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Google GCP-DE Exam Syllabus Topics:

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

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Google Data Engineer Sample Questions (Q16-Q21):

NEW QUESTION # 16
You operate an IoT pipeline built around Apache Kafka that normally receives around 5000 messages per second. You want to use Google Cloud Platform to create an alert as soon as the moving average over 1 hour drops below 4000 messages per second. What should you do?

A. Use Kafka Connect to link your Kafka message queue to Cloud Pub/Su
B. Use a Cloud Dataflow template to write your messages from Cloud Pub/Sub to Cloud Bigtabl
C. Compute the average when the window closes, and send an alert if the average is less than 4000 messages.
D. If that number falls below 4000, send an alert.
E. Use Cloud Scheduler to run a script every hour that counts the number of rows created in Cloud Bigtable in the last hour
F. Consume the stream of data in Cloud Dataflow using Kafka I
G. Set a sliding time window of 1 hour every 5 minute
H. Use a Cloud Dataflow template to write your messages from Cloud Pub/Sub to BigQuer
I. Use Cloud Scheduler to run a script every five minutes that counts the number of rows created in BigQuery in the last hour
J. Use Kafka Connect to link your Kafka message queue to Cloud Pub/Su
K. Consume the stream of data in Cloud Dataflow using Kafka I
L. Set a fixed time window of 1 hour.Compute the average when the window closes, and send an alert if the average is less than 4000 messages.
M. If that number falls below 4000, send an alert.

Answer: C

NEW QUESTION # 17
You have a data stored in BigQuery. The data in the BigQuery dataset must be highly available. You need to define a storage, backup, and recovery strategy of this data that minimizes cost. How should you configure the BigQuery table?

A. Set the BigQuery dataset to be multi-regional
B. Set the BigQuery dataset to be regiona
C. Set the BigQuery dataset to be regiona
D. Set the BigQuery dataset to be multi-regional
E. Create a scheduled query to make copies of the data to tables suffixed with the time of the backu
F. In the event of an emergency, use the backup copy of the table.
G. Create a scheduled query to make copies of the data to tables suffixed with the time of the backup
H. In the event of an emergency, use a point-in-time snapshot to recover the data.
I. In the event of an emergency, use the backup copy of the table.
J. In the event of an emergency, use a point-in-time snapshot to recover the data.

Answer: H

NEW QUESTION # 18
You are planning to use Google's Dataflow SDK to analyze customer data such as displayed below. Your project requirement is to extract only the customer name Passing Certification Exams Made Easy visit - https://www.2PassEasy.com from the data source and then write to an output PCollection.
Tom,555 X street Tim,553 Y street Sam, 111 Z street
Which operation is best suited for the above data processing requirement?

A. Sink API
B. Source API
C. Data extraction
D. ParDo

Answer: D

Explanation:
In Google Cloud dataflow SDK, you can use the ParDo to extract only a customer name of each element in your PCollection.
Reference: https://cloud.google.com/dataflow/model/par-do

NEW QUESTION # 19
An online retailer has built their current application on Google App Engine. A new initiative at the company mandates that they extend their application to allow their customers to transact directly via the application.
They need to manage their shopping transactions and analyze combined data from multiple datasets using a business intelligence (BI) tool. They want to use only a single database for this purpose. Which Google Cloud database should they choose?

A. BigQuery
B. Cloud SQL
C. Cloud BigTable
D. Cloud Datastore

Answer: C

NEW QUESTION # 20
What are all of the BigQuery operations that Google charges for?

A. Storage, queries, and loading data from a file
B. Storage, queries, and streaming inserts
C. Queries and streaming inserts
D. Storage, queries, and exporting data

Answer: B

Explanation:
Google charges for storage, queries, and streaming inserts. Loading data from a file and exporting data are free operations.
Reference: https://cloud.google.com/bigquery/pricing

NEW QUESTION # 21
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