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

B. Use collect() more frequently

C. Increase the number of columns

D. Use additional joins

Answer

✅ A

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

B. Use collect() more frequently

C. Increase the number of columns

D. Use additional joins

Answer: ✅ A

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. Use collect()

C. Convert to CSV first

D. Repartition to one partition

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 may create a performance bottleneck

B. This improves parallelism

C. This eliminates shuffles

D. This improves scalability

Answer: ✅ A

Question 104

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

A. Consider caching the intermediate DataFrame

B. Add more columns

C. Use collect()

D. Create a Cross Join

Answer: ✅ A

Question 105

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

A. Multiple actions may trigger repeated computation

B. show() improves performance

C. show() creates partitions

D. show() removes shuffles

Answer: ✅ A

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. Minimize unnecessary actions

B. Count more frequently

C. Replace with repartition()

D. Convert to RDD

Answer: ✅ A

Question 108

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

A. Was the join key unique?

B. Add additional columns

C. Increase partitions

D. Create another join

Answer: ✅ A

Question 109

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

A. Externalize environment-specific configuration

B. Add more hardcoded paths

C. Convert to broadcast

D. Use collect()

Answer: ✅ A

Question 110

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

A. Add Data Quality Validation

B. Remove Aggregations

C. Use Cross Join

D. Increase Cluster Size

Answer: ✅ A

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. Convert to JSON

D. Add collect()

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. Duplicate code further

C. Increase workers

D. Use more joins

Answer: ✅ A

Question 113

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

A. Are we intentionally replacing all existing data?

B. Increase executors

C. Add more partitions

D. Use collect()

Answer: ✅ A

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. Use more UDFs

C. Convert to collect()

D. Add more show()

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. Cache everything

C. Increase memory only

D. Convert to RDD

Answer: ✅ A

Question 117

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

A. Implement error tracking and logging

B. Ignore bad data

C. Remove validations

D. Add more partitions

Answer: ✅ A

Question 118

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

A. Improve resiliency and recovery logic

B. Disable monitoring

C. Use collect()

D. Create additional joins

Answer: ✅ A

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. Scalability

B. Variable names

C. Column ordering

D. Notebook color theme

Answer: ✅ A

Question 120

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

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

B. Maximize line count

C. Increase notebook cells

D. Add more comments than logic

Answer: ✅ A

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