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Google GCP-DE Exam Syllabus Topics:

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

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Google Data Engineer Sample Questions (Q25-Q30):

NEW QUESTION # 25
You are building a new data pipeline to share data between two different types of applications: jobs generators and job runners. Your solution must scale to accommodate increases in usage and must accommodate the addition of new applications without negatively affecting the performance of existing ones. What should you do?

A. Use a Cloud Pub/Sub topic to publish jobs, and use subscriptions to execute them
B. Create a table on Cloud SQL, and insert and delete rows with the job information
C. Create an API using App Engine to receive and send messages to the applications
D. Create a table on Cloud Spanner, and insert and delete rows with the job information

Answer: A

NEW QUESTION # 26
You are creating a new pipeline in Google Cloud to stream IoT data from Cloud Pub/Sub through Cloud Dataflow to BigQuery. While previewing the data, you notice that roughly 2% of the data appears to be corrupt. You need to modify the Cloud Dataflow pipeline to filter out this corrupt data. What should you do?

A. Add a GroupByKey transform in Cloud Dataflow to group all of the valid data together and discard the rest.
B. Add a Partition transform in Cloud Dataflow to separate valid data from corrupt data.
C. Add a SideInput that returns a Boolean if the element is corrupt.
D. Add a ParDo transform in Cloud Dataflow to discard corrupt elements.

Answer: D

NEW QUESTION # 27
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. Mount the Hive tables locally.
B. Mount the Hive tables locally.
C. Then run the Hadoop utility to copy them do HDF
D. Run the gsutil utility to transfer all ORC files from the Cloud Storage bucket to HDF
E. Run the gsutil utility to transfer all ORC files from the Cloud Storage bucket to any node of the Dataproc cluste
F. Replicate external Hive tables to the native ones.
G. Replicate external Hive tables to the native ones.
H. Mount the Hive tables from HDFS.
I. Run the gsutil utility to transfer all ORC files from the Cloud Storage bucket to the master node of the Dataproc cluste
J. Leverage BigQuery connector for Hadoop to mount the BigQuery tables as external Hive table
K. Leverage Cloud Storage connector for Hadoop to mount the ORC files as external Hive table
L. Load the ORC files into BigQuer

Answer: B,E

NEW QUESTION # 28
Your company produces 20,000 files every hour. Each data file is formatted as a comma separated values (CSV) file that is less than 4 KB. All files must be ingested on Google Cloud Platform before they can be processed. Your company site has a 200 ms latency to Google Cloud, and your Internet connection bandwidth is limited as 50 Mbps. You currently deploy a secure FTP (SFTP) server on a virtual machine in Google Compute Engine as the data ingestion point. A local SFTP client runs on a dedicated machine to transmit the CSV files as is. The goal is to make reports with data from the previous day available to the executives by 10:00 a.m. each day. This design is barely able to keep up with the current volume, even though the bandwidth utilization is rather low.
You are told that due to seasonality, your company expects the number of files to double for the next three months. Which two actions should you take? (choose two.)

A. Create an S3-compatible storage endpoint in your network, and use Google Cloud Storage Transfer Service to transfer on-premices data to the designated storage bucket.
B. Introduce data compression for each file to increase the rate file of file transfer.
C. Transmit the TAR files instead, and disassemble the CSV files in the cloud upon receiving them.
D. Redesign the data ingestion process to use gsutil tool to send the CSV files to a storage bucket in parallel.
E. Contact your internet service provider (ISP) to increase your maximum bandwidth to at least 100 Mbps.
F. Assemble 1,000 files into a tape archive (TAR) fil

Answer: C,D

NEW QUESTION # 29
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. Do not use a production instance.
B. Before you test, run a heavy pre-test for several minutes.
C. Use at least 300 GB of data.
D. Run your test for at least 10 minutes.

Answer: A

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 # 30
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