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Databricks Associate-Developer-Apache-Spark-3.5 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Using Spark SQL | 20% | - Spark SQL Operations
|
| Topic 2: Troubleshooting and Tuning | 10% | - Performance Optimization
|
| Topic 3: Developing Apache Spark DataFrame API Applications | 30% | - DataFrame Operations
|
| Topic 4: Using Spark Connect to Deploy Applications | 5% | - Spark Connect
|
| Topic 5: Using Pandas API on Spark | 5% | - Pandas API
|
| Topic 6: Structured Streaming | 10% | - Streaming Applications
|
| Topic 7: Apache Spark Architecture and Components | 20% | - Spark Architecture
|
Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions:
22 of 55.
A Spark application needs to read multiple Parquet files from a directory where the files have differing but compatible schemas.
The data engineer wants to create a DataFrame that includes all columns from all files.
Which code should the data engineer use to read the Parquet files and include all columns using Apache Spark?
- A. spark.read.parquet("/data/parquet/").option("mergeAllCols", True)
- B. spark.read.option("mergeSchema", True).parquet("/data/parquet/")
- C. spark.read.format("parquet").option("inferSchema", "true").load("/data/parquet/")
- D. spark.read.parquet("/data/parquet/")
Correct Answer: B 🗳️
Explanation: Only visible for Prep4pass members. You can sign-up / login (it's free).
23 of 55.
A data scientist is working with a massive dataset that exceeds the memory capacity of a single machine. The data scientist is considering using Apache Spark™ instead of traditional single-machine languages like standard Python scripts.
Which two advantages does Apache Spark™ offer over a normal single-machine language in this scenario? (Choose 2 answers)
- A. It can distribute data processing tasks across a cluster of machines, enabling horizontal scalability.
- B. It requires specialized hardware to run, making it unsuitable for commodity hardware clusters.
- C. It has built-in fault tolerance, allowing it to recover seamlessly from node failures during computation.
- D. It processes data solely on disk storage, reducing the need for memory resources.
- E. It eliminates the need to write any code, automatically handling all data processing.
Correct Answer: A,C 🗳️
Explanation: Only visible for Prep4pass members. You can sign-up / login (it's free).
A data engineer uses a broadcast variable to share a DataFrame containing millions of rows across executors for lookup purposes. What will be the outcome?
- A. The job will hang indefinitely as Spark will struggle to distribute and serialize such a large broadcast variable to all executors
- B. The job may fail because the driver does not have enough CPU cores to serialize the large DataFrame
- C. The job may fail if the memory on each executor is not large enough to accommodate the DataFrame being broadcasted
- D. The job may fail if the executors do not have enough CPU cores to process the broadcasted dataset
Correct Answer: C 🗳️
Explanation: Only visible for Prep4pass members. You can sign-up / login (it's free).
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.dropna(how="all")
- C. filtered_users = raw_users.na.drop("all")
- D. filtered_users = raw_users.na.drop("any")
Correct Answer: A 🗳️
Explanation: Only visible for Prep4pass members. You can sign-up / login (it's free).
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 provides a way to run Spark applications remotely in any programming language.
- B. It is primarily used for data ingestion into Spark from external sources.
- C. It can be used to interact with any remote cluster using the REST API.
- D. It allows for remote execution of Spark jobs.
Correct Answer: D 🗳️
Explanation: Only visible for Prep4pass members. You can sign-up / login (it's free).
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