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Google GCP-DE Exam Syllabus Topics:

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

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Google Data Engineer Sample Questions (Q42-Q47):

NEW QUESTION # 42
Which is not a valid reason for poor Cloud Bigtable performance?

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

Answer: B

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 # 43
You are running a pipeline in Cloud Dataflow that receives messages from a Cloud Pub/Sub topic and writes the results to a BigQuery dataset in the EU. Currently, your pipeline is located in europe-west4 and has a maximum of 3 workers, instance type n1-standard-1. You notice that during peak periods, your pipeline is struggling to process records in a timely fashion, when all 3 workers are at maximum CPU utilization. Which two actions can you take to increase performance of your pipeline? (Choose two.)

A. Create a temporary table in Cloud Bigtable that will act as a buffer for new data
B. Increase the number of max workers
C. Create a new step in your pipeline to write to this table first, and then create a new pipeline to write from Cloud Bigtable to BigQuery
D. Use a larger instance type for your Cloud Dataflow workers
E. Change the zone of your Cloud Dataflow pipeline to run in us-central1
F. Create a temporary table in Cloud Spanner that will act as a buffer for new data
G. Create a new step in your pipeline to write to this table first, and then create a new pipeline to write from Cloud Spanner to BigQuery

Answer: C,D

NEW QUESTION # 44
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. Consume the stream of data in Cloud Dataflow using Kafka I
B. Use Cloud Scheduler to run a script every hour that counts the number of rows created in Cloud Bigtable in the last hour
C. Use Kafka Connect to link your Kafka message queue to Cloud Pub/Su
D. If that number falls below 4000, send an alert.
E. Compute the average when the window closes, and send an alert if the average is less than 4000 messages.
F. Use a Cloud Dataflow template to write your messages from Cloud Pub/Sub to Cloud Bigtabl
G. If that number falls below 4000, send an alert.
H. Use a Cloud Dataflow template to write your messages from Cloud Pub/Sub to BigQuer
I. 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.
J. Set a sliding time window of 1 hour every 5 minute
K. Use Cloud Scheduler to run a script every five minutes that counts the number of rows created in BigQuery in the last hour
L. Use Kafka Connect to link your Kafka message queue to Cloud Pub/Su
M. Consume the stream of data in Cloud Dataflow using Kafka I

Answer: E

NEW QUESTION # 45
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 AutoML, but reduce your dataset twice.
B. Use Cloud Vision AutoML with the existing dataset.
C. Use Cloud Vision API by providing custom labels as recognition hints.
D. Train your own image recognition model leveraging transfer learning techniques.

Answer: B

NEW QUESTION # 46
For the best possible performance, what is the recommended zone for your Compute Engine instance and Cloud Bigtable instance?

A. Have both the Compute Engine instance and the Cloud Bigtable instance to be in different zones.
B. Have the Compute Engine instance in the furthest zone from the Cloud Bigtable instance.
C. Have the Cloud Bigtable instance to be in the same zone as all of the consumers of your data.
D. Have both the Compute Engine instance and the Cloud Bigtable instance to be in the same zone.

Answer: D

Explanation:
It is recommended to create your Compute Engine instance in the same zone as your Cloud Bigtable instance for the best possible performance, If it's not possible to create a instance in the same zone, you should create your instance in another zone within the same region. For example, if your Cloud Bigtable instance is located in us-central1-b, you could create your instance in us-central1-f. This change may result in several milliseconds of additional latency for each Cloud Bigtable request.
It is recommended to avoid creating your Compute Engine instance in a different region from your Cloud Bigtable instance, which can add hundreds of milliseconds of latency to each Cloud Bigtable request.
Reference: https://cloud.google.com/bigtable/docs/creating-compute-instance

NEW QUESTION # 47
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