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Google Professional-Data-Engineer Exam Syllabus Topics:

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

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Google Certified Professional Data Engineer Exam Sample Questions (Q377-Q382):

NEW QUESTION # 377
You are developing an Apache Beam pipeline to extract data from a Cloud SQL instance by using JdbclO.
You have two projects running in Google Cloud. The pipeline will be deployed and executed on Dataflow in Project A. The Cloud SQL instance is running jn Project B and does not have a public IP address. After deploying the pipeline, you noticed that the pipeline failed to extract data from the Cloud SQL instance due to connection failure. You verified that VPC Service Controls and shared VPC are not in use in these projects.
You want to resolve this error while ensuring that the data does not go through the public internet. What should you do?

A. Set up VPC Network Peering between Project A and Project B. Create a Compute Engine instance without external IP address in Project B on the peered subnet to serve as a proxy server to the Cloud SQL database.
B. Set up VPC Network Peering between Project A and Project B. Add a firewall rule to allow the peered subnet range to access all instances on the network.
C. Add the external IP addresses of the Dataflow worker as authorized networks in the Cloud SOL instance.
D. Turn off the external IP addresses on the Dataflow worker. Enable Cloud NAT in Project A.

Answer: A

Explanation:
* Option A is incorrect because VPC Network Peering alone does not enable connectivity to Cloud SQL instances with private IP addresses. You also need to configure private services access and allocate an IP address range for the service producer network1.
* Option B is incorrect because Cloud NAT does not support Cloud SQL instances with private IP addresses. Cloud NAT only provides outbound connectivity for resources that do not have public IP addresses, such as VMs, GKE clusters, and serverless instances2.
* Option C is correct because it allows you to use a Compute Engine instance as a proxy server to connect to the Cloud SQL database over the peered network. The proxy server does not need an external IP address because it can communicate with the Dataflow workers and the Cloud SQL instance using internal IP addresses. You need to install the Cloud SQL Auth proxy on the proxy server and configure it to use a service account that has the Cloud SQL Client role.
* Option D is incorrect because it requires you to assign public IP addresses to the Dataflow workers, which exposes the data to the public internet. This violates the requirement of ensuring that the data does not go through the public internet. Moreover, adding authorized networks does not work for Cloud SQL instances with private IP addresses.

NEW QUESTION # 378
You are building a model to make clothing recommendations. You know a user's fashion preference is likely to change over time, so you build a data pipeline to stream new data back to the model as it becomes available.
How should you use this data to train the model?

A. Train on the existing data while using the new data as your test set.
B. Train on the new data while using the existing data as your test set.
C. Continuously retrain the model on a combination of existing data and the new data.
D. Continuously retrain the model on just the new data.

Answer: C

NEW QUESTION # 379
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. Scale predictions you get out of the model (tune a scaling factor as a hyperparameter) in order to get the highest AUC
B. Train a classifier with deep neural networks, because neural networks would always beat SVMs
C. Perform hyperparameter tuning
D. Deploy the model and measure the real-world AUC; it's always higher because of generalization

Answer: C

NEW QUESTION # 380
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 Composer
B. Cloud Dataproc
C. Cloud Dataflow
D. Cloud Dataprep

Answer: A

Explanation:
Cloud Composer uses airflow which is open source and can help to orchestrate jobs.

NEW QUESTION # 381
Which of these is NOT a way to customize the software on Dataproc cluster instances?

A. Log into the master node and make changes from there
B. Configure the cluster using Cloud Deployment Manager
C. Modify configuration files using cluster properties
D. Set initialization actions

Answer: B

Explanation:
You can access the master node of the cluster by clicking the SSH button next to it in the Cloud Console.
You can easily use the --properties option of the dataproc command in the Google Cloud SDK to modify many common configuration files when creating a cluster.
When creating a Cloud Dataproc cluster, you can specify initialization actions in executables and/or scripts that Cloud Dataproc will run on all nodes in your Cloud Dataproc cluster immediately after the cluster is set up.
[https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/init-actions] Reference: https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/cluster- properties

NEW QUESTION # 382
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