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Google GCP-DE Exam Syllabus Topics:

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

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Google Data Engineer Sample Questions (Q34-Q39):

NEW QUESTION # 34
You need to deploy additional dependencies to all of a Cloud Dataproc cluster at startup using an existing initialization action. Company security policies require that Cloud Dataproc nodes do not have access to the Internet so public initialization actions cannot fetch resources. What should you do?

A. Deploy the Cloud SQL Proxy on the Cloud Dataproc master
B. Copy all dependencies to a Cloud Storage bucket within your VPC security perimeter
C. Use an SSH tunnel to give the Cloud Dataproc cluster access to the Internet
D. Use Resource Manager to add the service account used by the Cloud Dataproc cluster to the Network User role

Answer: D

NEW QUESTION # 35
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. If that number falls below 4000, send an alert.
C. Use a Cloud Dataflow template to write your messages from Cloud Pub/Sub to BigQuer
D. 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.
E. Consume the stream of data in Cloud Dataflow using Kafka I
F. Compute the average when the window closes, and send an alert if the average is less than 4000 messages.
G. Use Cloud Scheduler to run a script every hour that counts the number of rows created in Cloud Bigtable in the last hour
H. Consume the stream of data in Cloud Dataflow using Kafka I
I. Use Kafka Connect to link your Kafka message queue to Cloud Pub/Su
J. Use Cloud Scheduler to run a script every five minutes that counts the number of rows created in BigQuery in the last hour
K. Use a Cloud Dataflow template to write your messages from Cloud Pub/Sub to Cloud Bigtabl
L. Set a sliding time window of 1 hour every 5 minute
M. If that number falls below 4000, send an alert.

Answer: F

NEW QUESTION # 36
You need to choose a database for a new project that has the following requirements:
Fully managed
Able to automatically scale up
Transactionally consistent
Able to scale up to 6 TB
Able to be queried using SQL Which database do you choose?

A. Cloud Spanner
B. Cloud SQL
C. Cloud Datastore
D. Cloud Bigtable

Answer: A

NEW QUESTION # 37
You architect a system to analyze seismic dat
a. Your extract, transform, and load (ETL) process runs as a series of MapReduce jobs on an Apache Hadoop cluster. The ETL process takes days to process a data set because some steps are computationally expensive. Then you discover that a sensor calibration step has been omitted. How should you change your ETL process to carry out sensor calibration systematically in the future?

A. Introduce a new MapReduce job to apply sensor calibration to raw data, and ensure all other MapReduce jobs are chained after this.
B. Add sensor calibration data to the output of the ETL process, and document that all users need to apply sensor calibration themselves.
C. Modify the transformMapReduce jobs to apply sensor calibration before they do anything else.
D. Develop an algorithm through simulation to predict variance of data output from the last MapReduce job based on calibration factors, and apply the correction to all data.

Answer: C

NEW QUESTION # 38
What are the minimum permissions needed for a service account used with Google Dataproc?

A. Write to Google Cloud Storage; read to Google Cloud Logging
B. Execute to Google Cloud Storage; write to Google Cloud Logging
C. Read and write to Google Cloud Storage; write to Google Cloud Logging
D. Execute to Google Cloud Storage; execute to Google Cloud Logging

Answer: C

Explanation:
Service accounts authenticate applications running on your virtual machine instances to other Google Cloud Platform services. For example, if you write an application that reads and writes files on Google Cloud Storage, it must first authenticate to the Google Cloud Storage API. At a minimum, service accounts used with Cloud Dataproc need permissions to read and write to Google Cloud Storage, and to write to Google Cloud Logging.
Reference: https://cloud.google.com/dataproc/docs/concepts/service-accounts#important_notes

NEW QUESTION # 39
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