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Google GCP-DE Exam Syllabus Topics:

Section	Weight	Objectives
Building and operationalizing data processing systems	28%-33%	- Building data processing systems 1. Implement batch processing systems 2. Implement streaming data systems 3. Use BigQuery, Dataflow, Pub/Sub, Dataproc and related services - Operationalizing systems 1. Monitor data pipelines and workloads 2. Manage logging and alerting 3. Implement fault tolerance and recovery
Managing and optimizing solutions	20%-25%	- Reliability and scalability 1. Scale data workloads efficiently 2. Design highly available systems 3. Implement disaster recovery strategies - Managing resources and costs 1. Control operational costs 2. Optimize storage and compute usage 3. Tune query and pipeline performance
Designing data processing systems	22%-27%	- Designing for data ingestion 1. Design batch and streaming ingestion solutions 2. Design scalable and reliable ingestion architecture 3. Select appropriate storage systems - Designing data pipelines 1. Optimize pipeline performance and cost 2. Design orchestration workflows 3. Build ETL and ELT pipelines
Ensuring solution quality	20%-25%	- Data quality management 1. Maintain metadata and lineage 2. Implement data governance 3. Validate and clean data - Security and compliance 1. Manage IAM and access control 2. Protect sensitive data 3. Implement encryption and compliance controls

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Google Data Engineer Sample Questions (Q71-Q76):

NEW QUESTION # 71
The YARN ResourceManager and the HDFS NameNode interfaces are available on a Cloud Dataproc cluster .

A. application node
B. master node
C. worker node
D. conditional node

Answer: B

Explanation:
The YARN ResourceManager and the HDFS NameNode interfaces are available on a Cloud Dataproc cluster master node. The cluster master-host-name is the name of your Cloud Dataproc cluster followed by an -m suffix-for example, if your cluster is named "my-cluster", the master-host-name would be "my-cluster-m". Reference: https://cloud.google.com/dataproc/docs/concepts/cluster-web-interfaces#interfaces

NEW QUESTION # 72
What is the general recommendation when designing your row keys for a Cloud Bigtable schema?

A. Keep your row key reasonably short
B. Include multiple time series values within the row key
C. Keep the row keep as an 8 bit integer
D. Keep your row key as long as the field permits

Answer: A

Explanation:
A general guide is to, keep your row keys reasonably short. Long row keys take up additional memory and storage and increase the time it takes to get responses from the Cloud Bigtable server.
Reference: https://cloud.google.com/bigtable/docs/schema-design#row-keys

NEW QUESTION # 73
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. Transmit the TAR files instead, and disassemble the CSV files in the cloud upon receiving them.
C. Introduce data compression for each file to increase the rate file of file transfer.
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,B

NEW QUESTION # 74
What are all of the BigQuery operations that Google charges for?

A. Storage, queries, and loading data from a file
B. Queries and streaming inserts
C. Storage, queries, and streaming inserts
D. Storage, queries, and exporting data

Answer: C

Explanation:
Google charges for storage, queries, and streaming inserts. Loading data from a file and exporting data are free operations.
Reference: https://cloud.google.com/bigquery/pricing

NEW QUESTION # 75
You are building a new application that you need to collect data from in a scalable way. Data arrives continuously from the application throughout the day, and you expect to generate approximately 150 GB of JSON data per day by the end of the year. Your requirements are: Decoupling producer from consumer Space and cost-efficient storage of the raw ingested data, which is to be stored indefinitely Near real-time SQL query Maintain at least 2 years of historical data, which will be queried with SQ Which pipeline should you use to meet these requirements?

A. Create an application that publishes events to Cloud Pub/Sub, and create Spark jobs on Cloud Dataproc to convert the JSON data to Avro format, stored on HDFS on Persistent Disk.
B. Create an application that writes to a Cloud SQL database to store the dat
C. Write a tool to poll the API and write data to Cloud Storage as gzipped JSON files.
D. Create an application that publishes events to Cloud Pub/Sub, and create a Cloud Dataflow pipeline that transforms the JSON event payloads to Avro, writing the data to Cloud Storage and BigQuery.
E. Create an application that provides an AP
F. Set up periodic exports of the database to write to Cloud Storage and load into BigQuery.

Answer: E

NEW QUESTION # 76
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