20% | - Spark Architecture
|
| Using Pandas API on Spark | 5% | - Pandas API
|
| Developing Apache Spark DataFrame API Applications | 30% | - DataFrame Operations
|
| Structured Streaming | 10% | - Streaming Applications
|
| Using Spark SQL | 20% | - Spark SQL Operations
|
Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions:
Question #1
A developer notices that all the post-shuffle partitions in a dataset are smaller than the value set for spark.sql.adaptive.maxShuffledHashJoinLocalMapThreshold.
Which type of join will Adaptive Query Execution (AQE) choose in this case?
A. A Cartesian join
B. A broadcast nested loop join
C. A sort-merge join
D. A shuffled hash join
Question #2
45 of 55.
Which feature of Spark Connect should be considered when designing an application that plans to enable remote interaction with a Spark cluster?
A. It is primarily used for data ingestion into Spark from external sources.
B. It allows for remote execution of Spark jobs.
C. It can be used to interact with any remote cluster using the REST API.
D. It provides a way to run Spark applications remotely in any programming language.
Question #3
37 of 55.
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 = customerDF \
.withColumn("username", split(col("email"), "@").getItem(0)) \
.withColumn("domain", split(col("email"), "@").getItem(1))
B. customerDF = customerDF.select("email").alias("username", "domain")
C. customerDF = customerDF.withColumn("domain", col("email").split("@")[1])
D. customerDF = customerDF.withColumn("username", regexp_replace(col("email"), "@", ""))
Question #4
6 of 55.
Which components of Apache Spark's Architecture are responsible for carrying out tasks when assigned to them?
A. Executors
B. Driver Nodes
C. CPU Cores
D. Worker Nodes
Question #5
10 of 55.
What is the benefit of using Pandas API on Spark for data transformations?
A. It computes results immediately using eager execution.
B. It is available only with Python, thereby reducing the learning curve.
C. It executes queries faster using all the available cores in the cluster as well as provides Pandas's rich set of features.
D. It runs on a single node only, utilizing memory efficiently.
Solutions:
| Question #1 Correct Answer: D | Question #2 Correct Answer: B | Question #3 Correct Answer: A | Question #4 Correct Answer: A | Question #5 Correct Answer: C |
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