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Google GCP-DE Exam Syllabus Topics:

Section	Weight	Objectives
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
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 lake architecture on Google Cloud Storage 2. Data warehouse design using BigQuery
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. BigQuery ML usage 2. Vertex AI integration for pipelines
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

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Google Data Engineer Sample Questions (Q24-Q29):

NEW QUESTION # 24
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. Assemble 1,000 files into a tape archive (TAR) fil
C. Introduce data compression for each file to increase the rate file of file transfer.
D. Contact your internet service provider (ISP) to increase your maximum bandwidth to at least 100 Mbps.
E. Redesign the data ingestion process to use gsutil tool to send the CSV files to a storage bucket in parallel.
F. 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.

Answer: A,E

NEW QUESTION # 25
All Google Cloud Bigtable client requests go through a front-end server they are sent to a Cloud Bigtable node.

A. once
B. before
C. after
D. only if

Answer: B

Explanation:
In a Cloud Bigtable architecture all client requests go through a front-end server before they are sent to a Cloud Bigtable node.
The nodes are organized into a Cloud Bigtable cluster, which belongs to a Cloud Bigtable instance, which is a container for the cluster. Each node in the cluster handles a subset of the requests to the cluster.
When additional nodes are added to a cluster, you can increase the number of simultaneous requests that the cluster can handle, as well as the maximum throughput for the entire cluster.
Reference: https://cloud.google.com/bigtable/docs/overview

NEW QUESTION # 26
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. Run an UPDATE statement that updates the FullName column for each user with the concatenation of the FirstName and LastName values.
B. 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.
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: A

NEW QUESTION # 27
You have a data stored in BigQuery. The data in the BigQuery dataset must be highly available. You need to define a storage, backup, and recovery strategy of this data that minimizes cost. How should you configure the BigQuery table?

A. Set the BigQuery dataset to be multi-regional
B. In the event of an emergency, use the backup copy of the table.
C. In the event of an emergency, use a point-in-time snapshot to recover the data.
D. Create a scheduled query to make copies of the data to tables suffixed with the time of the backu
E. In the event of an emergency, use a point-in-time snapshot to recover the data.
F. Set the BigQuery dataset to be multi-regional
G. Set the BigQuery dataset to be regiona
H. In the event of an emergency, use the backup copy of the table.
I. Set the BigQuery dataset to be regiona
J. Create a scheduled query to make copies of the data to tables suffixed with the time of the backup

Answer: C

NEW QUESTION # 28
You are developing an application on Google Cloud that will automatically generate subject labels for users' blog posts. You are under competitive pressure to add this feature quickly, and you have no additional developer resources. No one on your team has experience with machine learning. What should you do?

A. Call the Cloud Natural Language API from your applicatio
B. Call the model from your application and process the results as labels.
C. Deploy the model using a KubernetesEngine cluste
D. Build and train a text classification model using TensorFlo
E. Process the generated Entity Analysis as labels.
F. Process the generated Sentiment Analysis as labels.
G. Build and train a text classification model using TensorFlo
H. Call the Cloud Natural Language API from your applicatio
I. Call the model from your application and process the results as labels.
J. Deploy the model using Cloud Machine Learning Engin

Answer: E

NEW QUESTION # 29
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