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This is a desktop-based GCP-DE practice exam software that doesn't require an internet connection except for license validation during purchase. The software provides Data Engineer (GCP-DE) practice exams that are customizable, helping students prepare for the actual GCP-DE Exam. The team updates the Google GCP-DE tests regularly and is available 24/7 to address any issues. Assessment records are saved for easy tracking. Windows computers support the desktop Google GCP-DE practice exam software.

Google GCP-DE Exam Syllabus Topics:

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

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Google Data Engineer Sample Questions (Q65-Q70):

NEW QUESTION # 65
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. Transmit the TAR files instead, and disassemble the CSV files in the cloud upon receiving them.
B. Introduce data compression for each file to increase the rate file of file transfer.
C. 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.
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: A,D

NEW QUESTION # 66
Flowlogistic's management has determined that the current Apache Kafka servers cannot handle the data volume for their real-time inventory tracking system. You need to build a new system on Google Cloud Platform (GCP) that will feed the proprietary tracking software. The system must be able to ingest data from a variety of global sources, process and query in real-time, and store the data reliably. Which combination of GCP products should you choose?

A. Cloud Pub/Sub, Cloud SQL, and Cloud Storage
B. Cloud Pub/Sub, Cloud Dataflow, and Cloud Storage
C. Cloud Load Balancing, Cloud Dataflow, and Cloud Storage
D. Cloud Pub/Sub, Cloud Dataflow, and Local SSD

Answer: A

NEW QUESTION # 67
You work for a large fast food restaurant chain with over 400,000 employees. You store employee information in Google BigQuery in a Users table consisting of a FirstName field and a LastName field. A member of IT is building an application and asks you to modify the schema and data in BigQuery so the application can query a FullName field consisting of the value of the FirstName field concatenated with a space, followed by the value of the LastName field for each employee. How can you make that data available while minimizing cost?

A. Create a Google Cloud Dataflow job that queries BigQuery for the entire Users table, concatenates the FirstName value and LastName value for each user, and loads the proper values for FirstName, LastName, and FullName into a new table in BigQuery.
B. Run an UPDATE statement that updates the FullName column for each user with the concatenation of the FirstName and LastName values.
C. Create a Google Cloud Dataproc job to process the CSV file and output a new CSV file containing the proper values for FirstName, LastName and FullNam G. Run a BigQuery load job to load the new CSV file into BigQuery.
D. Create a view in BigQuery that concatenates the FirstName and LastName field values to produce the FullName.
E. Use BigQuery to export the data for the table to a CSV fil
F. Add a new column called FullName to the Users tabl

Answer: B

NEW QUESTION # 68
When a Cloud Bigtable node fails, is lost.

A. the last transaction
B. the time dimension
C. no data
D. all data

Answer: C

Explanation:
A Cloud Bigtable table is sharded into blocks of contiguous rows, called tablets, to help balance the workload of queries. Tablets are stored on Colossus, Google's file system, in SSTable format. Each tablet is associated with a specific Cloud Bigtable node.
Data is never stored in Cloud Bigtable nodes themselves; each node has pointers to a set of tablets that are stored on Colossus. As a result:
Rebalancing tablets from one node to another is very fast, because the actual data is not copied. Cloud Bigtable simply updates the pointers for each node.
Recovery from the failure of a Cloud Bigtable node is very fast, because only metadata needs to be migrated to the replacement node.
When a Cloud Bigtable node fails, no data is lost Reference: https://cloud.google.com/bigtable/docs/overview

NEW QUESTION # 69
By default, which of the following windowing behavior does Dataflow apply to unbounded data sets?

A. Windows at every 1 minute
B. Windows at every 10 minutes
C. Windows at every 100 MB of data
D. Single, Global Window

Answer: D

Explanation:
Dataflow's default windowing behavior is to assign all elements of a PCollection to a single, global window, even for unbounded PCollections Reference: https://cloud.google.com/dataflow/model/pcollection

NEW QUESTION # 70
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