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Google Cloud Certified Professional Data Engineer Exam
- Topic 1: Designing data processing systems: It delves into designing for security and compliance, reliability and fidelity, flexibility and portability, and data migrations.
- Topic 2: Ingesting and processing the data: The topic discusses planning of the data pipelines, building the pipelines, acquisition and import of data, and deploying and operationalizing the pipelines.
- Topic 3: Storing the data: This topic explains how to select storage systems and how to plan using a data warehouse. Additionally, it discusses how to design for a data mesh.
- Topic 4: Preparing and using data for analysis: Questions about data for visualization, data sharing, and assessment of data may appear.
- Topic 5: Maintaining and automating data workloads: It discusses optimizing resources, automation and repeatability design, and organization of workloads as per business requirements. Lastly, the topic explains monitoring and troubleshooting processes and maintaining awareness of failures.
Free Google Google Cloud Certified Professional Data Engineer Exam Actual Questions
The questions for Google Cloud Certified Professional Data Engineer were last updated On Apr. 05, 2024
You are loading CSV files from Cloud Storage to BigQuery. The files have known data quality issues, including mismatched data types, such as STRINGS and INT64s in the same column, and inconsistent formatting of values such as phone numbers or addresses. You need to create the data pipeline to maintain data quality and perform the required cleansing and transformation. What should you do?
Data Fusion's advantages:
Visual interface: Offers a user-friendly interface for designing data pipelines without extensive coding, making it accessible to a wider range of users.
Built-in transformations: Includes a wide range of pre-built transformations to handle common data quality issues, such as:
Data type conversions
Data cleansing (e.g., removing invalid characters, correcting formatting)
Data validation (e.g., checking for missing values, enforcing constraints)
Data enrichment (e.g., adding derived fields, joining with other datasets)
Custom transformations: Allows for custom transformations using SQL or Java code for more complex cleaning tasks.
Scalability: Can handle large datasets efficiently, making it suitable for processing CSV files with potential data quality issues.
Integration with BigQuery: Integrates seamlessly with BigQuery, allowing for direct loading of transformed data.
You want to create a machine learning model using BigQuery ML and create an endpoint foe hosting the model using Vertex Al. This will enable the processing of continuous streaming data in near-real time from multiple vendors. The data may contain invalid values. What should you do?
Dataflow provides a scalable and flexible way to process and clean the incoming data in real-time before loading it into BigQuery.
You have a data processing application that runs on Google Kubernetes Engine (GKE). Containers need to be launched with their latest available configurations from a container registry. Your GKE nodes need to have GPUs. local SSDs, and 8 Gbps bandwidth. You want to efficiently provision the data processing infrastructure and manage the deployment process. What should you do?
You need to look at BigQuery data from a specific table multiple times a day. The underlying table you are querying is several petabytes in size, but you want to filter your data and provide simple aggregations to downstream users. You want to run queries faster and get up-to-date insights quicker. What should you do?
Materialized views are precomputed views that periodically cache the results of a query for increased performance and efficiency. BigQuery leverages precomputed results from materialized views and whenever possible reads only changes from the base tables to compute up-to-date results. Materialized views can significantly improve the performance of workloads that have the characteristic of common and repeated queries. Materialized views can also optimize queries with high computation cost and small dataset results, such as filtering and aggregating large tables. Materialized views are refreshed automatically when the base tables change, so they always return fresh data. Materialized views can also be used by the BigQuery optimizer to process queries to the base tables, if any part of the query can be resolved by querying the materialized view.Reference:
Introduction to materialized views
BigQuery Materialized View Simplified: Steps to Create and 3 Best Practices
You are building a data pipeline on Google Cloud. You need to prepare data using a casual method for a
machine-learning process. You want to support a logistic regression model. You also need to monitor and
adjust for null values, which must remain real-valued and cannot be removed. What should you do?
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anderson
25 days ago