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Google GCP-DE Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Ensuring solution quality | 20%-25% | - Data quality management
|
| Managing and optimizing solutions | 20%-25% | - Managing resources and costs
|
| Building and operationalizing data processing systems | 28%-33% | - Building data processing systems
|
| Designing data processing systems | 22%-27% | - Designing for data ingestion
|
Google Data Engineer Sample Questions:
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. Write a tool to poll the API and write data to Cloud Storage as gzipped JSON files.
- C. Set up periodic exports of the database to write to Cloud Storage and load into BigQuery.
- D. Create an application that provides an AP
- 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 writes to a Cloud SQL database to store the dat
Correct Answer: D 🗳️
You are responsible for writing your company's ETL pipelines to run on an Apache Hadoop cluster. The pipeline will require some checkpointing and splitting pipelines. Which method should you use to write the pipelines?
- A. PigLatin using Pig
- B. Java using MapReduce
- C. Python using MapReduce
- D. HiveQL using Hive
Correct Answer: C 🗳️
Flowlogistic's CEO wants to gain rapid insight into their customer base so his sales team can be better informed in the field. This team is not very technical, so they've purchased a visualization tool to simplify the creation of BigQuery reports. However, they've been overwhelmed by all the data in the table, and are spending a lot of money on queries trying to find the data they need. You want to solve their problem in the most cost-effective way. What should you do?
- A. Create identity and access management (IAM) roles on the appropriate columns, so only they appear in a query.
- B. Create a view on the table to present to the virtualization tool.
- C. Create an additional table with only the necessary columns.
- D. Export the data into a Google Sheet for virtualization.
Correct Answer: B 🗳️
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. Use a larger instance type for your Cloud Dataflow workers
- B. Change the zone of your Cloud Dataflow pipeline to run in us-central1
- 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. Increase the number of max workers
- E. Create a temporary table in Cloud Bigtable that will act as a buffer for new data
- F. 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
- G. Create a temporary table in Cloud Spanner that will act as a buffer for new data
Correct Answer: A,C 🗳️
You want to migrate an on-premises Hadoop system to Cloud Dataproc. Hive is the primary tool in use, and the data format is Optimized Row Columnar (ORC). All ORC files have been successfully copied to a Cloud Storage bucket. You need to replicate some data to the cluster's local Hadoop Distributed File System (HDFS) to maximize performance. What are two ways to start using Hive in Cloud Dataproc? (Choose two.)
- A. Run the gsutil utility to transfer all ORC files from the Cloud Storage bucket to any node of the Dataproc cluste
- B. Replicate external Hive tables to the native ones.
- C. Leverage BigQuery connector for Hadoop to mount the BigQuery tables as external Hive table
- D. Mount the Hive tables from HDFS.
- E. Run the gsutil utility to transfer all ORC files from the Cloud Storage bucket to the master node of the Dataproc cluste
- F. Run the gsutil utility to transfer all ORC files from the Cloud Storage bucket to HDF
- G. Leverage Cloud Storage connector for Hadoop to mount the ORC files as external Hive table
- H. Load the ORC files into BigQuer
- I. Mount the Hive tables locally.
- J. Then run the Hadoop utility to copy them do HDF
- K. Mount the Hive tables locally.
- L. Replicate external Hive tables to the native ones.
Correct Answer: A,I 🗳️
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