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Google GCP-DE Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: 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. Control operational costs 2. Optimize storage and compute usage 3. Tune query and pipeline performance
Topic 2: Building and operationalizing data processing systems	28%-33%	- Operationalizing systems 1. Implement fault tolerance and recovery 2. Manage logging and alerting 3. Monitor data pipelines and workloads - Building data processing systems 1. Implement batch processing systems 2. Use BigQuery, Dataflow, Pub/Sub, Dataproc and related services 3. Implement streaming data systems
Topic 3: Ensuring solution quality	20%-25%	- Security and compliance 1. Implement encryption and compliance controls 2. Manage IAM and access control 3. Protect sensitive data - Data quality management 1. Implement data governance 2. Maintain metadata and lineage 3. Validate and clean data
Topic 4: Designing data processing systems	22%-27%	- Designing for data ingestion 1. Design batch and streaming ingestion solutions 2. Select appropriate storage systems 3. Design scalable and reliable ingestion architecture - Designing data pipelines 1. Design orchestration workflows 2. Optimize pipeline performance and cost 3. Build ETL and ELT pipelines

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Google Data Engineer Sample Questions (Q60-Q65):

NEW QUESTION # 60
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. Compute the average when the window closes, and send an alert if the average is less than 4000 messages.
B. 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.
C. Use a Cloud Dataflow template to write your messages from Cloud Pub/Sub to BigQuer
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. If that number falls below 4000, send an alert.
G. Use Kafka Connect to link your Kafka message queue to Cloud Pub/Su
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. Consume the stream of data in Cloud Dataflow using Kafka I
K. Use a Cloud Dataflow template to write your messages from Cloud Pub/Sub to Cloud Bigtabl
L. Use Cloud Scheduler to run a script every five minutes that counts the number of rows created in BigQuery in the last hour
M. Set a sliding time window of 1 hour every 5 minute

Answer: A

NEW QUESTION # 61
You are a retailer that wants to integrate your online sales capabilities with different in-home assistants, such as Google Home. You need to interpret customer voice commands and issue an order to the backend systems. Which solutions should you choose?

A. Cloud Natural Language API
B. Cloud Speech-to-Text API
C. Dialogflow Enterprise Edition
D. Cloud AutoML Natural Language

Answer: D

NEW QUESTION # 62
You work for a manufacturing company that sources up to 750 different components, each from a different supplier. You've collected a labeled dataset that has on average 1000 examples for each unique component. Your team wants to implement an app to help warehouse workers recognize incoming components based on a photo of the component. You want to implement the first working version of this app (as Proof-Of-Concept) within a few working days. What should you do?

A. Use Cloud Vision API by providing custom labels as recognition hints.
B. Train your own image recognition model leveraging transfer learning techniques.
C. Use Cloud Vision AutoML with the existing dataset.
D. Use Cloud Vision AutoML, but reduce your dataset twice.

Answer: C

NEW QUESTION # 63
Which is not a valid reason for poor Cloud Bigtable performance?

A. The Cloud Bigtable cluster has too many nodes.
B. There are issues with the network connection.
C. The table's schema is not designed correctly.
D. The workload isn't appropriate for Cloud Bigtable.

Answer: A

Explanation:
The Cloud Bigtable cluster doesn't have enough nodes. If your Cloud Bigtable cluster is overloaded, adding more nodes can improve performance. Use the monitoring tools to check whether the cluster is overloaded.
Reference: https://cloud.google.com/bigtable/docs/performance

NEW QUESTION # 64
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. Run the gsutil utility to transfer all ORC files from the Cloud Storage bucket to any node of the Dataproc cluste
B. Run the gsutil utility to transfer all ORC files from the Cloud Storage bucket to the master node of the Dataproc cluste
C. Mount the Hive tables from HDFS.
D. Load the ORC files into BigQuer
E. Leverage Cloud Storage connector for Hadoop to mount the ORC files as external Hive table
F. Mount the Hive tables locally.
G. Leverage BigQuery connector for Hadoop to mount the BigQuery tables as external Hive table
H. Mount the Hive tables locally.
I. Replicate external Hive tables to the native ones.
J. Run the gsutil utility to transfer all ORC files from the Cloud Storage bucket to HDF
K. Replicate external Hive tables to the native ones.
L. Then run the Hadoop utility to copy them do HDF

Answer: A,F

NEW QUESTION # 65
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