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Google Professional-Data-Engineer Exam Syllabus Topics:

Section	Weight	Objectives
Building and operationalizing data processing systems	25%	- Building data pipelines 1. Ingesting data from various sources 2. Transforming and cleaning data 3. Orchestrating data workflows - Deploying and managing systems 1. Monitoring and logging data processes 2. Managing infrastructure and resources
Operationalizing machine learning models	20%	- Deploying and maintaining ML models 1. Model serving and monitoring 2. Optimizing model performance and cost - Preparing data for ML 1. Feature engineering and data preparation 2. Handling structured and unstructured data
Maintaining and automating data workloads	18%	- Automation and repeatability 1. Automating deployment and updates 2. Implementing CI/CD for data systems - Resource optimization 1. Cost management and resource allocation 2. Choosing appropriate compute and storage options
Ensuring solution quality and reliability	17%	- Troubleshooting and optimization 1. Optimizing queries and workloads 2. Diagnosing performance issues - Testing and validating data systems 1. Performance and scalability testing 2. Data quality validation
Designing data processing systems	20%	- Designing for business requirements 1. Designing for scalability and elasticity 2. Designing for reliability and fault tolerance 3. Selecting appropriate storage solutions - Designing for regulatory and security requirements 1. Ensuring data privacy and compliance 2. Implementing access control and data protection

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Google Certified Professional Data Engineer Exam Sample Questions (Q123-Q128):

NEW QUESTION # 123
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. 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.
B. Assemble 1,000 files into a tape archive (TAR) file. Transmit the TAR files instead, and disassemble the CSV files in the cloud upon receiving them.
C. Redesign the data ingestion process to use gsutil tool to send the CSV files to a storage bucket in parallel.
D. Introduce data compression for each file to increase the rate file of file transfer.
E. Contact your internet service provider (ISP) to increase your maximum bandwidth to at least 100 Mbps.

Answer: A,C

NEW QUESTION # 124
You store historic data in Cloud Storage. You need to perform analytics on the historic dat
a. You want to use a solution to detect invalid data entries and perform data transformations that will not require programming or knowledge of SQL.
What should you do?

A. Use Cloud Dataflow with Beam to detect errors and perform transformations.
B. Use federated tables in BigQuery with queries to detect errors and perform transformations.
C. Use Cloud Dataprep with recipes to detect errors and perform transformations.
D. Use Cloud Dataproc with a Hadoop job to detect errors and perform transformations.

Answer: C

NEW QUESTION # 125
Your organization has two Google Cloud projects, project A and project B. In project A, you have a Pub/Sub topic that receives data from confidential sources. Only the resources in project A should be able to access the data in that topic. You want to ensure that project B and any future project cannot access data in the project A topic. What should you do?

A. Use Identity and Access Management conditions to ensure that only users and service accounts in project A can access resources in project.
B. Add firewall rules in project A so only traffic from the VPC in project A is permitted.
C. Configure VPC Service Controls in the organization with a perimeter around project A.
D. Configure VPC Service Controls in the organization with a perimeter around the VPC of project A.

Answer: C

Explanation:
Identity and Access Management (IAM) is the recommended way to control access to Pub/Sub resources, such as topics and subscriptions. IAM allows you to grant roles and permissions to users and service accounts at the project level or the individual resource level. You can also use IAM conditions to specify additional attributes for granting or denying access, such as time, date, or origin. By using IAM conditions, you can ensure that only the resources in project A can access the data in the project A topic, regardless of the network configuration or the VPC Service Controls. You can also prevent project B and any future project from accessing the data in the project A topic by not granting them any roles or permissions on the topic.
Option A is not a good solution, as VPC Service Controls are designed to prevent data exfiltration from Google Cloud resources to the public internet, not to control access between Google Cloud projects. VPC Service Controls create a perimeter around the resources of one or more projects, and restrict the communication with resources outside the perimeter. However, VPC Service Controls do not apply to Pub
/Sub, as Pub/Sub is not associated with any specific IP address or VPC network. Therefore, configuring VPC Service Controls with a perimeter around the VPC of project A would not prevent project B or any future project from accessing the data in the project A topic, if they have the necessary IAM roles and permissions.
Option B is not a good solution, as firewall rules are used to control the ingress and egress traffic to and from the VPC network of a project. Firewall rules do not apply to Pub/Sub, as Pub/Sub is not associated with any specific IP address or VPC network. Therefore, adding firewall rules in project A to only permit traffic from the VPC in project A would not prevent project B or any future project from accessing the data in the project A topic, if they have the necessary IAM roles and permissions.
Option C is not a good solution, as VPC Service Controls are designed to prevent data exfiltration from Google Cloud resources to the public internet, not to control access between Google Cloud projects. VPC Service Controls create a perimeter around the resources of one or more projects, and restrict the communication with resources outside the perimeter. However, VPC Service Controls do not apply to Pub
/Sub, as Pub/Sub is not associated with any specific IP address or VPC network. Therefore, configuring VPC Service Controls with a perimeter around project A would not prevent project B or any future project from accessing the data in the project A topic, if they have the necessary IAM roles and permissions. References: Access control with IAM |Cloud Pub/Sub Documentation | Google Cloud, [Using IAM Conditions | Cloud IAMDocumentation | Google Cloud], [VPC Service Controls overview | Google Cloud], [Using VPC Service Controls | Google Cloud], [Pub/Sub tier capabilities | Memorystore for Redis | Google Cloud].

NEW QUESTION # 126
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 a Cloud Dataflow pipeline that transforms the JSON event payloads to Avro, writing the data to Cloud Storage and BigQuery.
B. Create an application that provides an API. Write a tool to poll the API and write data to Cloud Storage as gzipped JSON files.
C. Create an application that writes to a Cloud SQL database to store the data. Set up periodic exports of the database to write to Cloud Storage and load into BigQuery.
D. 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.

Answer: B

NEW QUESTION # 127
You have data located in BigQuery that is used to generate reports for your company. You have noticed some weekly executive report fields do not correspond to format according to company standards. For example, report errors include different telephone formats and different country code identifiers. This is a frequent issue, so you need to create a recurring job to normalize the data. You want a quick solution that requires no coding. What should you do?

A. Create a Spark job and submit it to Dataproc Serverless.
B. Use Cloud Data Fusion and Wrangler to normalize the data, and set up a recurring job.
C. Use Dataflow SQL to create a job that normalizes the data, and that after the first run of the job, schedule the pipeline to execute recurrently.
D. Use BigQuery and GoogleSQL to normalize the data, and schedule recurring queries in BigQuery.

Answer: B

NEW QUESTION # 128
......

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