VALID PROFESSIONAL-DATA-ENGINEER TEST MATERIALS - PROFESSIONAL-DATA-ENGINEER VALID EXAM TUTORIAL

Valid Professional-Data-Engineer Test Materials - Professional-Data-Engineer Valid Exam Tutorial

Valid Professional-Data-Engineer Test Materials - Professional-Data-Engineer Valid Exam Tutorial

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Ensuring Solution Quality

The last section of the certification exam evaluates the ability of the learners to design for security & compliance, including identity & access management, legal compliance, data security, and privacy ensuring. Moreover, they should be able to ensure flexibility & portability, reliability & fidelity, as well as scalability & efficiency.

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Professional-Data-Engineer Valid Exam Tutorial & Professional-Data-Engineer Valid Study Plan

Google Certification evolves swiftly, and a practice test may become obsolete within weeks of its publication. We provide free updates for Google Professional-Data-Engineer Exam Questions for three months after the purchase to ensure you are studying the most recent Google solutions. Furthermore, Prep4away is a very responsible and trustworthy platform dedicated to certifying you as a specialist.

To prepare for the exam, candidates can take advantage of various resources provided by Google, such as online training courses, practice exams, and study guides. In addition, candidates can gain hands-on experience with Google Cloud Platform by working on real-world projects and labs. With the increasing demand for data engineers and the growing popularity of cloud-based solutions, the Google Professional-Data-Engineer Certification can provide a significant boost to an individual's career prospects in the field of data engineering.

Google Certified Professional Data Engineer Exam Sample Questions (Q71-Q76):

NEW QUESTION # 71
You work for an economic consulting firm that helps companies identify economic trends as they happen. As part of your analysis, you use Google BigQuery to correlate customer data with the average prices of the 100 most common goods sold, including bread, gasoline, milk, and others. The average prices of these goods are updated every 30 minutes. You want to make sure this data stays up to date so you can combine it with other data in BigQuery as cheaply as possible. What should you do?

  • A. Store the data in a file in a regional Google Cloud Storage bucket. Use Cloud Dataflow to query BigQuery and combine the data programmatically with the data stored in Google Cloud Storage.
  • B. Store the data in Google Cloud Datastore. Use Google Cloud Dataflow to query BigQuery and combine the data programmatically with the data stored in Cloud Datastore
  • C. Load the data every 30 minutes into a new partitioned table in BigQuery.
  • D. Store and update the data in a regional Google Cloud Storage bucket and create a federated data source in BigQuery

Answer: B


NEW QUESTION # 72
You have a job that you want to cancel. It is a streaming pipeline, and you want to ensure that any data that is in-flight is processed and written to the output. Which of the following commands can you use on the Dataflow monitoring console to stop the pipeline job?

  • A. Stop
  • B. Finish
  • C. Drain
  • D. Cancel

Answer: C

Explanation:
Using the Drain option to stop your job tells the Dataflow service to finish your job in its current state. Your job will immediately stop ingesting new data from input sources, but the Dataflow
service will preserve any existing resources (such as worker instances) to finish processing and writing any buffered data in your pipeline.


NEW QUESTION # 73
You have 100 GB of data stored in a BigQuery table. This data is outdated and will only be accessed one or two times a year for analytics with SQL. For backup purposes, you want to store this data to be immutable for
3 years. You want to minimize storage costs. What should you do?

  • A. 1 Create a BigQuery table clone.
    2. Query the clone when you need to perform analytics.
  • B. 1. Perform a BigQuery export to a Cloud Storage bucket with archive storage class.
    2 Enable versionmg on the bucket.
    3. Create a BigQuery external table on the exported files.
  • C. 1 Create a BigQuery table snapshot.
    2 Restore the snapshot when you need to perform analytics.
  • D. 1 Perform a BigQuery export to a Cloud Storage bucket with archive storage class.
    2 Set a locked retention policy on the bucket.
    3. Create a BigQuery external table on the exported files.

Answer: D

Explanation:
This option will allow you to store the data in a low-cost storage option, as the archive storage class has the lowest price per GB among the Cloud Storage classes. It will also ensure that the data is immutable for 3 years, as the locked retention policy prevents the deletion or overwriting of the data until the retention period expires. You can still query the data using SQL by creating a BigQuery external table that references the exported files in the Cloud Storage bucket. Option A is incorrect because creating a BigQuery table clone will not reduce the storage costs, as the clone will have the same size and storage class as the original table. Option B is incorrect because creating a BigQuery table snapshot will also not reduce the storage costs, as the snapshot will have the same size and storage class as the original table. Option C is incorrect because enabling versioning on the bucket will not make the data immutable, as the versions can still be deleted or overwritten by anyone with the appropriate permissions. It will also increase the storage costs, as each version of the file will be charged separately. References:
* Exporting table data | BigQuery | Google Cloud
* Storage classes | Cloud Storage | Google Cloud
* Retention policies and retention periods | Cloud Storage | Google Cloud
* Federated queries | BigQuery | Google Cloud


NEW QUESTION # 74
You used Cloud Dataprep to create a recipe on a sample of data in a BigQuery table. You want to reuse this recipe on a daily upload of data with the same schema, after the load job with variable execution time completes. What should you do?

  • A. Export the Cloud Dataprep job as a Cloud Dataflow template, and incorporate it into a Cloud Composer job.
  • B. Export the recipe as a Cloud Dataprep template, and create a job in Cloud Scheduler.
  • C. Create an App Engine cron job to schedule the execution of the Cloud Dataprep job.
  • D. Create a cron schedule in Cloud Dataprep.

Answer: A

Explanation:
Topic 1, Flowlogistic Case Study
Company Overview
Flowlogistic is a leading logistics and supply chain provider. They help businesses throughout the world manage their resources and transport them to their final destination. The company has grown rapidly, expanding their offerings to include rail, truck, aircraft, and oceanic shipping.
Company Background
The company started as a regional trucking company, and then expanded into other logistics market. Because they have not updated their infrastructure, managing and tracking orders and shipments has become a bottleneck. To improve operations, Flowlogistic developed proprietary technology for tracking shipments in real time at the parcel level. However, they are unable to deploy it because their technology stack, based on Apache Kafka, cannot support the processing volume. In addition, Flowlogistic wants to further analyze their orders and shipments to determine how best to deploy their resources.
Solution Concept
Flowlogistic wants to implement two concepts using the cloud:
Use their proprietary technology in a real-time inventory-tracking system that indicates the location of their loads Perform analytics on all their orders and shipment logs, which contain both structured and unstructured data, to determine how best to deploy resources, which markets to expand info. They also want to use predictive analytics to learn earlier when a shipment will be delayed.
Existing Technical Environment
Flowlogistic architecture resides in a single data center:
Databases
8 physical servers in 2 clusters
SQL Server - user data, inventory, static data
3 physical servers
Cassandra - metadata, tracking messages
10 Kafka servers - tracking message aggregation and batch insert
Application servers - customer front end, middleware for order/customs
60 virtual machines across 20 physical servers
Tomcat - Java services
Nginx - static content
Batch servers
Storage appliances
iSCSI for virtual machine (VM) hosts
Fibre Channel storage area network (FC SAN) - SQL server storage
Network-attached storage (NAS) image storage, logs, backups
Apache Hadoop /Spark servers
Core Data Lake
Data analysis workloads
20 miscellaneous servers
Jenkins, monitoring, bastion hosts,
Business Requirements
Build a reliable and reproducible environment with scaled panty of production.
Aggregate data in a centralized Data Lake for analysis
Use historical data to perform predictive analytics on future shipments Accurately track every shipment worldwide using proprietary technology Improve business agility and speed of innovation through rapid provisioning of new resources Analyze and optimize architecture for performance in the cloud Migrate fully to the cloud if all other requirements are met Technical Requirements Handle both streaming and batch data Migrate existing Hadoop workloads Ensure architecture is scalable and elastic to meet the changing demands of the company.
Use managed services whenever possible
Encrypt data flight and at rest
Connect a VPN between the production data center and cloud environment
SEO Statement
We have grown so quickly that our inability to upgrade our infrastructure is really hampering further growth and efficiency. We are efficient at moving shipments around the world, but we are inefficient at moving data around.
We need to organize our information so we can more easily understand where our customers are and what they are shipping.
CTO Statement
IT has never been a priority for us, so as our data has grown, we have not invested enough in our technology. I have a good staff to manage IT, but they are so busy managing our infrastructure that I cannot get them to do the things that really matter, such as organizing our data, building the analytics, and figuring out how to implement the CFO' s tracking technology.
CFO Statement
Part of our competitive advantage is that we penalize ourselves for late shipments and deliveries. Knowing where out shipments are at all times has a direct correlation to our bottom line and profitability. Additionally, I don't want to commit capital to building out a server environment.


NEW QUESTION # 75
You need to create a data pipeline that copies time-series transaction data so that it can be queried from within BigQuery by your data science team for analysis. Every hour, thousands of transactions are updated with a new status. The size of the intitial dataset is 1.5 PB, and it will grow by 3 TB per day. The data is heavily structured, and your data science team will build machine learning models based on this dat
a. You want to maximize performance and usability for your data science team. Which two strategies should you adopt? Choose 2 answers.

  • A. Develop a data pipeline where status updates are appended to BigQuery instead of updated.
  • B. Use BigQuery UPDATE to further reduce the size of the dataset.
  • C. Denormalize the data as must as possible.
  • D. Copy a daily snapshot of transaction data to Cloud Storage and store it as an Avro file. Use BigQuery's support for external data sources to query.
  • E. Preserve the structure of the data as much as possible.

Answer: C,D


NEW QUESTION # 76
......

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