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Google Professional-Data-Engineer Certification Exam is a highly recognized certification exam for professionals who want to validate their skills and expertise in data engineering. Professional-Data-Engineer exam is designed to test the candidates' knowledge of Google Cloud Platform (GCP) and its data engineering services. It requires a deep understanding of various data tooling, technologies, and services used to build and maintain data pipelines, data processing systems, and machine learning models.
Achieving the Google Certified Professional Data Engineer certification demonstrates to employers and colleagues that an individual has the knowledge and skills necessary to design and build data processing systems on Google Cloud Platform. Google Certified Professional Data Engineer Exam certification can open up new career opportunities and help individuals advance in their current roles.
Reference: https://cloud.google.com/certification/data-engineer
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Google Professional-Data-Engineer Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Building and operationalizing data processing systems | 24% | - Data ingestion and integration
|
| Operationalizing machine learning models | 26% | - ML pipeline integration
|
| Designing data processing systems | 22% | - Batch and streaming data processing design
|
| Ensuring solution quality | 28% | - Reliability and performance
|
Google Certified Professional Data Engineer Sample Questions:
You're training a model to predict housing prices based on an available dataset with real estate properties. Your plan is to train a fully connected neural net, and you've discovered that the dataset contains latitude and longitude of the property. Real estate professionals have told you that the location of the property is highly influential on price, so you'd like to engineer a feature that incorporates this physical dependency. What should you do?
- A. Provide latitude and longitude as input vectors to your neural net.
- B. Create a feature cross of latitude and longitude, bucketize at the minute level and use L1 regularization during optimization.
- C. Create a numeric column from a feature cross of latitude and longitude.
- D. Create a feature cross of latitude and longitude, bucketize it at the minute level and use L2 regularization during optimization.
Correct Answer: B 🗳️
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Your company has a hybrid cloud initiative. You have a complex data pipeline that moves data between cloud provider services and leverages services from each of the cloud providers. Which cloud-native service should you use to orchestrate the entire pipeline?
- A. Cloud Dataprep
- B. Cloud Dataproc
- C. Cloud Dataflow
- D. Cloud Composer
Correct Answer: D 🗳️
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An online brokerage company requires a high volume trade processing architecture. You need to create a secure queuing system that triggers jobs. The jobs will run in Google Cloud and cat the company's Python API to execute trades. You need to efficiently implement a solution. What should you do?
- A. Use Cloud Composer to subscribe to a Pub/Sub tope and can the Python API.
- B. Use a Pub/Sub push subscription to trigger a Cloud Function to pass the data to tie Python API.
- C. Write an application hosted on a Compute Engine instance that makes a push subscription to the Pub/ Sub topic
- D. Write an application that makes a queue in a NoSQL database
Correct Answer: B 🗳️
A shipping company has live package-tracking data that is sent to an Apache Kafka stream in real time. This is then loaded into BigQuery. Analysts in your company want to query the tracking data in BigQuery to analyze geospatial trends in the lifecycle of a package. The table was originally created with ingest-date partitioning. Over time, the query processing time has increased. You need to copy all the data to a new clustered table. What should you do?
- A. Re-create the table using data partitioning on the package delivery date.
- B. Tier older data onto Cloud Storage files and create a BigQuery table using Cloud Storage as an external data source.
- C. Implement clustering in BigQuery on the package-tracking ID column.
- D. Implement clustering in BigQuery on the ingest date column.
Correct Answer: C 🗳️
You are administering a BigQuery dataset that uses a customer-managed encryption key (CMEK). You need to share the dataset with a partner organization that does not have access to your CMEK. What should you do?
- A. Export the tables to parquet files to a Cloud Storage bucket and grant the storageinsights.viewer role on the bucket to the partner organization.
- B. Copy the tables you need to share to a dataset without CMEKs. Create an Analytics Hub listing for this dataset.
- C. Create an authorized view that contains the CMEK to decrypt the data when accessed.
- D. Provide the partner organization a copy of your CMEKs to decrypt the data.
Correct Answer: B 🗳️
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