FREE PDF VALID PROFESSIONAL-DATA-ENGINEER VCE–AUTHORIZED TEST SIMULATOR FEE FOR PROFESSIONAL-DATA-ENGINEER

Free PDF Valid Professional-Data-Engineer Vce–Authorized Test Simulator Fee for Professional-Data-Engineer

Free PDF Valid Professional-Data-Engineer Vce–Authorized Test Simulator Fee for Professional-Data-Engineer

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Understanding functional and technical aspects of Google Professional Data Engineer Exam Designing data processing systems

The following will be discussed here:

  • Online (interactive) vs. batch predictions
  • Designing data pipelines
  • Tradeoffs involving latency, throughput, transactions
  • At least once, in-order, and exactly once, etc., event processing
  • Selecting the appropriate storage technologies
  • Schema design
  • Data modeling
  • Architecture options (e.g., message brokers, message queues, middleware, service-oriented architecture, serverless functions)
  • Mapping storage systems to business requirements
  • Designing data processing systems
  • Use of distributed systems
  • Choice of infrastructure

Google Professional-Data-Engineer Certification is a highly respected and sought-after credential in the field of data engineering. Google Certified Professional Data Engineer Exam certification is offered by Google Cloud and is designed for professionals who are skilled in designing and building data processing systems on the Google Cloud Platform. Professional-Data-Engineer exam tests candidates' knowledge of data engineering principles, including data collection, transformation, storage, and analysis.

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Google Certified Professional Data Engineer Exam Sample Questions (Q280-Q285):

NEW QUESTION # 280
Which of these are examples of a value in a sparse vector? (Select 2 answers.)

  • A. [1, 0, 0, 0, 0, 0, 0]
  • B. [0, 0, 0, 1, 0, 0, 1]
  • C. [0, 5, 0, 0, 0, 0]
  • D. [0, 1]

Answer: A,D

Explanation:
Categorical features in linear models are typically translated into a sparse vector in which each possible value has a corresponding index or id. For example, if there are only three possible eye colors you can represent 'eye_color' as a length 3 vector: 'brown' would become [1, 0, 0], 'blue' would become [0, 1, 0] and 'green' would become [0, 0, 1]. These vectors are called "sparse" because they may be very long, with many zeros, when the set of possible values is very large (such as all English words).
[0, 0, 0, 1, 0, 0, 1] is not a sparse vector because it has two 1s in it. A sparse vector contains only a single 1.
[0, 5, 0, 0, 0, 0] is not a sparse vector because it has a 5 in it. Sparse vectors only contain
0 s and 1s.
Reference:
https://www.tensorflow.org/tutorials/linear#feature_columns_and_transformations


NEW QUESTION # 281
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. Build a Cloud Function that reads the topics and makes a call to the Cloud Data Loss Prevention API.
    Use the tagging and confidence levels to either pass or quarantine the data in a bucket for review.
  • B. Create an authorized view in BigQuery to restrict access to tables with sensitive data.
  • C. Use Stackdriver logging to analyze the data passed through the total pipeline to identify transactions that may contain sensitive information.
  • D. Install a third-party data validation tool on Compute Engine virtual machines to check the incoming data for sensitive information.

Answer: B


NEW QUESTION # 282
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.
Flowlogistic's management has determined that the current Apache Kafka servers cannot handle the data volume for their real-time inventory tracking system. You need to build a new system on Google Cloud Platform (GCP) that will feed the proprietary tracking software. The system must be able to ingest data from a variety of global sources, process and query in real-time, and store the data reliably. Which combination of GCP products should you choose?

  • A. Cloud Pub/Sub, Cloud Dataflow, and Local SSD
  • B. Cloud Pub/Sub, Cloud Dataflow, and Cloud Storage
  • C. Cloud Load Balancing, Cloud Dataflow, and Cloud Storage
  • D. Cloud Pub/Sub, Cloud SQL, and Cloud Storage

Answer: D


NEW QUESTION # 283
Which of these is not a supported method of putting data into a partitioned table?

  • A. Run a query to get the records for a specific day from an existing table and for the destination table, specify a partitioned table ending with the day in the format "$YYYYMMDD".
  • B. Create a partitioned table and stream new records to it every day.
  • C. If you have existing data in a separate file for each day, then create a partitioned table and upload each file into the appropriate partition.
  • D. Use ORDER BY to put a table's rows into chronological order and then change the table's type to
    "Partitioned".

Answer: D

Explanation:
You cannot change an existing table into a partitioned table. You must create a partitioned table from scratch.
Then you can either stream data into it every day and the data will automatically be put in the right partition, or you can load data into a specific partition by using "$YYYYMMDD" at the end of the table name.
Reference: https://cloud.google.com/bigquery/docs/partitioned-tables


NEW QUESTION # 284
What are two of the characteristics of using online prediction rather than batch prediction?

  • A. Predictions are returned in the response message.
  • B. Predictions are written to output files in a Cloud Storage location that you specify.
  • C. It is optimized to minimize the latency of serving predictions.
  • D. It is optimized to handle a high volume of data instances in a job and to run more complex models.

Answer: A,C

Explanation:
Online prediction
.Optimized to minimize the latency of serving predictions.
.Predictions returned in the response message.
Batch prediction
.Optimized to handle a high volume of instances in a job and to run more complex models.
.Predictions written to output files in a Cloud Storage location that you specify.


NEW QUESTION # 285
......

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