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Data Engineer GCP-DE guide torrent materials
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Google GCP-DE Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Building and operationalizing data processing systems | 30% | - Data pipeline implementation
|
| Topic 2: Maintaining and optimizing data and ML solutions | 20% | - Security and governance
|
| Topic 3: Designing data processing systems | 20% | - Storage and data modeling
|
| Topic 4: Operationalizing data and ML pipelines | 30% | - Pipeline automation and orchestration
|
Google Data Engineer Sample Questions:
Question 1
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. Do not use a production instance.
C. Use at least 300 GB of data.
D. Before you test, run a heavy pre-test for several minutes.
Question 2
The YARN ResourceManager and the HDFS NameNode interfaces are available on a Cloud Dataproc cluster .
A. worker node
B. conditional node
C. application node
D. master node
Question 3
You need to choose a database for a new project that has the following requirements:
Fully managed
Able to automatically scale up
Transactionally consistent
Able to scale up to 6 TB
Able to be queried using SQL Which database do you choose?
A. Cloud Bigtable
B. Cloud SQL
C. Cloud Datastore
D. Cloud Spanner
Question 4
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 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.
B. Set up periodic exports of the database to write to Cloud Storage and load into BigQuery.
C. Create an application that writes to a Cloud SQL database to store the dat
D. Write a tool to poll the API and write data to Cloud Storage as gzipped JSON files.
E. 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.
F. Create an application that provides an AP
Question 5
You have a data pipeline with a Cloud Dataflow job that aggregates and writes time series metrics to Cloud Bigtable. This data feeds a dashboard used by thousands of users across the organization. You need to support additional concurrent users and reduce the amount of time required to write the dat a. Which two actions should you take? (Choose two.)
A. Increase the maximum number of Cloud Dataflow workers by setting maxNumWorkers in PipelineOptions
B. Configure your Cloud Dataflow pipeline to use local execution
C. Modify your Cloud Dataflow pipeline to use the Flatten transform before writing to Cloud Bigtable
D. Modify your Cloud Dataflow pipeline to use the CoGroupByKey transform before writing to Cloud Bigtable
E. Increase the number of nodes in the Cloud Bigtable cluster
Solutions:
| Question 1 Answer: B | Question 2 Answer: D | Question 3 Answer: D | Question 4 Answer: F | Question 5 Answer: C,D |
