Professional-Data-Engineer Dumps PDF - Exam4Help
                      
 
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Professional-Da ta-Engineer
Google Cloud Certified Professional Data Engineer
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Case Study: 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 re-taiml e 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 cluste rs
SQL Serve–r  user data, inventory, static data 
3 physical server s
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 ser vers
Tomcat–  Java services 
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Nginx – static content 
Batch servers 
Storage appliances 
iSCSI for virtual machine (VM) ho sts
Fibre Channel storage arenae twork (FC SAN) – SQL server storage 
Network-attached storage (NAS) image storage, logs, backups 
Apache Hadoop /Spark serve rs
Core Data Lak e
Data analysis workload s
20 miscellaneous serve rs
 
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 ana lysis
Use historical data to perform predictive analytics on future shipm ents
Accurately track every shipment worldwide us ipnrgoprietary technology 
Improve business agility and speed of innovation through rapid provisioning o f new
resources 
Analyze and optimize architecture for performance in the c loud
Migrate fully to the cloud if all other requirements are met 
Technical Requirements 
Handle both streaming and batch da ta
Migrate existing Hadoop workloa ds
Ensure architecture is scalable and elastic to meet the changing demands of the co mpany.
Use managed services whenever poss ible
Encrypt data flight and at re st
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 
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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. 
Question: 1 
   
Flowlogistic wants to use Google BigQuery as their primary analysis system, but they still 
have Apache Hadoop and Spark workloads that they cannot move to BigQuery. Flowlogistic 
does not know how to store the data that is common to both workloads. What should they 
do? 
 
 
A. Store the common data in BigQuery as partitioned tables. 
B. Store the common data in BigQuery and expose authorized views. 
C. Store the common data encoded as Avro in Google Cloud Storage. 
D. Store he common data in the HDFS storage for a Google Cloud Dataproc cluster. 
 
Answer: B     
 
Question: 2 
   
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 Cloud Storage 
B. Cloud Pub/Sub, Cloud Dataflow, and Local SSD 
C. Cloud Pub/Sub, Cloud SQL, and Cloud Storage 
D. Cloud Load Balancing, Cloud Dataflow, and Cloud Storage 
 
Answer: C     
 
Question: 3 
   
Flowlogistic’s CEO wants to gain rapid insight into their customer base so his sales team can 
be better informed in the field. This team is not very technical, so they’ve purchased a 
visualization tool to simplify the creation of BigQuery reports. However, they’ve been 
overwhelmed by all the data in the table, and are spending a lot of money on queries trying to 
find the data they need. You want to solve their problem in the most cost-effective way. What 
should you do? 
 
A. Export the data into a Google Sheet for virtualization. 
B. Create an additional table with only the necessary columns. 
C. Create a view on the table to present to the virtualization tool. 
D. Create identity and access management (IAM) roles on the appropriate columns, so only 
they appear in a query. 
 
Answer: C     
 
Question: 4 
   
 
Flowlogistic is rolling out their real-time inventory tracking system. The tracking devices 
will all send package-tracking messages, which will now go to a single Google Cloud 
Pub/Sub topic instead of the Apache Kafka cluster. A subscriber application will then process 
the messages for real-time reporting and store them in Google BigQuery for historical 
analysis. You want to ensure the package data can be analyzed over time. 
Which approach should you take? 
 
A. Attach the timestamp on each message in the Cloud Pub/Sub subscriber application as 
they are received. 
B. Attach the timestamp and Package ID on the outbound message from each publisher 
device as they are sent to Clod Pub/Sub. 
C. Use the NOW () function in BigQuery to record the event’s time. 
D. Use the automatically generated timestamp from Cloud Pub/Sub to order the data. 
 
Answer: B     
 
Case Study: 2 
MJTelco Case Study 
Company Overview 
MJTelco is a startup that plans to build networks in rapidly growing, underserved markets 
around the world. The company has patents for innovative optical communications hardware. 
Based on these patents, they can create many reliable, high-speed backbone links with 
inexpensive hardware. 
Company Background 
Founded by experienced telecom executives, MJTelco uses technologies originally developed 
to overcome communications challenges in space. Fundamental to their operation, they need 
to create a distributed data infrastructure that drives real-time analysis and incorporates 
machine learning to continuously optimize their topologies. Because their hardware is 
inexpensive, they plan to overdeploy the network allowing them to account for the impact of 
dynamic regional politics on location availability and cost. 
Their management and operations teams are situated all around the globe creating many-tomany 
relationship between data consumers and provides in their system. After careful 
consideration, they decided public cloud is the perfect environment to support their needs. 
 
Solution Concept 
MJTelco is running a successful proof-of-concept (PoC) project in its labs. They have two 
primary needs: 
Scale and harden their PoC to support significantly more data flows generatend t hwehye 
ramp to more than 50,000 installations. 
Refine their machin-elearning cycles to verify and improve the dynamic models they use to 
control topology definition. 
MJTelco will also use three separate operating environments – development/test, staging, and 
production – to meet the needs of running experiments, deploying new features, and serving 
production customers. 
Business Requirements 
Scale up their production environment with minimal cost, instantiating resources whe n and
where needed in an unpredictable, distributed telecom user community. 
Ensure security of their proprietary data to protect their lead-eindgge machine learning and 
analysis. 
Provide reliable and timely access to data for analysis from distributed research w orkers
Maintain isolated environments that support rapid iteration of their machine-learning 
models without affecting their customers. 
Technical Requirements 
Ensure secure and efficient transport and storage of telemetry data 
Rapidly scale instances to support between 10,000 and 100,000 data providers with multiple 
flows each. 
Allow analysis and presentation against data tables tracking up to 2 years of data storing 
approximately 100m records/day 
Support rapid iteration of monitoring infrastructure focused on awareness of data pipeline 
 
problems both in telemetry flows and in production learning cycles. 
CEO Statement 
Our business model relies on our patents, analytics and dynamic machine learning. Our 
inexpensive hardware is organized to be highly reliable, which gives us cost advantages. We 
need to quickly stabilize our large distributed data pipelines to meet our reliability and 
capacity commitments. 
CTO Statement 
Our public cloud services must operate as advertised. We need resources that scale and keep 
our data secure. We also need environments in which our data scientists can carefully study 
and quickly adapt our models. Because we rely on automation to process our data, we also 
need our development and test environments to work as we iterate. 
CFO Statement 
The project is too large for us to maintain the hardware and software required for the data 
and analysis. Also, we cannot afford to staff an operations team to monitor so many data 
feeds, so we will rely on automation and infrastructure. Google Cloud’s machine learning 
will allow our quantitative researchers to work on our high-value problems instead of 
problems with our data pipelines. 
 
Question: 5 
   
MJTelco’s Google Cloud Dataflow pipeline is now ready to start receiving data from the 
50,000 installations. You want to allow Cloud Dataflow to scale its compute power up as 
required. Which Cloud Dataflow pipeline configuration setting should you update? 
 
A. The zone 
B. The number of workers 
C. The disk size per worker 
D. The maximum number of workers 
 
 
Answer: A     
 
Question: 6 
   
You need to compose visualizations for operations teams with the following requirements: 
Which approach meets the requirements? 
 
A. Load the data into Google Sheets, use formulas to calculate a metric, and use 
filters/sorting to show only suboptimal links in a table. 
B. Load the data into Google BigQuery tables, write Google Apps Script that queries the 
data, calculates the metric, and shows only suboptimal rows in a table in Google Sheets. 
C. Load the data into Google Cloud Datastore tables, write a Google App Engine Application 
that queries all rows, applies a function to derive the metric, and then renders results in a 
table using the Google charts and visualization API. 
D. Load the data into Google BigQuery tables, write a Google Data Studio 360 report that 
connects to your data, calculates a metric, and then uses a filter expression to show only 
suboptimal rows in a table. 
 
Answer: C     
 
Question: 7 
   
You create a new report for your large team in Google Data Studio 360. The report uses 
Google BigQuery as its data source. It is company policy to ensure employees can view only 
the data associated with their region, so you create and populate a table for each region. You 
need to enforce the regional access policy to the data. 
Which two actions should you take? (Choose two.) 
 
A. Ensure all the tables are included in global dataset. 
B. Ensure each table is included in a dataset for a region. 
C. Adjust the settings for each table to allow a related region-based security group view 
access. 
D. Adjust the settings for each view to allow a related region-based security group view 
access. 
E. Adjust the settings for each dataset to allow a related region-based security group view 
access. 
 
Answer: B,D     
 
Question: 8 
   
MJTelco needs you to create a schema in Google Bigtable that will allow for the historical 
 
analysis of the last 2 years of records. Each record that comes in is sent every 15 minutes, 
and contains a unique identifier of the device and a data record. The most common query is 
for all the data for a given device for a given day. Which schema should you use? 
 
A. Rowkey: date#device_idColumn data: data_point 
B. Rowkey: dateColumn data: device_id, data_point 
C. Rowkey: device_idColumn data: date, data_point 
D. Rowkey: data_pointColumn data: device_id, date 
E. Rowkey: date#data_pointColumn data: device_id 
 
Answer: D     
 
Question: 9 
   
MJTelco is building a custom interface to share data. They have these requirements: 
They need to do aggregations over their petab-syctaele datasets. 
They need to scan specific time range rows with a very fast response time (millise conds).
Which combination of Google Cloud Platform products should you recommend? 
 
A. Cloud Datastore and Cloud Bigtable 
B. Cloud Bigtable and Cloud SQL 
C. BigQuery and Cloud Bigtable 
D. BigQuery and Cloud Storage 
 
Answer: C     
 
Question: 10 
   
You need to compose visualization for operations teams with the following requirements: 
Telemetry must include data from all 50,000 installations for the most rec ent 6
weeks (sampling once every minute) 
The report must not be more than 3 hours delayeodm f rlive data. 
The actionable reporsth ould only show suboptimal links. 
Most suboptimal links should be sorted to the t op.
Suboptimal links can be grouped and filtered by regional geogr aphy.
User response time to load the report must be