PySpark Certification Practice Test 06

Code Review Scenarios

Question 1 of 20

📋 Multiple Choice

Question 101

A developer submits: ```python large_df.collect() ``` The dataset contains 500 million records. What is the BEST review comment?

A. Use additional joins

B. Use collect() more frequently

✓

C. Avoid collect() on very large datasets because it may crash the Driver

D. Increase the number of columns

Answer

✅ C

Answered: 0 / 20

PySpark Certification Practice Test 06

Code Review Scenarios

Insightful Saga — Modern Data Engineering Certification Preparation

Question 101

A developer submits: ```python large_df.collect() ``` The dataset contains 500 million records. What is the BEST review comment?

A. Use additional joins

B. Use collect() more frequently

C. Avoid collect() on very large datasets because it may crash the Driver

D. Increase the number of columns

Answer: ✅ C

Question 102

Code Review Note: A 2 TB transaction table is joined with a 20 MB lookup table. The developer uses a regular join. What is the BEST review recommendation?

A. Consider a Broadcast Join

B. Repartition to one partition

C. Convert to CSV first

D. Use collect()

Answer: ✅ A

Question 103

A developer writes: ```python df.repartition(1) ``` before writing a 1 TB dataset. What is the BEST review comment?

A. This eliminates shuffles

B. This may create a performance bottleneck

C. This improves scalability

D. This improves parallelism

Answer: ✅ B

Question 104

A developer applies the same expensive transformation chain three times. What is the BEST recommendation?

A. Create a Cross Join

B. Add more columns

C. Consider caching the intermediate DataFrame

D. Use collect()

Answer: ✅ C

Question 105

A code review finds: ```python df.filter(...) .show() .show() .show() ``` What is the BEST observation?

A. show() improves performance

B. Multiple actions may trigger repeated computation

C. show() creates partitions

D. show() removes shuffles

Answer: ✅ B

Question 106

A developer uses: ```python df.withColumn(...) .withColumn(...) .withColumn(...) ``` 25 times. What is the BEST review feedback?

A. Large chains may impact readability and maintainability

B. Spark cannot support withColumn()

C. withColumn always causes failure

D. Remove all transformations

Answer: ✅ A

Question 107

A code review shows: ```python df.count() ``` used repeatedly for debugging. What is the BEST recommendation?

A. Replace with repartition()

B. Convert to RDD

C. Minimize unnecessary actions

D. Count more frequently

Answer: ✅ C

Question 108

A developer joins customer and transaction data. Duplicate customer records appear. What is the BEST review question?

A. Add additional columns

B. Was the join key unique?

C. Create another join

D. Increase partitions

Answer: ✅ B

Question 109

A developer hardcodes: ```python "/mnt/dev/customer/" ``` inside ETL logic. What is the BEST review feedback?

A. Add more hardcoded paths

B. Convert to broadcast

C. Externalize environment-specific configuration

D. Use collect()

Answer: ✅ C

Question 110

A review identifies no validation checks before writing Gold tables. What is the BEST recommendation?

A. Increase Cluster Size

B. Remove Aggregations

C. Add Data Quality Validation

D. Use Cross Join

Answer: ✅ C

Question 111

A developer uses: ```python dropDuplicates() ``` without specifying business keys. What is the BEST review comment?

A. Confirm deduplication business logic

B. Use more partitions

C. Add collect()

D. Convert to JSON

Answer: ✅ A

Question 112

A code review reveals transformations are repeated across many notebooks. What is the BEST engineering recommendation?

A. Create reusable functions/modules

B. Use more joins

C. Increase workers

D. Duplicate code further

Answer: ✅ A

Question 113

A developer writes: ```python df.write.mode("overwrite") ``` for a production customer dimension. What is the BEST review question?

A. Increase executors

B. Use collect()

C. Add more partitions

D. Are we intentionally replacing all existing data?

Answer: ✅ D

Question 114

A review identifies no audit columns such as: - created_date - updated_date - load_timestamp What is the BEST recommendation?

A. Add audit metadata

B. Remove timestamps entirely

C. Add more joins

D. Increase partitions

Answer: ✅ A

Question 115

A developer processes 800 million records using a Python UDF. Performance is very poor. What is the BEST review recommendation?

A. Prefer built-in Spark functions when possible

B. Add more show()

C. Use more UDFs

D. Convert to collect()

Answer: ✅ A

Question 116

A review shows: ```python df.cache() ``` but the DataFrame is only used once. What is the BEST comment?

A. Cache may be unnecessary

B. Convert to RDD

C. Cache everything

D. Increase memory only

Answer: ✅ A

Question 117

A developer handles failed records by dropping them silently. What is the BEST review feedback?

A. Remove validations

B. Implement error tracking and logging

C. Ignore bad data

D. Add more partitions

Answer: ✅ B

Question 118

A code review reveals there is no retry or failure handling in a production ingestion process. What is the BEST recommendation?

A. Disable monitoring

B. Create additional joins

C. Use collect()

D. Improve resiliency and recovery logic

Answer: ✅ D

Question 119

A developer created an ETL process that works for current data volume but may struggle if volume grows 10x. What is the BEST review concern?

A. Column ordering

B. Scalability

C. Notebook color theme

D. Variable names

Answer: ✅ B

Question 120

A Principal Data Engineer reviews a PySpark project. Which review comment provides the MOST enterprise value?

A. Add more comments than logic

B. Optimize for reliability, scalability, maintainability, and data quality

C. Increase notebook cells

D. Maximize line count

Answer: ✅ B

Explanation:

### Explanation Enterprise PySpark development is about building scalable, reliable, maintainable, and high-quality data systems rather than just making code run successfully. --- --- title: PySpark Certification Practice Test 07 description: Find The Problem In The Code --- # PySpark Certification Practice Test 07