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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%	- 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 2: Designing data processing systems (~30% of the exam)	30%	- Designing data processing resources 1. Compute options (Dataflow, Dataproc, Dataplex, Cloud Functions, Cloud Run) 2. Cluster sizing and autoscaling 3. Cost optimization - 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 pipelines 1. Streaming (e.g., windowing, late arriving data) 2. Integrating with new data sources 3. Batch processing 4. AI data enrichment 5. Processing logic 6. Data acquisition and import
Topic 3: Ingesting and processing the data (~20% of the exam)	20%	- Deploying and operationalizing the pipelines 1. CI/CD for data pipelines 2. Job automation and orchestration (Cloud Composer, Workflows) - 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. Defining data lifecycle 2. Planning for analytical and operational use cases
Topic 4: 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. Planning for monitoring and alerting 3. Performing data quality and validation checks - Automating data processes 1. Workflow orchestration 2. Continuous integration and continuous deployment (CI/CD) 3. Scheduling jobs
Topic 5: Storing the data (~20% of the exam)	20%	- 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 - Planning for using a data warehouse 1. Defining architecture to support data access patterns 2. Designing the data model 3. Deciding the degree of data normalization 4. Mapping business requirements - 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. Lifecycle management of data 2. Analyzing data access patterns 3. Planning for storage costs and performance

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Google Certified Professional Data Engineer Exam Professional-Data-Engineer Prüfungsfragen mit Lösungen (Q161-Q166):

161. Frage
How can you get a neural network to learn about relationships between categories in a categorical feature?

A. Create a hash bucket
B. Create a multi-hot column
C. Create an embedding column
D. Create a one-hot column

Antwort: C

Begründung:
There are two problems with one-hot encoding. First, it has high dimensionality, meaning that instead of having just one value, like a continuous feature, it has many values, or dimensions.
This makes computation more time-consuming, especially if a feature has a very large number of categories. The second problem is that it doesn't encode any relationships between the categories. They are completely independent from each other, so the network has no way of knowing which ones are similar to each other.
Both of these problems can be solved by representing a categorical feature with an embedding column. The idea is that each category has a smaller vector with, let's say, 5 values in it. But unlike a one-hot vector, the values are not usually 0. The values are weights, similar to the weights that are used for basic features in a neural network. The difference is that each category has a set of weights (5 of them in this case).
You can think of each value in the embedding vector as a feature of the category. So, if two categories are very similar to each other, then their embedding vectors should be very similar too.
Reference:
https://cloudacademy.com/google/introduction-to-google-cloud-machine-learning-engine-course/a-wide-and-deep-model.html

162. Frage
You are designing a Dataflow pipeline for a batch processing job. You want to mitigate multiple zonal failures at job submission time. What should you do?

A. Create an Eventarc trigger to resubmit the job in case of zonal failure when submitting the job.
B. Set the pipeline staging location as a regional Cloud Storage bucket.
C. Submit duplicate pipelines in two different zones by using the -zone flag.
D. Specify a worker region by using the -region flag.

Antwort: B

Begründung:
By specifying a worker region, you can run your Dataflow pipeline in a multi-zone or multi-region configuration, which provides higher availability and resilience in case of zonal failures1. The -region flag allows you to specify the regional endpoint for your pipeline, which determines the location of the Dataflow service and the default location of the Compute Engine resources1. If you do not specify a zone by using the -zone flag, Dataflow automatically selects a zone within the region for your job workers1. This option is recommended over submitting duplicate pipelines in two different zones, which would incur additional costs and complexity. Setting the pipeline staging location as a regional Cloud Storage bucket does not affect the availability of your pipeline, as the staging location only stores the pipeline code and dependencies2. Creating an Eventarc trigger to resubmit the job in case of zonal failure is not a reliable solution, as it depends on the availability of the Eventarc service and the zonal resources at the time of resubmission. Reference:
1: Pipeline troubleshooting and debugging | Cloud Dataflow | Google Cloud
3: Regional endpoints | Cloud Dataflow | Google Cloud

163. Frage
You are managing a Cloud Dataproc cluster. You need to make a job run faster while minimizing costs, without losing work in progress on your clusters. What should you do?

A. Increase the cluster size with more non-preemptible workers.
B. Increase the cluster size with preemptible worker nodes, and use Cloud Stackdriver to trigger a script to preserve work.
C. Increase the cluster size with preemptible worker nodes, and configure them to forcefully decommission.
D. Increase the cluster size with preemptible worker nodes, and configure them to use graceful decommissioning.

Antwort: D

Begründung:
Explanation
Reference https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/flex

164. Frage
You decided to use Cloud Datastore to ingest vehicle telemetry data in real time. You want to build a storage system that will account for the long-term data growth, while keeping the costs low. You also want to create snapshots of the data periodically, so that you can make a point-in- time (PIT) recovery, or clone a copy of the data for Cloud Datastore in a different environment.
You want to archive these snapshots for a long time. Which two methods can accomplish this?
(Choose two.)

A. Write an application that uses Cloud Datastore client libraries to read all the entities. Treat each entity as a BigQuery table row via BigQuery streaming insert. Assign an export timestamp for each export, and attach it as an extra column for each row. Make sure that the BigQuery table is partitioned using the export timestamp column.
B. Write an application that uses Cloud Datastore client libraries to read all the entities. Format the exported data into a JSON file. Apply compression before storing the data in Cloud Source Repositories.
C. Use managed export, and then import the data into a BigQuery table created just for that export, and delete temporary export files.
D. Use managed export, and then import to Cloud Datastore in a separate project under a unique namespace reserved for that export.
E. Use managed export, and store the data in a Cloud Storage bucket using Nearline or Coldline class.

Antwort: D,E

Begründung:
https://cloud.google.com/datastore/docs/export-import-entities

165. Frage
You are migrating your on-premises data warehouse to BigQuery. One of the upstream data sources resides on a MySQL database that runs in your on-premises data center with no public IP addresses. You want to ensure that the data ingestion into BigQuery is done securely and does not go through the public internet.
What should you do?

A. Use Datastream to replicate data from your on-premises MySQL database to BigQuery. Use Forward- SSH tunnel as the connectivity method to establish a secure tunnel between Datastream and your on- premises MySQL database through a tunnel server in your on-premises data center. Use None as the encryption type when setting up the connection profile in Datastream.
B. Update your existing on-premises ETL tool to write to BigQuery by using the BigQuery Open Database Connectivity (ODBC) driver. Set up the proxy parameter in the Simba. googlebigqueryodbc. ini tile to point to your data center's NAT gateway.
C. Use Datastream to replicate data from your on-premises MySQL database to BigQuery. Set up Cloud Interconnect between your on- premises data center and Google Cloud. Use Private connectivity as the connectivity method and allocate an IP address range within your VPC network to the Datastream connectivity configuration. Use Server-only as the encryption type when setting up the connection profile in Datastream.
D. Use Datastream to replicate data from your on-premises MySQL database to BigQuery. Gather Datastream public IP addresses of the Google Cloud region that will be used to set up the stream. Add those IP addresses to the firewall allowlist of your on-premises data center.Use IP Allovlisting as the connectivity method and Server-only as the encryption type when setting up the connection profile in Datastream.

Antwort: C

Begründung:
To securely ingest data from an on-premises MySQL database into BigQuery without routing through the public internet, using Datastream with Private connectivity over Cloud Interconnect is the best approach.
Here's why:
Datastream for Data Replication:
Datastream provides a managed service for data replication from various sources, including on-premises databases, to Google Cloud services like BigQuery.
Cloud Interconnect:
Cloud Interconnect establishes a private connection between your on-premises data center and Google Cloud, ensuring that data transfer occurs over a secure, private network rather than the public internet.
Private Connectivity:
Using Private connectivity with Datastream leverages the established Cloud Interconnect to securely connect your on-premises MySQL database with Google Cloud. This method ensures that the data does not traverse the public internet.
Encryption:
Using Server-only encryption ensures that data is encrypted in transit between Datastream and BigQuery, adding an extra layer of security.
Steps to Implement:
Set Up Cloud Interconnect:
Establish a Cloud Interconnect between your on-premises data center and Google Cloud to create a private connection.
Configure Datastream:
Set up Datastream to use Private connectivity as the connection method and allocate an IP address range within your VPC network.
Use Server-only encryption to ensure secure data transfer.
Create Connection Profile:
Create a connection profile in Datastream to define the connection parameters, including the use of Cloud Interconnect and Private connectivity.
Reference Links:
Datastream Documentation
Cloud Interconnect Documentation
Setting Up Private Connectivity in Datastream

166. Frage
......

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