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Google GCP-DE Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Maintaining and optimizing data and ML solutions	20%	- Machine learning integration 1. BigQuery ML usage 2. Vertex AI integration for pipelines - Security and governance 1. Data encryption and compliance 2. IAM and access control
Topic 2: Building and operationalizing data processing systems	30%	- Data pipeline implementation 1. Dataflow pipeline development 2. Dataproc and Spark-based processing - Data ingestion and transformation 1. ETL/ELT workflows 2. Pub/Sub streaming ingestion
Topic 3: Operationalizing data and ML pipelines	30%	- Monitoring and troubleshooting 1. Performance optimization and debugging 2. Logging and observability - Pipeline automation and orchestration 1. Cloud Composer workflows 2. Scheduling and monitoring pipelines
Topic 4: 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. Batch vs streaming data processing selection 2. Scalable data ingestion design

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Google Data Engineer Sample Questions (Q68-Q73):

NEW QUESTION # 68
Which of the following is NOT true about Dataflow pipelines?

A. Dataflow pipelines can be programmed in Java
B. Dataflow pipelines use a unified programming model, so can work both with streaming and batch data sources
C. Dataflow pipelines are tied to Dataflow, and cannot be run on any other runner
D. Dataflow pipelines can consume data from other Google Cloud services

Answer: C

Explanation:
Dataflow pipelines can also run on alternate runtimes like Spark and Flink, as they are built using the Apache Beam SDKs Reference: https://cloud.google.com/dataflow/

NEW QUESTION # 69
You want to automate execution of a multi-step data pipeline running on Google Cloud. The pipeline includes Cloud Dataproc and Cloud Dataflow jobs that have multiple dependencies on each other. You want to use managed services where possible, and the pipeline will run every day. Which tool should you use?

A. Workflow Templates on Cloud Dataproc
B. cron
C. Cloud Composer
D. Cloud Scheduler

Answer: A

NEW QUESTION # 70
You launched a new gaming app almost three years ago. You have been uploading log files from the previous day to a separate Google BigQuery table with the table name format LOGS_yyyymmdd. You have been using table wildcard functions to generate daily and monthly reports for all time ranges. Recently, you discovered that some queries that cover long date ranges are exceeding the limit of 1,000 tables and failing. How can you resolve this issue?

A. Create separate views to cover each month, and query from these views
B. Convert the sharded tables into a single partitioned table
C. Convert all daily log tables into date-partitioned tables
D. Enable query caching so you can cache data from previous months

Answer: C

NEW QUESTION # 71
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. Introduce data compression for each file to increase the rate file of file transfer.
B. Contact your internet service provider (ISP) to increase your maximum bandwidth to at least 100 Mbps.
C. Redesign the data ingestion process to use gsutil tool to send the CSV files to a storage bucket in parallel.
D. 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.
E. Transmit the TAR files instead, and disassemble the CSV files in the cloud upon receiving them.
F. Assemble 1,000 files into a tape archive (TAR) fil

Answer: C,E

NEW QUESTION # 72
You need to deploy additional dependencies to all of a Cloud Dataproc cluster at startup using an existing initialization action. Company security policies require that Cloud Dataproc nodes do not have access to the Internet so public initialization actions cannot fetch resources. What should you do?

A. Use an SSH tunnel to give the Cloud Dataproc cluster access to the Internet
B. Use Resource Manager to add the service account used by the Cloud Dataproc cluster to the Network User role
C. Deploy the Cloud SQL Proxy on the Cloud Dataproc master
D. Copy all dependencies to a Cloud Storage bucket within your VPC security perimeter

Answer: B

NEW QUESTION # 73
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