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Google GCP-DE Exam Syllabus Topics:

Section	Weight	Objectives
Maintaining and automating data workloads	18%	- Automation and optimization 1. Automating deployment and management 2. Optimizing resource usage and costs - Operationalizing workloads 1. Orchestrating and scheduling jobs 2. Ensuring reliability and recoverability 3. Monitoring and troubleshooting pipelines
Ingesting and processing data	25%	- Transforming data 1. Optimizing transformations for performance 2. Managing schema evolution 3. Applying data processing logic - Building data pipelines 1. Handling data quality and consistency 2. Developing batch and streaming workflows 3. Implementing ingestion mechanisms
Storing and managing data	20%	- Optimizing storage performance and cost 1. Implementing cost optimization strategies 2. Configuring storage for access patterns - Implementing storage solutions 1. Designing data warehouses and data lakes 2. Using data storage services appropriately 3. Managing data lifecycle and retention
Designing data processing systems	24%	- Planning data solutions 1. Designing for reliability, scalability, and efficiency 2. Selecting appropriate storage solutions 3. Planning for data security and compliance - Designing data pipelines 1. Planning for data migration and integration 2. Defining architecture for batch and streaming processing 3. Designing for data transformation and enrichment
Preparing data for analysis and machine learning	13%	- Preparing data for ML 1. Feature engineering and data preparation 2. Supporting model training and serving - Enabling data analysis 1. Preparing datasets for querying and reporting 2. Implementing data sharing and governance 3. Optimizing query performance

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Google Data Engineer Sample Questions (Q12-Q17):

NEW QUESTION # 12
You have developed three data processing jobs. One executes a Cloud Dataflow pipeline that transforms data uploaded to Cloud Storage and writes results to BigQuery. The second ingests data from on-premises servers and uploads it to Cloud Storage. The third is a Cloud Dataflow pipeline that gets information from third-party data providers and uploads the information to Cloud Storage. You need to be able to schedule and monitor the execution of these three workflows and manually execute them when needed. What should you do?

A. Use Stackdriver Monitoring and set up an alert with a Webhook notification to trigger the jobs.
B. Create a Direct Acyclic Graph in Cloud Composer to schedule and monitor the jobs.
C. Develop an App Engine application to schedule and request the status of the jobs using GCP API calls.
D. Set up cron jobs in a Compute Engine instance to schedule and monitor the pipelines using GCP API calls.

Answer: D

NEW QUESTION # 13
Which of the following statements is NOT true regarding Bigtable access roles?

A. To give a user access to only one table in a project, grant the user the Bigtable Editor role for that table.
B. Using IAM roles, you cannot give a user access to only one table in a project, rather than all tables in a project.
C. To give a user access to only one table in a project, you must configure access through your application.
D. You can configure access control only at the project level.

Answer: A

Explanation:
For Cloud Bigtable, you can configure access control at the project level. For example, you can grant the ability to:
Read from, but not write to, any table within the project.
Read from and write to any table within the project, but not manage instances. Read from and write to any table within the project, and manage instances. Reference: https://cloud.google.com/bigtable/docs/access-control

NEW QUESTION # 14
Which Google Cloud Platform service is an alternative to Hadoop with Hive?

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

Answer: B

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 # 15
You are selecting services to write and transform JSON messages from Cloud Pub/Sub to BigQuery for a data pipeline on Google Cloud. You want to minimize service costs. You also want to monitor and accommodate input data volume that will vary in size with minimal manual intervention. What should you do?

A. Use Cloud Dataflow to run your transformation
B. Locate the bottleneck and adjust cluster resources.
C. Monitor the total execution time for a sampling of job
D. Monitor the job system lag with Stackdrive
E. Use Cloud Dataflow to run your transformation
F. Use Cloud Dataproc to run your transformation
G. Use the diagnose command to generate an operational output archiv
H. Use Cloud Dataproc to run your transformation
I. Use the default autoscaling setting for worker instances.
J. Configure the job to use non-default Compute Engine machine types when needed.
K. Monitor CPU utilization for the cluste
L. Resize the number of worker nodes in your cluster via the command line.

Answer: K

NEW QUESTION # 16
You are building a new application that you need to collect data from in a scalable way. Data arrives continuously from the application throughout the day, and you expect to generate approximately 150 GB of JSON data per day by the end of the year. Your requirements are: Decoupling producer from consumer Space and cost-efficient storage of the raw ingested data, which is to be stored indefinitely Near real-time SQL query Maintain at least 2 years of historical data, which will be queried with SQ Which pipeline should you use to meet these requirements?

A. Create an application that provides an AP
B. Create an application that publishes events to Cloud Pub/Sub, and create a Cloud Dataflow pipeline that transforms the JSON event payloads to Avro, writing the data to Cloud Storage and BigQuery.
C. Create an application that publishes events to Cloud Pub/Sub, and create Spark jobs on Cloud Dataproc to convert the JSON data to Avro format, stored on HDFS on Persistent Disk.
D. Write a tool to poll the API and write data to Cloud Storage as gzipped JSON files.
E. Set up periodic exports of the database to write to Cloud Storage and load into BigQuery.
F. Create an application that writes to a Cloud SQL database to store the dat

Answer: A

NEW QUESTION # 17
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