PySpark Certification Practice Test 11
Spark Optimization Scenarios
PySpark Certification Practice Test 11
Spark Optimization Scenarios
Insightful Saga — Modern Data Engineering Certification Preparation
Question 201
A PySpark job joins: - Sales Table = 4 TB - Product Lookup = 15 MB Spark UI shows: - Large shuffle - Long join stage Best optimization?
### Explanation Broadcasting the small lookup table avoids a costly shuffle.
Question 202
Spark UI shows: - 200 Tasks - 199 finish quickly - 1 task runs for 45 minutes Most likely optimization?
Question 203
A DataFrame is used in: - 5 joins - 3 aggregations - 4 transformations Spark recomputes it repeatedly. Best optimization?
Question 204
A write operation creates: ```text 10,000 small files ``` Performance issues appear downstream. Best optimization?
Question 205
A job contains multiple joins. Spark 3.x is available. The team wants Spark to optimize join strategies automatically. Best feature?
Question 206
A table is partitioned by: ```text year month ``` Query: ```sql WHERE year = 2025 ``` Which optimization helps?
Question 207
Spark UI shows: ```text Shuffle Read = Extremely High ``` during a join. What should be investigated first?
Question 208
A dataset contains: ```text 5 Billion Rows ``` Output needs only: ```text 100 Sample Records ``` Best approach?
Question 209
A job repeatedly scans the same source files. Which optimization may help?
Question 210
A Data Engineer writes: ```python repartition(5000) ``` for a table containing only 100,000 rows. What should be reviewed?
Question 211
A Spark job performs: ```python orderBy() ``` on 2 billion rows. What major operation should be expected?
Question 212
A dimension table is used in nearly every ETL job. Its size: ```text 5 MB ``` Best optimization?
Question 213
A Spark application experiences memory pressure. The cached dataset is rarely reused. Best action?
Question 214
Spark UI shows: ```text Thousands of tiny tasks ``` with minimal processing per task. Which area should be investigated?
Question 215
A job fails because one partition contains most of the data. Which issue is occurring?
Question 216
A company stores data in Delta format. Queries always filter by: ```text customer_id ``` What should engineers evaluate?
Question 217
Spark UI indicates: ```text Task Serialization Time ``` is unusually high. First review?
Question 218
A DataFrame is used only once. A developer proposes: ```python cache() ``` Best recommendation?
Question 219
A Spark SQL query scans an entire table even though a filter is applied. What optimization should be verified?
Question 220
A Senior Data Engineer is reviewing a slow PySpark job. Which area generally provides the largest performance gains?
### Explanation In large-scale Spark environments, performance improvements typically come from reducing shuffles, optimizing joins, handling skew, pruning partitions, and minimizing unnecessary data movement. --- --- title: PySpark Certification Practice Test 12 description: Principal Engineer and Architecture Scenarios --- # PySpark Certification Practice Test 12