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Google Professional-Data-Engineer Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Maintaining and automating data workloads (~15% of the exam)	15%	- Monitoring data pipelines and data processes 1. Logging, monitoring, and troubleshooting 2. Managing quotas and resource usage - Automating data processes 1. Workflow orchestration 2. Continuous integration and continuous deployment (CI/CD) 3. Scheduling jobs - Designing for reliability and fidelity 1. Planning for monitoring and alerting 2. Performing data quality and validation checks 3. Recovering from failures
Topic 2: Designing data processing systems (~30% of the exam)	30%	- Designing data pipelines 1. Batch processing 2. Integrating with new data sources 3. Streaming (e.g., windowing, late arriving data) 4. Processing logic 5. AI data enrichment 6. Data acquisition and import - Selecting appropriate storage technologies 1. Choosing between BigQuery, Bigtable, Spanner, Cloud SQL, Cloud Storage, Firestore, Memorystore, AlloyDB 2. Mapping storage options to business requirements - Designing data processing resources 1. Cluster sizing and autoscaling 2. Compute options (Dataflow, Dataproc, Dataplex, Cloud Functions, Cloud Run) 3. Cost optimization
Topic 3: Storing the data (~20% of the exam)	20%	- Using a data lake 1. Processing data 2. Monitoring the data lake 3. Managing the lake (data discovery, access, cost controls) - Designing for a data platform 1. Building a data platform using Dataplex, Dataplex Catalog, BigQuery, Cloud Storage 2. Building a federated governance model for distributed data systems - Selecting storage systems 1. Lifecycle management of data 2. Analyzing data access patterns 3. Planning for storage costs and performance - Planning for using a data warehouse 1. Defining architecture to support data access patterns 2. Mapping business requirements 3. Deciding the degree of data normalization 4. Designing the data model
Topic 4: Preparing and using data for analysis (~15% of the exam)	15%	- Preparing data for visualization 1. Connecting to Looker and other BI tools 2. Preparing data for reporting and dashboards - Sharing data securely 1. Publishing datasets 2. Data sharing and collaboration
Topic 5: Ingesting and processing the data (~20% of the exam)	20%	- Deploying and operationalizing the pipelines 1. Job automation and orchestration (Cloud Composer, Workflows) 2. CI/CD for data pipelines - Performing security considerations 1. Data encryption 2. Auditing, privacy, and compliance 3. Identity and Access Management (IAM) - Building and maintaining data structures and databases 1. Planning for analytical and operational use cases 2. Defining data lifecycle

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Google Certified Professional Data Engineer Exam Sample Questions (Q373-Q378):

NEW QUESTION # 373
You are developing a software application using Google's Dataflow SDK, and want to use conditional, for loops and other complex programming structures to create a branching pipeline.
Which component will be used for the data processing operation?

A. Pipeline
B. Transform
C. Sink API
D. PCollection

Answer: B

Explanation:
In Google Cloud, the Dataflow SDK provides a transform component. It is responsible for the data processing operation. You can use conditional, for loops, and other complex programming structure to create a branching pipeline.
Reference:
https://cloud.google.com/dataflow/model/programming-model

NEW QUESTION # 374
You work for an economic consulting firm that helps companies identify economic trends as they happen. As part of your analysis, you use Google BigQuery to correlate customer data with the average prices of the 100 most common goods sold, including bread, gasoline, milk, and others. The average prices of these goods are updated every 30 minutes. You want to make sure this data stays up to date so you can combine it with other data in BigQuery as cheaply as possible. What should you do?

A. Store and update the data in a regional Google Cloud Storage bucket and create a federated data source in BigQuery
B. Store the data in Google Cloud Datastore. Use Google Cloud Dataflow to query BigQuery and combine the data programmatically with the data stored in Cloud Datastore
C. Load the data every 30 minutes into a new partitioned table in BigQuery.
D. Store the data in a file in a regional Google Cloud Storage bucket. Use Cloud Dataflow to query BigQuery and combine the data programmatically with the data stored in Google Cloud Storage.

Answer: B

Explanation:
Explanation

NEW QUESTION # 375
You are working on a sensitive project involving private user data. You have set up a project on Google Cloud Platform to house your work internally. An external consultant is going to assist with coding a complex transformation in a Google Cloud Dataflow pipeline for your project. How should you maintain users' privacy?

A. Create an anonymized sample of the data for the consultant to work with in a different project.
B. Grant the consultant the Viewer role on the project.
C. Create a service account and allow the consultant to log on with it.
D. Grant the consultant the Cloud Dataflow Developer role on the project.

Answer: A

NEW QUESTION # 376
Your organization is modernizing their IT services and migrating to Google Cloud. You need to organize the data that will be stored in Cloud Storage and BigQuery. You need to enable a data mesh approach to share the data between sales, product design, and marketing departments What should you do?

A. 1 Create multiple projects for storage of the data for each of your departments' applications.2 Enable each department to create Cloud Storage buckets and BigQuery datasets.3. Publish the data that each department shared in Analytics Hub.4 Enable all departments to discover and subscribe to the data they need in Analytics Hub.
B. 1Create a project for storage of the data for your organization.2 Create a central Cloud Storage bucket with three folders to store the files for each department.3. Create a central BigQuery dataset with tables prefixed with the department name.4 Give viewer rights for the storage project for the users of your departments.
C. 1Create a project for storage of the data for each of your departments.2 Enable each department to create Cloud Storage buckets and BigQuery datasets.3. Create user groups for authorized readers for each bucket and dataset.4 Enable the IT team to administer the user groups to add or remove users as the departments' request.
D. 1 Create multiple projects for storage of the data for each of your departments' applications.2 Enable each department to create Cloud Storage buckets and BigQuery datasets.3 In Dataplex, map each department to a data lake and the Cloud Storage buckets, and map the BigQuery datasets to zones.4 Enable each department to own and share the data of their data lakes.

Answer: A

Explanation:
Implementing a data mesh approach involves treating data as a product and enabling decentralized data ownership and architecture. The steps outlined in option C support this approach by creating separate projects for each department, which aligns with the principle of domain-oriented decentralized data ownership. By allowing departments to create their own Cloud Storage buckets and BigQuery datasets, it promotes autonomy and self-service. Publishing the data in Analytics Hub facilitates data sharing and discovery across departments, enabling a collaborative environment where data can be easily accessed and utilized by different parts of the organization.
References:
Architecture and functions in a data mesh - Google Cloud
Professional Data Engineer Certification Exam Guide | Learn - Google Cloud Build a Data Mesh with Dataplex | Google Cloud Skills Boost

NEW QUESTION # 377
Your United States-based company has created an application for assessing and responding to user actions. The primary table's data volume grows by 250,000 records per second. Many third parties use your application's APIs to build the functionality into their own frontend applications. Your application's APIs should comply with the following requirements:
* Single global endpoint
* ANSI SQL support
* Consistent access to the most up-to-date data
What should you do?

A. Implement BigQuery with no region selected for storage or processing.
B. Implement Cloud SQL for PostgreSQL with the master in Norht America and read replicas in Asia and Europe.
C. Implement Cloud Bigtable with the primary cluster in North America and secondary clusters in Asia and Europe.
D. Implement Cloud Spanner with the leader in North America and read-only replicas in Asia and Europe.

Answer: D

NEW QUESTION # 378
......

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