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Google GCP-DE Exam Syllabus Topics:

Section	Weight	Objectives
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
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
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
Designing data processing systems	20%	- Storage and data modeling 1. Data lake architecture on Google Cloud Storage 2. Data warehouse design using BigQuery - Data pipeline architecture design 1. Scalable data ingestion design 2. Batch vs streaming data processing selection

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These GCP-DE exam question formats contain real, valid, and updated Google GCP-DE exam questions that will assist you in Google Data Engineer exam preparation and enable you to pass the challenging Google GCP-DE Exam with good scores. The Google GCP-DE questions are prepared by highly experienced professionals and, thus, are kept to the point and concise.

Google Data Engineer Sample Questions (Q41-Q46):

NEW QUESTION # 41
Which Google Cloud Platform service is an alternative to Hadoop with Hive?

A. Cloud Bigtable
B. Cloud Datastore
C. Cloud Dataflow
D. BigQuery

Answer: D

Explanation:
Apache Hive is a data warehouse software project built on top of Apache Hadoop for providing data summarization, query, and analysis. Google BigQuery is an enterprise data warehouse. Reference: https://en.wikipedia.org/wiki/Apache_Hive

NEW QUESTION # 42
Which of the following job types are supported by Cloud Dataproc (select 3 answers)?

A. Pig
B. Hive
C. Spark
D. YARN

Answer: A,B,C

Explanation:
Cloud Dataproc provides out-of-the box and end-to-end support for many of the most popular job types, including Spark, Spark SQL, PySpark, MapReduce, Hive, and Pig jobs.
Reference: https://cloud.google.com/dataproc/docs/resources/faq#what_type_of_jobs_can_i_run

NEW QUESTION # 43
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 Cloud Scheduler to run a script every five minutes that counts the number of rows created in BigQuery in the last hour
B. Set a sliding time window of 1 hour every 5 minute
C. If that number falls below 4000, send an alert.
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. Use Kafka Connect to link your Kafka message queue to Cloud Pub/Su
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. Use Cloud Scheduler to run a script every hour that counts the number of rows created in Cloud Bigtable in the last hour
I. Use a Cloud Dataflow template to write your messages from Cloud Pub/Sub to BigQuer
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. Compute the average when the window closes, and send an alert if the average is less than 4000 messages.
M. Consume the stream of data in Cloud Dataflow using Kafka I

Answer: L

NEW QUESTION # 44
You are integrating one of your internal IT applications and Google BigQuery, so users can query BigQuery from the application's interface. You do not want individual users to authenticate to BigQuery and you do not want to give them access to the dataset. You need to securely access BigQuery from your IT application.
What should you do?

A. Integrate with a single sign-on (SSO) platform, and pass each user's credentials along with the query request
B. Create groups for your users and give those groups access to the dataset
C. Create a service account and grant dataset access to that accoun
D. Use the service account's private key to access the dataset
E. Store the username and password for that user in a file on the files system, and use those credentials to access the BigQuery dataset
F. Create a dummy user and grant dataset access to that use

Answer: C

NEW QUESTION # 45
As your organization expands its usage of GCP, many teams have started to create their own projects. Projects are further multiplied to accommodate different stages of deployments and target audiences. Each project requires unique access control configurations. The central IT team needs to have access to all projects. Furthermore, data from Cloud Storage buckets and BigQuery datasets must be shared for use in other projects in an ad hoc way. You want to simplify access control management by minimizing the number of policies. Which two steps should you take? Choose 2 answers.

A. For each Cloud Storage bucket or BigQuery dataset, decide which projects need acces
B. Only use service accounts when sharing data for Cloud Storage buckets and BigQuery datasets.
C. Create distinct groups for various teams, and specify groups in Cloud IAM policies.
D. Use Cloud Deployment Manager to automate access provision.
E. Find all the active members who have access to these projects, and create a Cloud IAM policy to grant access to all these users.
F. Introduce resource hierarchy to leverage access control policy inheritance.

Answer: C,F

NEW QUESTION # 46
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