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Google GCP-DE Exam Syllabus Topics:

Section	Weight	Objectives
Ingesting and processing data	25%	- Transforming data 1. Applying data processing logic 2. Optimizing transformations for performance 3. Managing schema evolution - Building data pipelines 1. Handling data quality and consistency 2. Developing batch and streaming workflows 3. Implementing ingestion mechanisms
Designing data processing systems	24%	- Designing data pipelines 1. Planning for data migration and integration 2. Designing for data transformation and enrichment 3. Defining architecture for batch and streaming processing - Planning data solutions 1. Selecting appropriate storage solutions 2. Designing for reliability, scalability, and efficiency 3. Planning for data security and compliance
Storing and managing data	20%	- Implementing storage solutions 1. Managing data lifecycle and retention 2. Designing data warehouses and data lakes 3. Using data storage services appropriately - Optimizing storage performance and cost 1. Implementing cost optimization strategies 2. Configuring storage for access patterns
Maintaining and automating data workloads	18%	- Operationalizing workloads 1. Orchestrating and scheduling jobs 2. Ensuring reliability and recoverability 3. Monitoring and troubleshooting pipelines - Automation and optimization 1. Automating deployment and management 2. Optimizing resource usage and costs
Preparing data for analysis and machine learning	13%	- Preparing data for ML 1. Feature engineering and data preparation 2. Supporting model training and serving - Enabling data analysis 1. Preparing datasets for querying and reporting 2. Optimizing query performance 3. Implementing data sharing and governance

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Google Data Engineer Sample Questions (Q53-Q58):

NEW QUESTION # 53
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. Process the generated Entity Analysis as labels.
B. Deploy the model using Cloud Machine Learning Engin
C. Call the model from your application and process the results as labels.
D. Deploy the model using a KubernetesEngine cluste
E. Call the Cloud Natural Language API from your applicatio
F. Process the generated Sentiment Analysis as labels.
G. Call the Cloud Natural Language API from your applicatio
H. Call the model from your application and process the results as labels.
I. Build and train a text classification model using TensorFlo
J. Build and train a text classification model using TensorFlo

Answer: A

NEW QUESTION # 54
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. 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. Introduce data compression for each file to increase the rate file of file transfer.
E. Redesign the data ingestion process to use gsutil tool to send the CSV files to a storage bucket in parallel.
F. Contact your internet service provider (ISP) to increase your maximum bandwidth to at least 100 Mbps.

Answer: A,E

NEW QUESTION # 55
Which of these is not a supported method of putting data into a partitioned table?

A. Create a partitioned table and stream new records to it every day.
B. Use ORDER BY to put a table's rows into chronological order and then change the table's type to "Partitioned".
C. Run a query to get the records for a specific day from an existing table and for the destination table, specify a partitioned table ending with the day in the format "$YYYYMMDD".
D. If you have existing data in a separate file for each day, then create a partitioned table and upload each file into the appropriate partition.

Answer: B

Explanation:
You cannot change an existing table into a partitioned table. You must create a partitioned table from scratch. Then you can either stream data into it every day and the data will automatically be put in the right partition, or you can load data into a specific partition by using "$YYYYMMDD" at the end of the table name. Reference: https://cloud.google.com/bigquery/docs/partitioned-tables

NEW QUESTION # 56
You are integrating one of your internal IT applications and Google BigQuery, so users can query BigQuery from the application's interface. You do not want individual users to authenticate to BigQuery and you do not want to give them access to the dataset. You need to securely access BigQuery from your IT application.
What should you do?

A. Store the username and password for that user in a file on the files system, and use those credentials to access the BigQuery dataset
B. Integrate with a single sign-on (SSO) platform, and pass each user's credentials along with the query request
C. Create groups for your users and give those groups access to the dataset
D. Create a service account and grant dataset access to that accoun
E. Create a dummy user and grant dataset access to that use
F. Use the service account's private key to access the dataset

Answer: D

NEW QUESTION # 57
You want to process payment transactions in a point-of-sale application that will run on Google Cloud Platform. Your user base could grow exponentially, but you do not want to manage infrastructure scaling.
Which Google database service should you use?

A. BigQuery
B. Cloud SQL
C. Cloud Bigtable
D. Cloud Datastore

Answer: B

NEW QUESTION # 58
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