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Google Professional-Data-Engineer Exam Syllabus Topics:

Section	Weight	Objectives
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. Publishing datasets 2. Data sharing and collaboration
Ingesting and processing the data (~20% of the exam)	20%	- Deploying and operationalizing the pipelines 1. Job automation and orchestration (Cloud Composer, Workflows) 2. CI/CD for data pipelines - Performing security considerations 1. Auditing, privacy, and compliance 2. Identity and Access Management (IAM) 3. Data encryption - Building and maintaining data structures and databases 1. Planning for analytical and operational use cases 2. Defining data lifecycle
Maintaining and automating data workloads (~15% of the exam)	15%	- Designing for reliability and fidelity 1. Performing data quality and validation checks 2. Recovering from failures 3. Planning for monitoring and alerting - 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. Workflow orchestration 3. Scheduling jobs
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 - 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. Managing the lake (data discovery, access, cost controls) 2. Monitoring the data lake 3. Processing data - Planning for using a data warehouse 1. Deciding the degree of data normalization 2. Defining architecture to support data access patterns 3. Designing the data model 4. Mapping business requirements
Designing data processing systems (~30% of the exam)	30%	- Designing data pipelines 1. Data acquisition and import 2. Batch processing 3. Streaming (e.g., windowing, late arriving data) 4. Integrating with new data sources 5. AI data enrichment 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 - 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 Professional-Data-Engineer Prüfungsfragen mit Lösungen (Q171-Q176):

171. Frage
You work for a car manufacturer and have set up a data pipeline using Google Cloud Pub/Sub to capture
anomalous sensor events. You are using a push subscription in Cloud Pub/Sub that calls a custom HTTPS
endpoint that you have created to take action of these anomalous events as they occur. Your custom
HTTPS endpoint keeps getting an inordinate amount of duplicate messages. What is the most likely cause
of these duplicate messages?

A. Your custom endpoint is not acknowledging messages within the acknowledgement deadline.
B. Your custom endpoint has an out-of-date SSL certificate.
C. The message body for the sensor event is too large.
D. The Cloud Pub/Sub topic has too many messages published to it.

Antwort: B

172. Frage
Which of the following are feature engineering techniques? (Select 2 answers)

A. Crossed feature columns
B. Feature prioritization
C. Bucketization of a continuous feature
D. Hidden feature layers

Antwort: A,C

Begründung:
Explanation
Selecting and crafting the right set of feature columns is key to learning an effective model.
Bucketization is a process of dividing the entire range of a continuous feature into a set of consecutive bins/buckets, and then converting the original numerical feature into a bucket ID (as a categorical feature) depending on which bucket that value falls into.
Using each base feature column separately may not be enough to explain the data. To learn the differences between different feature combinations, we can add crossed feature columns to the model.
Reference:
https://www.tensorflow.org/tutorials/wide#selecting_and_engineering_features_for_the_model

173. Frage
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 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.
B. 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.
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.

Antwort: C

174. Frage
You are on the data governance team and are implementing security requirements. You need to encrypt all your data in BigQuery by using an encryption key managed by your team. You must implement a mechanism to generate and store encryption material only on your on-premises hardware security module (HSM). You want to rely on Google managed solutions. What should you do?

A. Create the encryption key in the on-premises HSM, and import it into a Cloud Key Management Service (Cloud KMS) key. Associate the created Cloud KMS key while creating the BigQuery resources.
B. Create the encryption key in the on-premises HSM. Create BigQuery resources and encrypt data while ingesting them into BigQuery.
C. Create the encryption key in the on-premises HSM, and import it into Cloud Key Management Service (Cloud HSM) key. Associate the created Cloud HSM key while creating the BigQuery resources.
D. Create the encryption key in the on-premises HSM and link it to a Cloud External Key Manager (Cloud EKM) key. Associate the created Cloud KMS key while creating the BigQuery resources.

Antwort: D

175. Frage
When you design a Google Cloud Bigtable schema it is recommended that you _________.

A. Create schema designs that require atomicity across rows
B. Avoid schema designs that are based on NoSQL concepts
C. Avoid schema designs that require atomicity across rows
D. Create schema designs that are based on a relational database design

Antwort: C

Begründung:
Explanation
All operations are atomic at the row level. For example, if you update two rows in a table, it's possible that one row will be updated successfully and the other update will fail. Avoid schema designs that require atomicity across rows.
Reference: https://cloud.google.com/bigtable/docs/schema-design#row-keys

176. Frage
......

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