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Google Professional-Data-Engineer Exam Syllabus Topics:

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

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Google Professional-Data-Engineer PDF Questions - Pass Your Exam With Ease

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Google Certified Professional Data Engineer Exam Sample Questions (Q74-Q79):

NEW QUESTION # 74
Which is not a valid reason for poor Cloud Bigtable performance?

A. There are issues with the network connection.
B. The Cloud Bigtable cluster has too many nodes.
C. The table's schema is not designed correctly.
D. The workload isn't appropriate for Cloud Bigtable.

Answer: B

Explanation:
The Cloud Bigtable cluster doesn't have enough nodes. If your Cloud Bigtable cluster is overloaded, adding more nodes can improve performance. Use the monitoring tools to check whether the cluster is overloaded.
Reference:
https://cloud.google.com/bigtable/docs/performance

NEW QUESTION # 75
You are designing a data mesh on Google Cloud by using Dataplex to manage data in BigQuery and Cloud Storage. You want to simplify data asset permissions. You are creating a customer virtual lake with two user groups:
- Data engineers, which require full data lake access
- Analytic users, which require access to curated data
You need to assign access rights to these two groups. What should you do?

A. 1. Grant the bigquery.dataOwner role on BigQuery datasets and the storage.objectCreator role on Cloud Storage buckets to data engineers.
2. Grant the bigquery.dataViewer role on BigQuery datasets and the storage.objectViewer role on Cloud Storage buckets to analytic users.
B. 1. Grant the dataplex.dataOwner role to the data engineer group on the customer data lake.
2. Grant the dataplex.dataReader role to the analytic user group on the customer curated zone.
C. 1. Grant the dataplex.dataReader role to the data engineer group on the customer data lake.
2. Grant the dataplex.dataOwner to the analytic user group on the customer curated zone.
D. 1. Grant the bigquery.dataViewer role on BigQuery datasets and the storage.objectViewer role on Cloud Storage buckets to data engineers.
2. Grant the bigquery.dataOwner role on BigQuery datasets and the storage.objectEditor role on Cloud Storage buckets to analytic users.

Answer: B

NEW QUESTION # 76
Your company uses separate Google Cloud projects for development, staging, and production. Developers need edit access in the development project, read-only access in the staging project, and no access in the production project. You need an effective and manageable solution to assign and enforce these permissions according to Google-recommended practices. What should you do?

A. Grant individual developers specific IAM roles directly on each project.
B. Configure firewall rules within each project's VPC to block traffic from unauthorized developer machines.
C. Create separate VPC Service Controls perimeters for each project and use access levels.
D. Create Google Groups for access levels, assign developers to groups, and grant the groups the appropriate IAM roles on each project.

Answer: D

Explanation:
Google Cloud's core recommendation for Identity and Access Management (IAM) scalability is to manage access using Google Groups rather than individual users.
* Manageability: Instead of updating IAM policies in three different projects every time a developer joins or leaves the company, you simply add/remove them from a single Google Group (e.g., developers@company.com).
* Enforcement: You apply the Editor role to the group in the Dev Project, the Viewer role in the Staging Project, and no roles in the Production Project. This centrally manages the "Joiner-Mover- Leaver" process.
* Correcting other options:
* A: Firewalls control network traffic, not identity-based access to GCP resources like BigQuery or Dataflow.
* C: Granting roles to individuals is prone to error, difficult to audit, and does not scale.
* D: VPC Service Controls are for mitigating data exfiltration, not for basic user-level role management across environments.
Reference: Google Cloud IAM Best Practices:
"Grant roles to groups instead of individual users. It is easier to update the members of a group than to update the principals in your allow policies... Using groups helps you manage permissions at scale and simplifies auditing." (Source: Best practices for using Google Groups)
"For a consistent security posture, use groups to represent job functions and assign roles to those groups at the appropriate level in the resource hierarchy." (Source: IAM guide - Manage access to projects, folders, and organizations)

NEW QUESTION # 77
You want to migrate an Apache Spark 3 batch job from on-premises to Google Cloud. You need to minimally change the job so that the job reads from Cloud Storage and writes the result to BigQuery. Your job is optimized for Spark, where each executor has 8 vCPU and 16 GB memory, and you want to be able to choose similar settings. You want to minimize installation and management effort to run your job. What should you do?

A. Execute as a Dataproc Serverless job.
B. Execute the job from a new Compute Engine VM.
C. Execute the job in a new Dataproc cluster.
D. Execute the job as part of a deployment in a new Google Kubernetes Engine cluster.

Answer: A

Explanation:
Dataproc Serverless is the ideal choice for running Apache Spark jobs in Google Cloud with minimal changes and effort. It is designed to:
1. Minimize management effort: Dataproc Serverless removes the need to provision or manage clusters, as it dynamically allocates resources based on the job's requirements.
2. Compatibility: It supports Apache Spark 3, allowing you to run your existing batch job with little to no modification.
3. Resource customization: You can specify resource configurations (e.g., number of vCPUs and memory for executors) that align with your job's requirements (e.g., 8 vCPU and 16 GB memory).
4. Cloud-native integration: It natively integrates with Cloud Storage for input data and BigQuery for output, simplifying the migration process.

NEW QUESTION # 78
Your team is working on a binary classification problem. You have trained a support vector machine (SVM) classifier with default parameters, and received an area under the Curve (AUC) of 0.87 on the validation set.
You want to increase the AUC of the model. What should you do?

A. Train a classifier with deep neural networks, because neural networks would always beat SVMs
B. Deploy the model and measure the real-world AUC; it's always higher because of generalization
C. Perform hyperparameter tuning
D. Scale predictions you get out of the model (tune a scaling factor as a hyperparameter) in order to get the highest AUC

Answer: D

NEW QUESTION # 79
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