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Google Professional-Data-Engineer Exam Syllabus Topics:

Section	Weight	Objectives
Storing the data (~20% of the exam)	20%	- Planning for using a data warehouse 1. Designing the data model 2. Mapping business requirements 3. Defining architecture to support data access patterns 4. Deciding the degree of data normalization - 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 - Selecting storage systems 1. Analyzing data access patterns 2. Planning for storage costs and performance 3. Lifecycle management of data - Using a data lake 1. Monitoring the data lake 2. Managing the lake (data discovery, access, cost controls) 3. Processing data
Maintaining and automating data workloads (~15% of the exam)	15%	- Automating data processes 1. Continuous integration and continuous deployment (CI/CD) 2. Workflow orchestration 3. Scheduling jobs - Monitoring data pipelines and data processes 1. Logging, monitoring, and troubleshooting 2. Managing quotas and resource usage - Designing for reliability and fidelity 1. Performing data quality and validation checks 2. Recovering from failures 3. Planning for monitoring and alerting
Designing data processing systems (~30% of the exam)	30%	- 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. Compute options (Dataflow, Dataproc, Dataplex, Cloud Functions, Cloud Run) 2. Cluster sizing and autoscaling 3. Cost optimization - Designing data pipelines 1. Data acquisition and import 2. Integrating with new data sources 3. AI data enrichment 4. Processing logic 5. Batch processing 6. Streaming (e.g., windowing, late arriving data)
Ingesting and processing the data (~20% of the exam)	20%	- 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 - Deploying and operationalizing the pipelines 1. Job automation and orchestration (Cloud Composer, Workflows) 2. CI/CD for data pipelines
Preparing and using data for analysis (~15% of the exam)	15%	- Preparing data for visualization 1. Preparing data for reporting and dashboards 2. Connecting to Looker and other BI tools - Sharing data securely 1. Data sharing and collaboration 2. Publishing datasets

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Google Certified Professional Data Engineer Exam Sample Questions (Q345-Q350):

NEW QUESTION # 345
You are designing storage for 20 TB of text files as part of deploying a data pipeline on Google Cloud. Your input data is in CSV format. You want to minimize the cost of querying aggregate values for multiple users who will query the data in Cloud Storage with multiple engines. Which storage service and schema design should you use?

A. Use Cloud Storage for storage. Link as permanent tables in BigQuery for query.
B. Use Cloud Bigtable for storage. Link as permanent tables in BigQuery for query.
C. Use Cloud Storage for storage. Link as temporary tables in BigQuery for query.
D. Use Cloud Bigtable for storage. Install the HBase shell on a Compute Engine instance to query the Cloud Bigtable data.

Answer: D

NEW QUESTION # 346
You need to migrate 1 PB of data from an on-premises data center to Google Cloud. Data transfer time during the migration should take only a few hours. You want to follow Google- recommended practices to facilitate the large data transfer over a secure connection. What should you do?

A. Establish a Cloud VPN connection, start gcloud compute scp jobs in parallel, and run checksums to verify the data.
B. Reduce the data into 3 TB batches, transfer the data using gsutil, and run checksums to verify the data.
C. Establish a Cloud Interconnect connection between the on-premises data center and Google Cloud, and then use the Storage Transfer Service.
D. Use a Transfer Appliance and have engineers manually encrypt, decrypt, and verify the data.

Answer: C

Explanation:
https://cloud.google.com/architecture/migration-to-google-cloud-transferring-your-large-datasets

NEW QUESTION # 347
Your startup has a web application that currently serves customers out of a single region in Asia. You are targeting funding that will allow your startup lo serve customers globally. Your current goal is to optimize for cost, and your post-funding goat is to optimize for global presence and performance. You must use a native JDBC driver. What should you do?

A. Use a Cloud SQL for PostgreSQL highly available instance first, and bagtable with US. Europe, and Asiareplication alter securing funding
B. Use Cloud Spanner to configure a single region instance initially. and then configure multi-region C oud Spanner instances after securing funding.
C. Use a Cloud SOL for PostgreSQL zonal instance first, and Cloud SOL for PostgreSQL with highly available configuration after securing funding.
D. Use a Cloud SQL for PostgreSQL zonal instance first and Bigtable with US. Europe, and Asia after securing funding.

Answer: B

Explanation:
https://cloud.google.com/spanner/docs/instance-configurations#tradeoffs_regional_versus_multi- region_configurations

NEW QUESTION # 348
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?

A. Use Dataflow to provision the data pipeline, and use Cloud Scheduler to run the job.
B. Use GKE to autoscale containers, and use gloud commands to provision the infrastructure.
C. Use Cloud Build to schedule a job using Terraform build to provision the infrastructure and launch with the most current container images.
D. Use Compute Engi.no startup scriots to pull container Images, and use gloud commands to provision the infrastructure.

Answer: C

Explanation:
https://cloud.google.com/architecture/managing-infrastructure-as-code

NEW QUESTION # 349
You migrated a data backend for an application that serves 10 PB of historical product data for analytics. Only the last known state for a product, which is about 10 GB of data, needs to be served through an API to the other applications. You need to choose a cost-effective persistent storage solution that can accommodate the analytics requirements and the API performance of up to 1000 queries per second (QPS) with less than 1 second latency. What should you do?

A. 1. Store the products as a collection in Firestore with each product having a set of historical changes.
2. Use simple and compound queries for analytics.
3. Serve the last state data directly from Firestore to the API.
B. 1. Store the historical data in BigQuery for analytics.
2. Use a materialized view to precompute the last state of a product.
3. Serve the last state data directly from BigQuery to the API.
C. 1. Store the historical data in BigQuery for analytics.
2. In a Cloud SQL table, store the last state of the product after every product change.
3. Serve the last state data directly from Cloud SQL to the API.
D. 1. Store the historical data in Cloud SQL for analytics.
2. In a separate table, store the last state of the product after every product change.
3. Serve the last state data directly from Cloud SQL to the API.

Answer: B

NEW QUESTION # 350
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