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Databricks Associate-Developer-Apache-Spark-3.5 Exam Overview:
| Certification Vendor: | Databricks |
|---|---|
| Exam Name: | Databricks Certified Associate Developer for Apache Spark 3.5 - Python |
| Exam Number: | Associate-Developer-Apache-Spark-3.5 |
| Passing Score: | Not publicly disclosed |
| Exam Duration: | 90 minutes |
| Exam Price: | $200 USD |
| Real Exam Qty: | 45 |
| Available Languages: | English |
| Related Certifications: | Databricks Certified Professional Data Engineer Databricks Certified Data Engineer Associate |
| Exam Format: | Multiple Choice, Multiple Select |
| Certificate Validity Period: | 2 years |
| Sample Questions: | Databricks Associate-Developer-Apache-Spark-3.5 Sample Questions |
| Exam Way: | Online proctored exam via Kryterion Webassessor or authorized test center. |
| Pre Condition: | No formal prerequisites. Databricks recommends at least 6 months of hands-on Apache Spark experience and Python programming knowledge. |
| Official Syllabus URL: | https://www.databricks.com/learn/certification/apache-spark-developer-associate |
Databricks Associate-Developer-Apache-Spark-3.5 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Using Spark SQL | 20% | - Spark SQL Operations
|
| Topic 2: Apache Spark Architecture and Components | 20% | - Spark Architecture
|
| Topic 3: Structured Streaming | 10% | - Streaming Applications
|
| Topic 4: Troubleshooting and Tuning | 10% | - Performance Optimization
|
| Topic 5: Using Pandas API on Spark | 5% | - Pandas API
|
| Topic 6: Using Spark Connect to Deploy Applications | 5% | - Spark Connect
|
| Topic 7: Developing Apache Spark DataFrame API Applications | 30% | - DataFrame Operations
|
Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions:
Which Spark configuration controls the number of tasks that can run in parallel on the executor?
Options:
- A. spark.executor.cores
- B. spark.task.maxFailures
- C. spark.driver.cores
- D. spark.executor.memory
Correct Answer: A 🗳️
Explanation: Only visible for BraindumpQuiz members. You can sign-up / login (it's free).
A data scientist has identified that some records in the user profile table contain null values in any of the fields, and such records should be removed from the dataset before processing. The schema includes fields like user_id, username, date_of_birth, created_ts, etc.
The schema of the user profile table looks like this:
Which block of Spark code can be used to achieve this requirement?
Options:
- A. filtered_df = users_raw_df.na.drop(how='any')
- B. filtered_df = users_raw_df.na.drop(thresh=0)
- C. filtered_df = users_raw_df.na.drop(how='all', thresh=None)
- D. filtered_df = users_raw_df.na.drop(how='all')
Correct Answer: A 🗳️
Explanation: Only visible for BraindumpQuiz members. You can sign-up / login (it's free).
What is the difference between df.cache() and df.persist() in Spark DataFrame?
- A. Both functions perform the same operation. The persist() function provides improved performance as its default storage level is DISK_ONLY.
- B. persist() - Persists the DataFrame with the default storage level (MEMORY_AND_DISK_SER) and cache() - Can be used to set different storage levels to persist the contents of the DataFrame.
- C. cache() - Persists the DataFrame with the default storage level (MEMORY_AND_DISK) and persist() - Can be used to set different storage levels to persist the contents of the DataFrame
- D. Both cache() and persist() can be used to set the default storage level (MEMORY_AND_DISK_SER)
Correct Answer: C 🗳️
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39 of 55.
A Spark developer is developing a Spark application to monitor task performance across a cluster.
One requirement is to track the maximum processing time for tasks on each worker node and consolidate this information on the driver for further analysis.
Which technique should the developer use?
- A. Broadcast a variable to share the maximum time among workers.
- B. Configure the Spark UI to automatically collect maximum times.
- C. Use an RDD action like reduce() to compute the maximum time.
- D. Use an accumulator to record the maximum time on the driver.
Correct Answer: C 🗳️
Explanation: Only visible for BraindumpQuiz members. You can sign-up / login (it's free).
A data engineer is working with a large JSON dataset containing order information. The dataset is stored in a distributed file system and needs to be loaded into a Spark DataFrame for analysis. The data engineer wants to ensure that the schema is correctly defined and that the data is read efficiently.
Which approach should the data scientist use to efficiently load the JSON data into a Spark DataFrame with a predefined schema?
- A. Use spark.read.json() with the inferSchema option set to true
- B. Use spark.read.format("json").load() and then use DataFrame.withColumn() to cast each column to the desired data type.
- C. Use spark.read.json() to load the data, then use DataFrame.printSchema() to view the inferred schema, and finally use DataFrame.cast() to modify column types.
- D. Define a StructType schema and use spark.read.schema(predefinedSchema).json() to load the data.
Correct Answer: D 🗳️
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