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Databricks Associate-Developer-Apache-Spark-3.5 valid - in .pdf
- Exam Code: Associate-Developer-Apache-Spark-3.5
- Exam Name: Databricks Certified Associate Developer for Apache Spark 3.5 - Python
- Q & A: 135 Questions and Answers
- PDF Price: $59.99
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- Exam Code: Associate-Developer-Apache-Spark-3.5
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- Exam Code: Associate-Developer-Apache-Spark-3.5
- Exam Name: Databricks Certified Associate Developer for Apache Spark 3.5 - Python
- Q & A: 135 Questions and Answers
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Databricks Associate-Developer-Apache-Spark-3.5 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Apache Spark Architecture and Components | 20% | - Execution and deployment modes - Spark architecture overview - Shuffling, actions, and broadcasting - Execution hierarchy and lazy evaluation - Fault tolerance and garbage collection |
| Topic 2: Structured Streaming | 10% | - Fault tolerance and state management - Defining streaming queries - Output modes and triggers - Streaming concepts and architecture |
| Topic 3: Using Pandas API on Apache Spark | 5% | - Overview of Pandas API on Spark - Converting between Pandas and Spark structures - Key differences and limitations |
| Topic 4: Using Spark Connect to Deploy Applications | 5% | - Connecting to remote Spark clusters - Spark Connect architecture - Running applications via Spark Connect |
| Topic 5: Developing Apache Spark DataFrame API Applications | 30% | - Joining and combining datasets - Partitioning and bucketing data - Creating DataFrames and defining schemas - Selecting, renaming, and modifying columns - User-defined functions (UDFs) - Reading and writing data in various formats - Filtering, sorting, and aggregating data - Handling missing values and data quality |
| Topic 6: Using Spark SQL | 20% | - Running SQL queries - Working with functions and expressions - Using catalog and metadata APIs - Integrating Spark SQL with DataFrames |
| Topic 7: Troubleshooting and Tuning Apache Spark DataFrame API Applications | 10% | - Debugging and logging - Optimizing transformations and actions - Managing memory and resource usage - Identifying performance bottlenecks |
Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions:
Question #1
4 of 55.
A developer is working on a Spark application that processes a large dataset using SQL queries. Despite having a large cluster, the developer notices that the job is underutilizing the available resources. Executors remain idle for most of the time, and logs reveal that the number of tasks per stage is very low. The developer suspects that this is causing suboptimal cluster performance.
Which action should the developer take to improve cluster utilization?
- A. Enable dynamic resource allocation to scale resources as needed
- B. Increase the value of spark.sql.shuffle.partitions
- C. Reduce the value of spark.sql.shuffle.partitions
- D. Increase the size of the dataset to create more partitions
Correct Answer: B 🗳️
Explanation: Only visible for ValidBraindumps members. You can sign-up / login (it's free).
Question #2
24 of 55.
Which code should be used to display the schema of the Parquet file stored in the location events.parquet?
- A. spark.sql("SELECT schema FROM events.parquet").show()
- B. spark.read.format("parquet").load("events.parquet").show()
- C. spark.sql("SELECT * FROM events.parquet").show()
- D. spark.read.parquet("events.parquet").printSchema()
Correct Answer: D 🗳️
Explanation: Only visible for ValidBraindumps members. You can sign-up / login (it's free).
Question #3
3 of 55. A data engineer observes that the upstream streaming source feeds the event table frequently and sends duplicate records. Upon analyzing the current production table, the data engineer found that the time difference in the event_timestamp column of the duplicate records is, at most, 30 minutes.
To remove the duplicates, the engineer adds the code:
df = df.withWatermark("event_timestamp", "30 minutes")
What is the result?
- A. It is not able to handle deduplication in this scenario.
- B. It removes all duplicates regardless of when they arrive.
- C. It accepts watermarks in seconds and the code results in an error.
- D. It removes duplicates that arrive within the 30-minute window specified by the watermark.
Correct Answer: D 🗳️
Explanation: Only visible for ValidBraindumps members. You can sign-up / login (it's free).
Question #4
35 of 55.
A data engineer is building a Structured Streaming pipeline and wants it to recover from failures or intentional shutdowns by continuing where it left off.
How can this be achieved?
- A. By configuring the option checkpointLocation during writeStream.
- B. By configuring the option recoveryLocation during SparkSession initialization.
- C. By configuring the option checkpointLocation during readStream.
- D. By configuring the option recoveryLocation during writeStream.
Correct Answer: A 🗳️
Explanation: Only visible for ValidBraindumps members. You can sign-up / login (it's free).
Question #5
A data scientist is analyzing a large dataset and has written a PySpark script that includes several transformations and actions on a DataFrame. The script ends with a collect() action to retrieve the results.
How does Apache Spark™'s execution hierarchy process the operations when the data scientist runs this script?
- A. The entire script is treated as a single job, which is then divided into multiple stages, and each stage is further divided into tasks based on data partitions.
- B. The collect() action triggers a job, which is divided into stages at shuffle boundaries, and each stage is split into tasks that operate on individual data partitions.
- C. Spark creates a single task for each transformation and action in the script, and these tasks are grouped into stages and jobs based on their dependencies.
- D. The script is first divided into multiple applications, then each application is split into jobs, stages, and finally tasks.
Correct Answer: B 🗳️
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