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Google Professional-Data-Engineer Exam Syllabus Topics:

Section	Weight	Objectives
Operationalizing machine learning models	26%	- Model deployment and monitoring 1. Model monitoring and drift detection 2. Online vs batch prediction - ML pipeline integration 1. Feature engineering and feature stores 2. Vertex AI pipeline deployment
Building and operationalizing data processing systems	24%	- Data ingestion and integration 1. Streaming ingestion (Pub/Sub, Dataflow) 2. Batch ingestion pipelines (BigQuery, Cloud Storage) - Data processing and transformation 1. ETL/ELT pipeline design 2. Using Dataproc, Dataflow, and BigQuery SQL
Ensuring solution quality	28%	- Reliability and performance 1. Monitoring pipelines and workloads 2. Fault tolerance and recovery strategies - Security and governance 1. Data governance and compliance 2. IAM and access control in GCP
Designing data processing systems	22%	- Batch and streaming data processing design 1. Latency, throughput, and consistency trade-offs 2. Event-driven vs batch architectures - Data architecture and storage design 1. Designing scalable and cost-effective data models 2. Choosing appropriate data storage solutions (relational, NoSQL, data warehouse)

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Google Certified Professional Data Engineer Exam Professional-Data-Engineer Prüfungsfragen mit Lösungen (Q268-Q273):

268. Frage
Your company is implementing a data warehouse using BigQuery and you have been tasked with designing the data model You move your on-premises sales data warehouse with a star data schema to BigQuery but notice performance issues when querying the data of the past 30 days Based on Google's recommended practices, what should you do to speed up the query without increasing storage costs?

A. Shard the data by customer ID
B. Denormalize the data
C. Materialize the dimensional data in views
D. Partition the data by transaction date

Antwort: D

269. Frage
You are choosing a NoSQL database to handle telemetry data submitted from millions of Internet-of- Things (IoT) devices. The volume of data is growing at 100 TB per year, and each data entry has about
100 attributes. The data processing pipeline does not require atomicity, consistency, isolation, and durability (ACID). However, high availability and low latency are required. You need to analyze the data by querying against individual fields. Which three databases meet your requirements? (Choose three.)

A. Redis
B. Cassandra
C. MySQL
D. HDFS with Hive
E. HBase
F. MongoDB

Antwort: D,E,F

270. Frage
You're training a model to predict housing prices based on an available dataset with real estate properties. Your plan is to train a fully connected neural net, and you've discovered that the dataset contains latitude and longitude of the property. Real estate professionals have told you that the location of the property is highly influential on price, so you'd like to engineer a feature that incorporates this physical dependency. What should you do?

A. Provide latitude and longitude as input vectors to your neural net.
B. Create a feature cross of latitude and longitude, bucketize at the minute level and use L1 regularization during optimization.
C. Create a numeric column from a feature cross of latitude and longitude.
D. Create a feature cross of latitude and longitude, bucketize it at the minute level and use L2 regularization during optimization.

Antwort: B

Begründung:
Use L1 regularization when you need to assign greater importance to more influential features. It shrinks less important feature to 0.
L2 regularization performs better when all input features influence the output & all with the weights are of equal size.

271. Frage
Which role must be assigned to a service account used by the virtual machines in a Dataproc cluster so they can execute jobs?

A. Dataproc Editor
B. Dataproc Viewer
C. Dataproc Worker
D. Dataproc Runner

Antwort: C

Begründung:
Service accounts used with Cloud Dataproc must have Dataproc/Dataproc Worker role (or have all the permissions granted by Dataproc Worker role).
Reference:
https://cloud.google.com/dataproc/docs/concepts/service-accounts#important_notes

272. Frage
You need to compose visualization for operations teams with the following requirements:
Telemetry must include data from all 50,000 installations for the most recent 6 weeks (sampling once every minute) The report must not be more than 3 hours delayed from live data.
The actionable report should only show suboptimal links.
Most suboptimal links should be sorted to the top.
Suboptimal links can be grouped and filtered by regional geography.
User response time to load the report must be <5 seconds.
You create a data source to store the last 6 weeks of data, and create visualizations that allow viewers to see multiple date ranges, distinct geographic regions, and unique installation types. You always show the latest data without any changes to your visualizations. You want to avoid creating and updating new visualizations each month. What should you do?

A. Look through the current data and compose a series of charts and tables, one for each possible combination of criteria.
B. Export the data to a spreadsheet, compose a series of charts and tables, one for each possible combination of criteria, and spread them across multiple tabs.
C. Load the data into relational database tables, write a Google App Engine application that queries all rows, summarizes the data across each criteria, and then renders results using the Google Charts and visualization API.
D. Look through the current data and compose a small set of generalized charts and tables bound to criteria filters that allow value selection.

Antwort: D

273. Frage
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