GCP-DE Buch - GCP-DE Übungsmaterialien

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Google GCP-DE Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Designing data processing systems	20%	- Data pipeline architecture design 1. Scalable data ingestion design 2. Batch vs streaming data processing selection - Storage and data modeling 1. Data lake architecture on Google Cloud Storage 2. Data warehouse design using BigQuery
Topic 2: Building and operationalizing data processing systems	30%	- Data pipeline implementation 1. Dataflow pipeline development 2. Dataproc and Spark-based processing - Data ingestion and transformation 1. ETL/ELT workflows 2. Pub/Sub streaming ingestion
Topic 3: Operationalizing data and ML pipelines	30%	- Pipeline automation and orchestration 1. Scheduling and monitoring pipelines 2. Cloud Composer workflows - Monitoring and troubleshooting 1. Logging and observability 2. Performance optimization and debugging
Topic 4: Maintaining and optimizing data and ML solutions	20%	- Security and governance 1. Data encryption and compliance 2. IAM and access control - Machine learning integration 1. BigQuery ML usage 2. Vertex AI integration for pipelines

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GCP-DE Übungsmaterialien, GCP-DE Exam Fragen

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Google Data Engineer GCP-DE Prüfungsfragen mit Lösungen (Q11-Q16):

11. Frage
You need to create a data pipeline that copies time-series transaction data so that it can be queried from within BigQuery by your data science team for analysis. Every hour, thousands of transactions are updated with a new status. The size of the intitial dataset is 1.5 PB, and it will grow by 3 TB per day. The data is heavily structured, and your data science team will build machine learning models based on this dat a. You want to maximize performance and usability for your data science team. Which two strategies should you adopt? Choose 2 answers.

A. Copy a daily snapshot of transaction data to Cloud Storage and store it as an Avro fil
B. Use BigQuery'ssupport for external data sources to query.
C. Develop a data pipeline where status updates are appended to BigQuery instead of updated.
D. Preserve the structure of the data as much as possible.
E. Use BigQuery UPDATE to further reduce the size of the dataset.
F. Denormalize the data as must as possible.

Antwort: A,C

12. Frage
You are developing an application on Google Cloud that will automatically generate subject labels for users' blog posts. You are under competitive pressure to add this feature quickly, and you have no additional developer resources. No one on your team has experience with machine learning. What should you do?

A. Deploy the model using Cloud Machine Learning Engin
B. Call the model from your application and process the results as labels.
C. Call the Cloud Natural Language API from your applicatio
D. Process the generated Entity Analysis as labels.
E. Call the model from your application and process the results as labels.
F. Process the generated Sentiment Analysis as labels.
G. Build and train a text classification model using TensorFlo
H. Call the Cloud Natural Language API from your applicatio
I. Build and train a text classification model using TensorFlo
J. Deploy the model using a KubernetesEngine cluste

Antwort: D

13. Frage
What Dataflow concept determines when a Window's contents should be output based on certain criteria being met?

A. Triggers
B. Windows
C. OutputCriteria
D. Sessions

Antwort: A

Begründung:
Triggers control when the elements for a specific key and window are output. As elements arrive, they are put into one or more windows by a Window transform and its associated WindowFn, and then passed to the associated Trigger to determine if the Windows contents should be output.
Reference:
https://cloud.google.com/dataflow/java-sdk/JavaDoc/com/google/cloud/dataflow/sdk/transforms/windowing/Tri

14. Frage
Cloud Dataproc is a managed Apache Hadoop and Apache service.

A. Spark
B. Blaze
C. Fire
D. Ignite

Antwort: A

Begründung:
Cloud Dataproc is a managed Apache Spark and Apache Hadoop service that lets you use open source data tools for batch processing, querying, streaming, and machine learning.
Reference: https://cloud.google.com/dataproc/docs/

15. Frage
All Google Cloud Bigtable client requests go through a front-end server they are sent to a Cloud Bigtable node.

A. before
B. after
C. only if
D. once

Antwort: A

Begründung:
In a Cloud Bigtable architecture all client requests go through a front-end server before they are sent to a Cloud Bigtable node.
The nodes are organized into a Cloud Bigtable cluster, which belongs to a Cloud Bigtable instance, which is a container for the cluster. Each node in the cluster handles a subset of the requests to the cluster.
When additional nodes are added to a cluster, you can increase the number of simultaneous requests that the cluster can handle, as well as the maximum throughput for the entire cluster.
Reference: https://cloud.google.com/bigtable/docs/overview

16. Frage
......

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