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Google Professional-Data-Engineer Exam Syllabus Topics:

Section	Weight	Objectives
Ensuring solution quality and reliability	17%	- Testing and validating data systems 1. Data quality validation 2. Performance and scalability testing - Troubleshooting and optimization 1. Optimizing queries and workloads 2. Diagnosing performance issues
Operationalizing machine learning models	20%	- Deploying and maintaining ML models 1. Optimizing model performance and cost 2. Model serving and monitoring - Preparing data for ML 1. Feature engineering and data preparation 2. Handling structured and unstructured data
Building and operationalizing data processing systems	25%	- Deploying and managing systems 1. Managing infrastructure and resources 2. Monitoring and logging data processes - Building data pipelines 1. Transforming and cleaning data 2. Ingesting data from various sources 3. Orchestrating data workflows
Maintaining and automating data workloads	18%	- Resource optimization 1. Choosing appropriate compute and storage options 2. Cost management and resource allocation - Automation and repeatability 1. Automating deployment and updates 2. Implementing CI/CD for data systems
Designing data processing systems	20%	- Designing for business requirements 1. Selecting appropriate storage solutions 2. Designing for reliability and fault tolerance 3. Designing for scalability and elasticity - Designing for regulatory and security requirements 1. Implementing access control and data protection 2. Ensuring data privacy and compliance

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Google Certified Professional Data Engineer Exam Professional-Data-Engineer Prüfungsfragen mit Lösungen (Q363-Q368):

363. Frage
Your organization has been collecting and analyzing data in Google BigQuery for 6 months. The majority
of the data analyzed is placed in a time-partitioned table named events_partitioned. To reduce the
cost of queries, your organization created a view called events, which queries only the last 14 days of
data. The view is described in legacy SQL. Next month, existing applications will be connecting to
BigQuery to read the eventsdata via an ODBC connection. You need to ensure the applications can
connect. Which two actions should you take? (Choose two.)

A. Create a new view over events using standard SQL
B. Create a Google Cloud Identity and Access Management (Cloud IAM) role for the ODBC connection
and shared "events"
C. Create a new view over events_partitioned using standard SQL
D. Create a new partitioned table using a standard SQL query
E. Create a service account for the ODBC connection to use for authentication

Antwort: A,B

364. Frage
You are integrating your legacy on-premises MySQL database into a new Google Cloud data warehouse. The database contains historical customer loyalty data which needs to be extracted daily and loaded into BigQuery. You want a managed integration solution that is secure and supports incremental loads. What should you do?

A. Configure a scheduled transfer in BigQuery Data Transfer Service to ingest data from the MySQL database with a scheduled transfer configuration.
B. Leverage Database Migration Service for continuous replication of the entire database to Cloud SQL.
C. Implement a data pipeline using Pub/Sub for streaming the data changes from MySQL.
D. Use Cloud SQL federated queries to directly query the on-premises MySQL database from BigQuery.

Antwort: A

Begründung:
BigQuery Data Transfer Service supports scheduled, managed ingestion from MySQL with incremental loads, providing a secure, reliable, and low-maintenance solution for daily ETL without needing to manage a custom pipeline or full database replication.

365. Frage
What is the general recommendation when designing your row keys for a Cloud Bigtable schema?

A. Keep your row key reasonably short
B. Keep your row key as long as the field permits
C. Keep the row keep as an 8 bit integer
D. Include multiple time series values within the row key

Antwort: A

Begründung:
A general guide is to, keep your row keys reasonably short. Long row keys take up additional memory and storage and increase the time it takes to get responses from the Cloud Bigtable server.

366. Frage
Which of these statements about exporting data from BigQuery is false?

A. The only compression option available is GZIP.
B. The only supported export destination is Google Cloud Storage.
C. To export more than 1 GB of data, you need to put a wildcard in the destination filename.
D. Data can only be exported in JSON or Avro format.

Antwort: D

Begründung:
Data can be exported in CSV, JSON, or Avro format. If you are exporting nested or repeated data, then CSV format is not supported.
https://cloud.google.com/bigquery/docs/exporting-data

367. Frage
Flowlogistic is rolling out their real-time inventory tracking system. The tracking devices will all send package-tracking messages, which will now go to a single Google Cloud Pub/Sub topic instead of the Apache Kafka cluster. A subscriber application will then process the messages for real-time reporting and store them in Google BigQuery for historical analysis. You want to ensure the package data can be analyzed over time.
Which approach should you take?

A. Attach the timestamp and Package ID on the outbound message from each publisher device as they are sent to Clod Pub/Sub.
B. Attach the timestamp on each message in the Cloud Pub/Sub subscriber application as they are received.
C. Use the automatically generated timestamp from Cloud Pub/Sub to order the data.
D. Use the NOW () function in BigQuery to record the event's time.

Antwort: A

Begründung:
Explanation:

368. Frage
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