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Google GCP-DE Exam Syllabus Topics:

Section	Weight	Objectives
Designing data processing systems	22%-27%	- Designing data pipelines 1. Design orchestration workflows 2. Build ETL and ELT pipelines 3. Optimize pipeline performance and cost - Designing for data ingestion 1. Select appropriate storage systems 2. Design batch and streaming ingestion solutions 3. Design scalable and reliable ingestion architecture
Managing and optimizing solutions	20%-25%	- Reliability and scalability 1. Implement disaster recovery strategies 2. Scale data workloads efficiently 3. Design highly available systems - Managing resources and costs 1. Tune query and pipeline performance 2. Control operational costs 3. Optimize storage and compute usage
Ensuring solution quality	20%-25%	- Security and compliance 1. Manage IAM and access control 2. Protect sensitive data 3. Implement encryption and compliance controls - Data quality management 1. Maintain metadata and lineage 2. Validate and clean data 3. Implement data governance
Building and operationalizing data processing systems	28%-33%	- Building data processing systems 1. Use BigQuery, Dataflow, Pub/Sub, Dataproc and related services 2. Implement batch processing systems 3. Implement streaming data systems - Operationalizing systems 1. Manage logging and alerting 2. Implement fault tolerance and recovery 3. Monitor data pipelines and workloads

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Google Data Engineer Sample Questions (Q15-Q20):

NEW QUESTION # 15
You are responsible for writing your company's ETL pipelines to run on an Apache Hadoop cluster. The pipeline will require some checkpointing and splitting pipelines. Which method should you use to write the pipelines?

A. Java using MapReduce
B. PigLatin using Pig
C. Python using MapReduce
D. HiveQL using Hive

Answer: C

NEW QUESTION # 16
You want to migrate an on-premises Hadoop system to Cloud Dataproc. Hive is the primary tool in use, and the data format is Optimized Row Columnar (ORC). All ORC files have been successfully copied to a Cloud Storage bucket. You need to replicate some data to the cluster's local Hadoop Distributed File System (HDFS) to maximize performance. What are two ways to start using Hive in Cloud Dataproc? (Choose two.)

A. Replicate external Hive tables to the native ones.
B. Run the gsutil utility to transfer all ORC files from the Cloud Storage bucket to HDF
C. Mount the Hive tables locally.
D. Replicate external Hive tables to the native ones.
E. Mount the Hive tables locally.
F. Run the gsutil utility to transfer all ORC files from the Cloud Storage bucket to any node of the Dataproc cluste
G. Load the ORC files into BigQuer
H. Leverage Cloud Storage connector for Hadoop to mount the ORC files as external Hive table
I. Then run the Hadoop utility to copy them do HDF
J. Mount the Hive tables from HDFS.
K. Run the gsutil utility to transfer all ORC files from the Cloud Storage bucket to the master node of the Dataproc cluste
L. Leverage BigQuery connector for Hadoop to mount the BigQuery tables as external Hive table

Answer: E,F

NEW QUESTION # 17
You work for an economic consulting firm that helps companies identify economic trends as they happen. As part of your analysis, you use Google BigQuery to correlate customer data with the average prices of the 100 most common goods sold, including bread, gasoline, milk, and others. The average prices of these goods are updated every 30 minutes. You want to make sure this data stays up to date so you can combine it with other data in BigQuery as cheaply as possible. What should you do?

A. Store the data in a file in a regional Google Cloud Storage bucke
B. Use Cloud Dataflow to query BigQuery and combine the data programmatically with the data stored in Google Cloud Storage.
C. Load the data every 30 minutes into a new partitioned table in BigQuery.
D. Store the data in Google Cloud Datastor
E. Use Google Cloud Dataflow to query BigQuery and combine the data programmatically with the data stored in Cloud Datastore
F. Store and update the data in a regional Google Cloud Storage bucket and create a federated data source in BigQuery

Answer: C

NEW QUESTION # 18
You want to automate execution of a multi-step data pipeline running on Google Cloud. The pipeline includes Cloud Dataproc and Cloud Dataflow jobs that have multiple dependencies on each other. You want to use managed services where possible, and the pipeline will run every day. Which tool should you use?

A. Cloud Scheduler
B. Cloud Composer
C. Workflow Templates on Cloud Dataproc
D. cron

Answer: C

NEW QUESTION # 19
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. Set a sliding time window of 1 hour every 5 minute
C. If that number falls below 4000, send an alert.
D. Use Cloud Scheduler to run a script every hour that counts the number of rows created in Cloud Bigtable in the last hour
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 Kafka Connect to link your Kafka message queue to Cloud Pub/Su
H. Use a Cloud Dataflow template to write your messages from Cloud Pub/Sub to Cloud Bigtabl
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. 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.
K. Consume the stream of data in Cloud Dataflow using Kafka I
L. Use a Cloud Dataflow template to write your messages from Cloud Pub/Sub to BigQuer
M. If that number falls below 4000, send an alert.

Answer: F

NEW QUESTION # 20
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