Google GCP-DE the latest exam practice questions and answers

The modern Google world is changing its dynamics at a fast pace. With the Google GCP-DE certification, you can learn these changes and stay updated all the time. There are other countless Data Engineer (GCP-DE) certification exam benefits that you can gain after passing the exam. The prominent Data Engineer (GCP-DE) certification exam benefits are validation of skills, more career opportunity, salary increment, and the opportunity to become a member of the Google community.

Google GCP-DE Exam Syllabus Topics:

Section	Weight	Objectives
Building and operationalizing data processing systems	30%	- Data ingestion and transformation 1. Pub/Sub streaming ingestion 2. ETL/ELT workflows - Data pipeline implementation 1. Dataproc and Spark-based processing 2. Dataflow pipeline development
Designing data processing systems	20%	- Data pipeline architecture design 1. Scalable data ingestion design 2. Batch vs streaming data processing selection - Storage and data modeling 1. Data warehouse design using BigQuery 2. Data lake architecture on Google Cloud Storage
Operationalizing data and ML pipelines	30%	- Monitoring and troubleshooting 1. Logging and observability 2. Performance optimization and debugging - Pipeline automation and orchestration 1. Scheduling and monitoring pipelines 2. Cloud Composer workflows
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. IAM and access control 2. Data encryption and compliance

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Google Data Engineer Sample Questions (Q35-Q40):

NEW QUESTION # 35
You are designing storage for two relational tables that are part of a 10-TB database on Google Cloud. You want to support transactions that scale horizontally. You also want to optimize data for range queries on nonkey columns. What should you do?

A. Use Cloud Dataflow to transform data to support query patterns.
B. Add secondary indexes to support query patterns.
C. Use Cloud Spanner for storag
D. Use Cloud Spanner for storag
E. Add secondary indexes to support query patterns.
F. Use Cloud SQL for storag
G. Use Cloud Dataflow to transform data to support query patterns.
H. Use Cloud SQL for storag

Answer: A

Explanation:
Reference: https://cloud.google.com/solutions/data-lifecycle-cloud-platform

NEW QUESTION # 36
You are planning to migrate your current on-premises Apache Hadoop deployment to the cloud. You need to ensure that the deployment is as fault-tolerant and cost-effective as possible for long-running batch jobs. You want to use a managed service. What should you do?

A. Change references in scripts from hdfs:// to gs://
B. Use an SSD persistent disk and 50% preemptible worker
C. Store data in Cloud Storage, and change references in scripts from hdfs:// to gs://
D. Install Hadoop and Spark on a 10-node Compute Engine instance group with preemptible instances.Store data in HDF
E. Deploy a Cloud Dataproc cluste
F. Use a standard persistent disk and 50% preemptible worker
G. Install the Cloud Storage connector, and store the data in Cloud Storag
H. Store data in Cloud Storage, and change references in scripts from hdfs:// to gs://
I. Change references in scripts from hdfs:// to gs://
J. Install Hadoop and Spark on a 10-node Compute Engine instance group with standard instance
K. Deploy a Cloud Dataproc cluste

Answer: K

NEW QUESTION # 37
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. Set a sliding time window of 1 hour every 5 minute
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. Consume the stream of data in Cloud Dataflow using Kafka I
G. Use a Cloud Dataflow template to write your messages from Cloud Pub/Sub to BigQuer
H. Use Kafka Connect to link your Kafka message queue to Cloud Pub/Su
I. Use a Cloud Dataflow template to write your messages from Cloud Pub/Sub to Cloud Bigtabl
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 Cloud Scheduler to run a script every hour that counts the number of rows created in Cloud Bigtable in the last hour
M. Use Cloud Scheduler to run a script every five minutes that counts the number of rows created in BigQuery in the last hour

Answer: E

NEW QUESTION # 38
If you're running a performance test that depends upon Cloud Bigtable, all the choices except one below are recommended steps. Which is NOT a recommended step to follow?

A. Run your test for at least 10 minutes.
B. Use at least 300 GB of data.
C. Before you test, run a heavy pre-test for several minutes.
D. Do not use a production instance.

Answer: D

Explanation:
If you're running a performance test that depends upon Cloud Bigtable, be sure to follow these steps as you plan and execute your test:
Use a production instance. A development instance will not give you an accurate sense of how a production instance performs under load.
Use at least 300 GB of data. Cloud Bigtable performs best with 1 TB or more of data. However, 300 GB of data is enough to provide reasonable results in a performance test on a 3-node cluster. On larger clusters, use 100 GB of data per node.
Before you test, run a heavy pre-test for several minutes. This step gives Cloud Bigtable a chance to balance data across your nodes based on the access patterns it observes.
Run your test for at least 10 minutes. This step lets Cloud Bigtable further optimize your data, and it helps ensure that you will test reads from disk as well as cached reads from memory.
Reference: https://cloud.google.com/bigtable/docs/performance

NEW QUESTION # 39
You are developing an application on Google Cloud that will automatically generate subject labels for users' blog posts. You are under competitive pressure to add this feature quickly, and you have no additional developer resources. No one on your team has experience with machine learning. What should you do?

A. Process the generated Sentiment Analysis as labels.
B. Deploy the model using Cloud Machine Learning Engin
C. Call the Cloud Natural Language API from your applicatio
D. Build and train a text classification model using TensorFlo
E. Call the model from your application and process the results as labels.
F. Process the generated Entity Analysis as labels.
G. Build and train a text classification model using TensorFlo
H. Call the model from your application and process the results as labels.
I. Call the Cloud Natural Language API from your applicatio
J. Deploy the model using a KubernetesEngine cluste

Answer: F

NEW QUESTION # 40
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