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Google Professional-Data-Engineer Exam Syllabus Topics:

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

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Google Certified Professional Data Engineer Exam Sample Questions (Q161-Q166):

NEW QUESTION # 161
You are implementing security best practices on your data pipeline. Currently, you are manually executing jobs as the Project Owner. You want to automate these jobs by taking nightly batch files containing non- public information from Google Cloud Storage, processing them with a Spark Scala job on a Google Cloud Dataproc cluster, and depositing the results into Google BigQuery.
How should you securely run this workload?

A. Use a user account with the Project Viewer role on the Cloud Dataproc cluster to read the batch files and write to BigQuery
B. Use a service account with the ability to read the batch files and to write to BigQuery
C. Restrict the Google Cloud Storage bucket so only you can see the files
D. Grant the Project Owner role to a service account, and run the job with it

Answer: B

NEW QUESTION # 162
You are operating a streaming Cloud Dataflow pipeline. Your engineers have a new version of the pipeline with a different windowing algorithm and triggering strategy. You want to update the running pipeline with the new version. You want to ensure that no data is lost during the update. What should you do?

A. Update the Cloud Dataflow pipeline inflight by passing the --update option with the --jobName set to a new unique job name
B. Stop the Cloud Dataflow pipeline with the Cancel option. Create a new Cloud Dataflow job with the updated code
C. Stop the Cloud Dataflow pipeline with the Drain option. Create a new Cloud Dataflow job with the updated code
D. Update the Cloud Dataflow pipeline inflight by passing the --update option with the --jobName set to the existing job name

Answer: D

Explanation:
References:

NEW QUESTION # 163
Which of the following is not possible using primitive roles?

A. Give GroupA owner access and GroupB editor access for all datasets in a project.
B. Give a user access to view all datasets in a project, but not run queries on them.
C. Give a user viewer access to BigQuery and owner access to Google Compute Engine instances.
D. Give UserA owner access and UserB editor access for all datasets in a project.

Answer: B

Explanation:
Explanation
Primitive roles can be used to give owner, editor, or viewer access to a user or group, but they can't be used to separate data access permissions from job-running permissions.
Reference: https://cloud.google.com/bigquery/docs/access-control#primitive_iam_roles

NEW QUESTION # 164
As your organization expands its usage of GCP, many teams have started to create their own projects.
Projects are further multiplied to accommodate different stages of deployments and target audiences. Each project requires unique access control configurations. The central IT team needs to have access to all projects.
Furthermore, data from Cloud Storage buckets and BigQuery datasets must be shared for use in other projects in an ad hoc way. You want to simplify access control management by minimizing the number of policies.
Which two steps should you take? (Choose two.)

A. Introduce resource hierarchy to leverage access control policy inheritance.
B. Use Cloud Deployment Manager to automate access provision.
C. For each Cloud Storage bucket or BigQuery dataset, decide which projects need access. Find all the active members who have access to these projects, and create a Cloud IAM policy to grant access to all these users.
D. Create distinct groups for various teams, and specify groups in Cloud IAM policies.
E. Only use service accounts when sharing data for Cloud Storage buckets and BigQuery datasets.

Answer: B,D

NEW QUESTION # 165
You used Cloud Dataprep to create a recipe on a sample of data in a BigQuery table. You want to reuse this recipe on a daily upload of data with the same schema, after the load job with variable execution time completes. What should you do?

A. Create a cron schedule in Cloud Dataprep.
B. Export the recipe as a Cloud Dataprep template, and create a job in Cloud Scheduler.
C. Create an App Engine cron job to schedule the execution of the Cloud Dataprep job.
D. Export the Cloud Dataprep job as a Cloud Dataflow template, and incorporate it into a Cloud Composer job.

Answer: D

Explanation:
Topic 1, Flowlogistic Case Study
Company Overview
Flowlogistic is a leading logistics and supply chain provider. They help businesses throughout the world manage their resources and transport them to their final destination. The company has grown rapidly, expanding their offerings to include rail, truck, aircraft, and oceanic shipping.
Company Background
The company started as a regional trucking company, and then expanded into other logistics market. Because they have not updated their infrastructure, managing and tracking orders and shipments has become a bottleneck. To improve operations, Flowlogistic developed proprietary technology for tracking shipments in real time at the parcel level. However, they are unable to deploy it because their technology stack, based on Apache Kafka, cannot support the processing volume. In addition, Flowlogistic wants to further analyze their orders and shipments to determine how best to deploy their resources.
Solution Concept
Flowlogistic wants to implement two concepts using the cloud:
Use their proprietary technology in a real-time inventory-tracking system that indicates the location of their loads Perform analytics on all their orders and shipment logs, which contain both structured and unstructured data, to determine how best to deploy resources, which markets to expand info. They also want to use predictive analytics to learn earlier when a shipment will be delayed.
Existing Technical Environment
Flowlogistic architecture resides in a single data center:
Databases
8 physical servers in 2 clusters
SQL Server - user data, inventory, static data
3 physical servers
Cassandra - metadata, tracking messages
10 Kafka servers - tracking message aggregation and batch insert
Application servers - customer front end, middleware for order/customs
60 virtual machines across 20 physical servers
Tomcat - Java services
Nginx - static content
Batch servers
Storage appliances
iSCSI for virtual machine (VM) hosts
Fibre Channel storage area network (FC SAN) - SQL server storage
Network-attached storage (NAS) image storage, logs, backups
Apache Hadoop /Spark servers
Core Data Lake
Data analysis workloads
20 miscellaneous servers
Jenkins, monitoring, bastion hosts,
Business Requirements
Build a reliable and reproducible environment with scaled panty of production.
Aggregate data in a centralized Data Lake for analysis
Use historical data to perform predictive analytics on future shipments Accurately track every shipment worldwide using proprietary technology Improve business agility and speed of innovation through rapid provisioning of new resources Analyze and optimize architecture for performance in the cloud Migrate fully to the cloud if all other requirements are met Technical Requirements Handle both streaming and batch data Migrate existing Hadoop workloads Ensure architecture is scalable and elastic to meet the changing demands of the company.
Use managed services whenever possible
Encrypt data flight and at rest
Connect a VPN between the production data center and cloud environment
SEO Statement
We have grown so quickly that our inability to upgrade our infrastructure is really hampering further growth and efficiency. We are efficient at moving shipments around the world, but we are inefficient at moving data around.
We need to organize our information so we can more easily understand where our customers are and what they are shipping.
CTO Statement
IT has never been a priority for us, so as our data has grown, we have not invested enough in our technology. I have a good staff to manage IT, but they are so busy managing our infrastructure that I cannot get them to do the things that really matter, such as organizing our data, building the analytics, and figuring out how to implement the CFO' s tracking technology.
CFO Statement
Part of our competitive advantage is that we penalize ourselves for late shipments and deliveries. Knowing where out shipments are at all times has a direct correlation to our bottom line and profitability. Additionally, I don't want to commit capital to building out a server environment.

NEW QUESTION # 166
......

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