PySpark Certification Practice Test 05

Production Project Walkthrough Scenarios

Question 1 of 20

📋 Multiple Choice

Question 81

A newly joined Data Engineer at a retail company receives daily sales files from 2,000 stores. The data contains duplicate transactions because some stores resend files after network failures. Before loading into the Gold layer, management wants only unique transactions. What should be implemented first?

✓

A. Deduplication Logic

B. Broadcast Join

C. Repartition

D. Cache

Answer

✅ A. Deduplication Logic

Answered: 0 / 20

PySpark Certification Practice Test 05

Production Project Walkthrough Scenarios

Insightful Saga — Modern Data Engineering Certification Preparation

Question 81

A. Deduplication Logic

B. Broadcast Join

C. Repartition

D. Cache

Answer: ✅ A. Deduplication Logic

Question 82

A banking company receives customer account updates every day. Business users must see both: - Current Account Details - Historical Account Changes Which design pattern should be implemented?

A. SCD Type 2

B. SCD Type 1

C. Full Refresh

D. Truncate and Load

Answer: ✅ A. SCD Type 2

Question 83

A healthcare company processes 500 million claim records daily. Only 10,000 records change each day. Management wants to reduce processing time. What is the BEST recommendation?

A. Incremental Processing

B. Full Reload

C. Collect Dataset

D. Single Partition

Answer: ✅ A. Incremental Processing

Question 84

A telecom company joins a 2 TB transaction table with a 5 MB reference table. Engineers report poor performance. Which optimization should be considered?

A. Broadcast Join

B. Cross Join

C. Collect()

D. Union

Answer: ✅ A. Broadcast Join

Question 85

A retail organization reports sales metrics taking 40 minutes to generate. The same DataFrame is reused by multiple downstream transformations. What should engineers evaluate?

A. Cache/Persist

B. Collect

C. DropDuplicates

D. Coalesce(1)

Answer: ✅ A. Cache/Persist

Question 86

A manufacturing company processes machine sensor data. Business users want alerts whenever temperature exceeds safety limits. Which transformation approach best supports this?

A. withColumn() + when()

B. collect()

C. distinct()

D. union()

Answer: ✅ A. withColumn() + when()

Question 87

A retail company stores customer information in a Bronze table. The Silver layer requires: - Valid customer IDs - No duplicates - Standardized formats What is the PRIMARY goal of the Silver layer?

A. Data Cleansing and Validation

B. Dashboard Reporting

C. Archival Storage

D. Cluster Management

Answer: ✅ A. Data Cleansing and Validation

Question 88

An insurance company notices some records are rejected because mandatory columns contain NULL values. What should be introduced?

A. Data Quality Validation

B. Cross Join

C. Broadcast Hint

D. Collect

Answer: ✅ A. Data Quality Validation

Question 89

A banking workflow identifies customers with unusually large transactions. The business wants: - Normal - High Value - Risk categories. Which PySpark function is commonly used?

A. when()

B. union()

C. repartition()

D. collect()

Answer: ✅ A. when()

Question 90

A healthcare company loads data from multiple hospitals. Schema differences occasionally occur. Engineering leadership wants early detection of schema changes. What should be implemented?

A. Schema Validation Checks

B. More Executors

C. More Partitions

D. Coalesce

Answer: ✅ A. Schema Validation Checks

Question 91

A logistics company wants to identify records arriving multiple times. Which operation is most commonly used?

A. dropDuplicates()

B. count()

C. union()

D. collect()

Answer: ✅ A. dropDuplicates()

Question 92

A retail company creates Gold-level sales metrics. Executives consume the results in Power BI. What is the PRIMARY purpose of the Gold layer?

A. Business Reporting

B. Raw Storage

C. Landing Zone

D. Temporary Processing

Answer: ✅ A. Business Reporting

Question 93

A telecom company receives late-arriving records. Business users still want historical accuracy. Which data engineering concept becomes important?

A. Late Arriving Data Handling

B. Cache Management

C. Driver Scaling

D. Repartitioning Only

Answer: ✅ A. Late Arriving Data Handling

Question 94

A financial institution audits an ETL pipeline. Auditors request: - Records Processed - Start Time - End Time - Status What should the engineering team maintain?

A. Audit Logging

B. Broadcast Tables

C. Caching

D. Union Logic

Answer: ✅ A. Audit Logging

Question 95

A large retailer loads daily inventory files. One corrupted file causes the entire workflow to fail. Management wants resilience. What should be added?

A. Error Handling Framework

B. More Workers

C. More Schemas

D. collect()

Answer: ✅ A. Error Handling Framework

Question 96

A banking company tracks customer status changes. Customers move through: - Prospect - Active - Premium The business wants to analyze transitions over time. Which design is most suitable?

A. Historical Tracking (SCD Type 2)

B. Full Refresh

C. Distinct Only

D. Cache

Answer: ✅ A. Historical Tracking (SCD Type 2)

Question 97

A healthcare ETL pipeline must recover automatically after temporary source outages. What production feature is most important?

A. Retry Strategy

B. More Columns

C. More Aggregations

D. More Unions

Answer: ✅ A. Retry Strategy

Question 98

A telecom company discovers different pipelines calculate customer revenue differently. Executives lose trust in reports. What should be standardized?

A. Business Rules

B. Executor Count

C. Partition Count

D. Cache Usage

Answer: ✅ A. Business Rules

Question 99

A newly joined Senior Data Engineer reviews a PySpark ETL flow. Requirements include: - Scalability - Reusability - Monitoring - Data Quality What architecture characteristic is being emphasized?

A. Production Readiness

B. Local Processing

C. Manual Operations

D. Single-Node Design

Answer: ✅ A. Production Readiness

Question 100

A Principal Data Engineer is asked: "What is the primary objective of a PySpark-based enterprise ETL platform?"

A. Convert raw large-scale data into trusted business-ready information

B. Create maximum partitions

C. Use the largest cluster possible

D. Generate the longest code

Answer: ✅ A. Convert raw large-scale data into trusted business-ready information

Explanation:

### Explanation The goal of enterprise PySpark platforms is not just processing data, but delivering reliable, scalable, high-quality business information that supports decision-making. --- --- title: PySpark Certification Practice Test 06 description: Code Review Scenarios --- # PySpark Certification Practice Test 06