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This exam focuses on the role of a data engineer in successful analytic projects and the various tools and techniques including SQL, NoSQL, the Hadoop ecosystem, Apache Spark, data governance, streaming and IoT data processing, Python, and building data pipelines. This certification will benefit practicing or aspiring data engineers, data scientists, data stewards, or anyone who is responsible for managing or processing data sets. The duration of the Data Engineering Optimize D-DS-OP-23 Exam is 90 minutes. The exam consists of a total of 60 questions. In order to pass the exam, a score of 63% is required.

Exam Topics

The Role of the Data Engineer (5%)

• Describe the skills of a data engineer
• Describe the role of a data engineer in a data analytics project

Data Warehousing with SQL and NoSQL (17%)

• Describe characteristics and performance considerations of a relational database
• Describe relational database schemas and normalization techniques
• Describe use cases and features of various NoSQL tools

Extract-Transform-Load (ETL) Offload with Hadoop and Spark (18%)

• Describe ETL, ELT, and related schedulers
• Describe the Hadoop ecosystem, HDFS, and data ingestion tools
• Describe Apache Spark and its architecture

Data Governance, Security and Privacy for Big Data (20%)

• Describe data governance, key roles, and related models
• Describe metadata and Master Data Management
• Describe security considerations with Hadoop and the Cloud
• Describe the uses of Apache Atlas, Ranger, and Knox
• Describe privacy regulations and ethics

Processing Streaming and IoT Data (20%)

• Describe uses and application of IoT tools
• Describe the Apache Storm system and topology
• Describe the Apache Kafka queueing system and architecture
• Describe Apache Spark - Streaming processing and architecture
• Describe Apache Flink and its architecture
• Describe Pravega and its storage architecture
• Describe EdgeX Foundry and its architecture

Building Data Pipelines with Python (20%)

• Describe Python, reasons to use, and its libraries
• Describe the use of lists, dictionaries, tuples, sets, and strings
• Describe the use of Apache Airflow
• Describe data pipeline best practices

View Online Data Engineering Optimize D-DS-OP-23 Free Questions

1. Which of the following describes Apache Flink?
A. A distributed file storage system
B. A graph processing framework
C. A stream processing framework
D. A batch processing engine
Answer: C

2. Which of the following are characteristics of the Hadoop ecosystem? (Select all that apply)
A. Real-time processing
B. Batch processing
C. Low fault tolerance
D. Scalability
E. Single-node architecture
Answer: BD

3. Which skill is crucial for a data engineer to effectively manage and optimize large-scale data processing systems?
A. Data analysis
B. Front-end development
C. Cloud computing
D. Graphic design
Answer: C

4. What type of expertise is required to be an effective data engineer?
A. Big Data tools
B. Business analysis
C. Project management
D. Accounting
Answer: A

5. What is the primary purpose of Apache Kafka in a data processing architecture?
A. Storing historical data
B. Running machine learning algorithms
C. Processing real-time data streams
D. Running complex SQL queries
Answer: C

6. What is the purpose of sensor operators in Apache Airflow?
A. Perform validation checks in parallel
B. Move data sequentially from one system to another
C. Use triggers to report each successive retry
D. Use a poke method to monitor external processes
Answer: D

7. What is the primary focus of the EdgeX Foundry architecture?
A. Cloud computing
B. Edge computing and IoT devices
C. Big data analytics
D. Quantum computing
Answer: B

8. Which feature of Apache Kafka makes it suitable for handling high-throughput data streams?
A. In-memory data storage
B. Partitioning and replication
C. Only supports batch processing
D. Static schema enforcement
Answer: B

9. Which of the following are data pipeline best practices? (Select all that apply)
A. Using a monolithic design for simplicity
B. Ensuring data quality and validation
C. Avoiding error handling and monitoring
D. Using a single tool for all pipeline components
Answer: B

10. In a data analytics project, what is the primary responsibility of a data engineer?
A. Creating data visualizations
B. Designing machine learning models
C. Building data pipelines and ETL processes
D. Conducting statistical analysis
Answer: C