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

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Google Data Engineer Sample Questions (Q51-Q56):

NEW QUESTION # 51
You are implementing several batch jobs that must be executed on a schedule. These jobs have many interdependent steps that must be executed in a specific order. Portions of the jobs involve executing shell scripts, running Hadoop jobs, and running queries in BigQuery. The jobs are expected to run for many minutes up to several hours. If the steps fail, they must be retried a fixed number of times. Which service should you use to manage the execution of these jobs?

A. Cloud Scheduler
B. Cloud Dataflow
C. Cloud Functions
D. Cloud Composer

Answer: A

NEW QUESTION # 52
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. HiveQL using Hive
B. PigLatin using Pig
C. Python using MapReduce
D. Java using MapReduce

Answer: C

NEW QUESTION # 53
You work for a shipping company that uses handheld scanners to read shipping labels. Your company has strict data privacy standards that require scanners to only transmit recipients' personally identifiable information (PII) to analytics systems, which violates user privacy rules. You want to quickly build a scalable solution using cloud-native managed services to prevent exposure of PII to the analytics systems. What should you do?

A. Create an authorized view in BigQuery to restrict access to tables with sensitive data.
B. Install a third-party data validation tool on Compute Engine virtual machines to check the incoming data for sensitive information.
C. Use Stackdriver logging to analyze the data passed through the total pipeline to identify transactions that may contain sensitive information.
D. Use the tagging and confidence levels to either pass or quarantine the data in a bucket for review.
E. Build a Cloud Function that reads the topics and makes a call to the Cloud Data Loss Prevention AP

Answer: A

NEW QUESTION # 54
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 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.
C. Add a new column called FullName to the Users tabl
D. Use BigQuery to export the data for the table to a CSV fil
E. Create a view in BigQuery that concatenates the FirstName and LastName field values to produce the FullName.
F. 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.

Answer: A

NEW QUESTION # 55
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. Redesign the data ingestion process to use gsutil tool to send the CSV files to a storage bucket in parallel.
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. Assemble 1,000 files into a tape archive (TAR) fil
E. Contact your internet service provider (ISP) to increase your maximum bandwidth to at least 100 Mbps.
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,C

NEW QUESTION # 56
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