[Q91-Q116] May-2024 Realistic Professional-Data-Engineer Accurate & Verified Answers As Experienced in the Actual Test!

May-2024 Realistic Professional-Data-Engineer Accurate & Verified Answers As Experienced in the Actual Test!

Latest Google Professional-Data-Engineer Practice Test Questions, Google Certified Professional Data Engineer Exam Exam Dumps

Google Professional-Data-Engineer certification is a valuable asset for data professionals who are seeking to advance their career in the field of data engineering. It demonstrates that a candidate has the skills and knowledge required to design, build, and maintain data processing systems on Google Cloud Platform, which is a highly sought-after skill in today’s data-driven world.

Career Path

Completing the exam associated with the Google Professional Data Engineer certification provides you with a great validation of your skills in designing, building, operationalizing, securing, and monitoring data processing systems. The job roles that you can take up after getting certified include a Google Cloud Data Engineer, an Operations Engineer, a Cloud Infrastructure Engineer, a DevOps Infrastructure Engineer, a Cloud Database Engineer, a Google Cloud IAM Engineer, a DataOps Engineer, a Big Data Engineer, a Google Cloud Platform Data Architect, and more. The average salary that you can expect to earn with this certificate is around $125,550 per year. However, the real remuneration will depend on a specific job title, location of an individual, and his/her working experience.

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.

 

NEW QUESTION # 91
When creating a new Cloud Dataproc cluster with the projects.regions.clusters.create operation, these four values are required: project, region, name, and ____.

• A. zone
• B. type
• C. node
• D. label

Answer: A

Explanation:
At a minimum, you must specify four values when creating a new cluster with the projects.regions.clusters.create operation:
The project in which the cluster will be created
The region to use
The name of the cluster
The zone in which the cluster will be created
You can specify many more details beyond these minimum requirements. For example, you can
also specify the number of workers, whether preemptible compute should be used, and the network settings.

NEW QUESTION # 92
Your company maintains a hybrid deployment with GCP, where analytics are performed on your
anonymized customer data. The data are imported to Cloud Storage from your data center through parallel
uploads to a data transfer server running on GCP. Management informs you that the daily transfers take
too long and have asked you to fix the problem. You want to maximize transfer speeds. Which action
should you take?

• A. Increase the size of the Google Persistent Disk on your server.
• B. Increase your network bandwidth from Compute Engine to Cloud Storage.
• C. Increase your network bandwidth from your datacenter to GCP.
• D. Increase the CPU size on your server.

Answer: C

Explanation:
Explanation/Reference:

NEW QUESTION # 93
An organization maintains a Google BigQuery dataset that contains tables with user-level data. They want to expose aggregates of this data to other Google Cloud projects, while still controlling access to the user- level data. Additionally, they need to minimize their overall storage cost and ensure the analysis cost for other projects is assigned to those projects. What should they do?

• A. Create dataViewer Identity and Access Management (IAM) roles on the dataset to enable sharing.
• B. Create and share an authorized view that provides the aggregate results.
• C. Create and share a new dataset and table that contains the aggregate results.
• D. Create and share a new dataset and view that provides the aggregate results.

Answer: A

Explanation:
Explanation/Reference:
Reference: https://cloud.google.com/bigquery/docs/access-control

NEW QUESTION # 94
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-to-many 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 generated when they ramp to more than 50,000 installations.
* Refine their machine-learning 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 when and where needed in an unpredictable, distributed telecom user community.
* Ensure security of their proprietary data to protect their leading-edge machine learning and analysis.
* Provide reliable and timely access to data for analysis from distributed research workers
* 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.
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 each table is included in a dataset for a region.
• B. Adjust the settings for each table to allow a related region-based security group view access.
• C. Adjust the settings for each view to allow a related region-based security group view access.
• D. Ensure all the tables are included in global dataset.
• E. Adjust the settings for each dataset to allow a related region-based security group view access.

Answer: A,C

NEW QUESTION # 95
Which of the following statements is NOT true regarding Bigtable access roles?

• A. You can configure access control only at the project level.
• B. To give a user access to only one table in a project, you must configure access through your application.
• C. To give a user access to only one table in a project, grant the user the Bigtable Editor role for
  that table.
• D. Using IAM roles, you cannot give a user access to only one table in a project, rather than all tables in a project.

Answer: C

Explanation:
For Cloud Bigtable, you can configure access control at the project level. For example, you can grant the ability to:
Read from, but not write to, any table within the project.
Read from and write to any table within the project, but not manage instances.
Read from and write to any table within the project, and manage instances.

NEW QUESTION # 96
You create an important report for your large team in Google Data Studio 360. The report uses Google BigQuery as its data source. You notice that visualizations are not showing data that is less than 1 hour old. What should you do?

• A. Clear your browser history for the past hour then reload the tab showing the virtualizations.
• B. Disable caching by editing the report settings.
• C. Disable caching in BigQuery by editing table details.
• D. Refresh your browser tab showing the visualizations.

Answer: B

Explanation:
https://support.google.com/datastudio/answer/7020039?hl=en

NEW QUESTION # 97
You are developing a model to identify the factors that lead to sales conversions for your customers. You have completed processing your data. You want to continue through the model development lifecycle. What should you do next?

• A. Monitor your model performance, and make any adjustments needed.
• B. Test and evaluate your model on your curated data to determine how well the model performs.
• C. Delineate what data will be used for testing and what will be used for training the model.
• D. Use your model to run predictions on fresh customer input data.

Answer: B

Explanation:
After processing your data, the next step in the model development lifecycle is to test and evaluate your model on the curated data. This is crucial to determine the performance of the model and to understand how well it can predict sales conversions for your customers. The evaluation phase involves using various metrics and techniques to assess the accuracy, precision, recall, and other relevant performance indicators of the model. It helps in identifying any issues or areas for improvement before deploying the model in a production environment. References: The information provided here is verified by the Google Professional Data Engineer Certification Exam Guide and related resources, which outline the steps and best practices in the model development lifecycle

NEW QUESTION # 98
You have a data stored in BigQuery. The data in the BigQuery dataset must be highly available. You need to define a storage, backup, and recovery strategy of this data that minimizes cost. How should you configure the BigQuery table?

• A. Set the BigQuery dataset to be regional. In the event of an emergency, use a point-in-time snapshot to recover the data.
• B. Set the BigQuery dataset to be multi-regional. Create a scheduled query to make copies of the data to tables suffixed with the time of the backup. In the event of an emergency, use the backup copy of the table.
• C. Set the BigQuery dataset to be regional. Create a scheduled query to make copies of the data to tables suffixed with the time of the backup. In the event of an emergency, use the backup copy of the table.
• D. Set the BigQuery dataset to be multi-regional. In the event of an emergency, use a point-in-time snapshot to recover the data.

Answer: C

NEW QUESTION # 99
You are choosing a NoSQL database to handle telemetry data submitted from millions of Internet-of-Things (IoT) devices. The volume of data is growing at 100 TB per year, and each data entry has about 100 attributes. The data processing pipeline does not require atomicity, consistency, isolation, and durability (ACID). However, high availability and low latency are required. You need to analyze the data by querying against individual fields. Which three databases meet your requirements? (Choose three.)

• A. MongoDB
• B. MySQL
• C. HDFS with Hive
• D. Cassandra
• E. HBase
• F. Redis

Answer: A,C,E

NEW QUESTION # 100
You have spent a few days loading data from comma-separated values (CSV) files into the Google BigQuery table CLICK_STREAM. The column DT stores the epoch time of click events. For convenience, you chose a simple schema where every field is treated as the STRING type. Now, you want to compute web session durations of users who visit your site, and you want to change its data type to the TIMESTAMP. You want to minimize the migration effort without making future queries computationally expensive. What should you do?

• A. Create a view CLICK_STREAM_V, where strings from the column DT are cast into TIMESTAMP values. the view CLICK_STREAM_V instead of the table CLICK_STREAM from now on.
• B. Delete the table CLICK_STREAM, and then re-create it such that the column DT is of the TIMESTAMP type. Reload the data.
• C. Construct a query to return every row of the table CLICK_STREAM, while using the built-in function to cast strings from the column DT into TIMESTAMP values. Run the query into a destination table NEW_CLICK_STREAM, in which the column TS is the TIMESTAMP type. the table NEW_CLICK_STREAM instead of the table CLICK_STREAM from now on. In the future, new data is loaded into the table NEW_CLICK_STREAM.
• D. Add a column TS of the TIMESTAMP type to the table CLICK_STREAM, and populate the numeric values from the column TS for each row. the column TS instead of the column DT from now on.
• E. Add two columns to the table CLICK STREAM: TS of the TIMESTAMP type and IS_NEW of the BOOLEAN type. Reload all data in append mode. For each appended row, set the value of IS_NEW to true. For future queries, the column TS instead of the column DT, with the WHERE clause ensuring that the value of IS_NEW must be true.

Answer: C

Explanation:
It's better to create a new table and delete old one when we are changing the datatype is permanent. View is not suitable because every time the query will run and additional charges will be applied.

NEW QUESTION # 101
You work for an airline and you need to store weather data in a BigQuery table Weather data will be used as input to a machine learning model. The model only uses the last 30 days of weather data. You want to avoid storing unnecessary data and minimize costs. What should you do?

• A. Create a BigQuery table partitioned by datetime value of the weather date Set up partition expiration to
  30 days.
• B. Create a BigQuery table partitioned by ingestion time Set up partition expiration to 30 days.
• C. Create a BigQuery table where each record has an ingestion timestamp Run a scheduled query to delete all the rows with an ingestion timestamp older than 30 days.
• D. Create a BigQuery table with a datetime column for the day the weather data refers to. Run a scheduled query to delete rows with a datetime value older than 30 days.

Answer: B

Explanation:
Partitioning a table by ingestion time means that the data is divided into partitions based on the time when the data was loaded into the table. This allows you to delete or archive old data by setting a partition expiration policy. You can specify the number of days to keep the data in each partition, and BigQuery automatically deletes the data when it expires. This way, you can avoid storing unnecessary data and minimize costs.

NEW QUESTION # 102
The marketing team at your organization provides regular updates of a segment of your customer dataset.
The marketing team has given you a CSV with 1 million records that must be updated in BigQuery. When you use the UPDATE statement in BigQuery, you receive a quotaExceeded error. What should you do?

• A. Increase the BigQuery UPDATE DML statement limit in the Quota management section of the Google Cloud Platform Console.
• B. Reduce the number of records updated each day to stay within the BigQuery UPDATE DML statement limit.
• C. Split the source CSV file into smaller CSV files in Cloud Storage to reduce the number of BigQuery UPDATE DML statements per BigQuery job.
• D. Import the new records from the CSV file into a new BigQuery table. Create a BigQuery job that merges the new records with the existing records and writes the results to a new BigQuery table.

Answer: D

Explanation:
https://cloud.google.com/blog/products/gcp/performing-large-scale-mutations-in-bigquery

NEW QUESTION # 103
You are managing a Cloud Dataproc cluster. You need to make a job run faster while minimizing costs, without losing work in progress on your clusters. What should you do?

• A. Increase the cluster size with preemptible worker nodes, and configure them to forcefully decommission.
• B. Increase the cluster size with preemptible worker nodes, and configure them to use graceful decommissioning.
• C. Increase the cluster size with preemptible worker nodes, and use Cloud Stackdriver to trigger a script to preserve work.
• D. Increase the cluster size with more non-preemptible workers.

Answer: B

Explanation:
Reference https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/flex

NEW QUESTION # 104
You are deploying a new storage system for your mobile application, which is a media streaming service.
You decide the best fit is Google Cloud Datastore. You have entities with multiple properties, some of which can take on multiple values. For example, in the entity 'Movie'the property 'actors'and the property 'tags' have multiple values but the property 'date released' does not. A typical query would ask for all movies with actor=<actorname>ordered by date_releasedor all movies with tag=Comedyordered by date_released. How should you avoid a combinatorial explosion in the number of indexes?


C: Set the following in your entity options: exclude_from_indexes = 'actors, tags' D: Set the following in your entity options: exclude_from_indexes = 'date_published'

• A. Option A
• B. Option D
• C. Option C
• D. Option B.

Answer: A

NEW QUESTION # 105
You want to analyze hundreds of thousands of social media posts daily at the lowest cost and with the fewest steps.
You have the following requirements:
* You will batch-load the posts once per day and run them through the Cloud Natural Language API.
* You will extract topics and sentiment from the posts.
* You must store the raw posts for archiving and reprocessing.
* You will create dashboards to be shared with people both inside and outside your organization.
You need to store both the data extracted from the API to perform analysis as well as the raw social media posts for historical archiving. What should you do?

• A. Store the raw social media posts in Cloud Storage, and write the data extracted from the API into BigQuery.
• B. Store the social media posts and the data extracted from the API in BigQuery.
• C. Store the social media posts and the data extracted from the API in Cloud SQL.
• D. Feed to social media posts into the API directly from the source, and write the extracted data from the API into BigQuery.

Answer: A

Explanation:
Social media posts can images/videos which cannot be stored in bigquery/

NEW QUESTION # 106
You store and analyze your relational data in BigQuery on Google Cloud with all data that resides in US regions. You also have a variety of object stores across Microsoft Azure and Amazon Web Services (AWS), also in US regions. You want to query all your data in BigQuery daily with as little movement of data as possible. What should you do?

• A. Create a Dataflow pipeline to ingest files from Azure and AWS to BigQuery.
• B. Use BigQuery Data Transfer Service to load files from Azure and AWS into BigQuery.
• C. Load files from AWS and Azure to Cloud Storage with Cloud Shell gautil rsync arguments.
• D. Use the BigQuery Omni functionality and BigLake tables to query files in Azure and AWS.

Answer: D

Explanation:
BigQuery Omni is a multi-cloud analytics solution that lets you use the BigQuery interface to analyze data stored in other public clouds, such as AWS and Azure, without moving or copying the data. BigLake tables are a type of external table that let you query structured data in external data stores with access delegation. By using BigQuery Omni and BigLake tables, you can query data in AWS and Azure object stores directly from BigQuery, with minimal data movement and consistent performance. References:
* 1: Introduction to BigLake tables
* 2: Deep dive on how BigLake accelerates query performance
* 3: BigQuery Omni and BigLake (Analytics Data Federation on GCP)

NEW QUESTION # 107
You are building new real-time data warehouse for your company and will use Google BigQuery streaming inserts. There is no guarantee that data will only be sent in once but you do have a unique ID for each row of data and an event timestamp. You want to ensure that duplicates are not included while interactively querying dat
a. Which query type should you use?

• A. Include ORDER BY DESK on timestamp column and LIMIT to 1.
• B. Use the ROW_NUMBER window function with PARTITION by unique ID along with WHERE row equals 1.
• C. Use the LAG window function with PARTITION by unique ID along with WHERE LAG IS NOT NULL.
• D. Use GROUP BY on the unique ID column and timestamp column and SUM on the values.

Answer: B

Explanation:
https://cloud.google.com/bigquery/docs/reference/standard-sql/analytic-function-concepts

NEW QUESTION # 108
You are using Google BigQuery as your data warehouse. Your users report that the following simple query is running very slowly, no matter when they run the query:
SELECT country, state, city FROM [myproject:mydataset.mytable] GROUP BY country
You check the query plan for the query and see the following output in the Read section of Stage:1:

What is the most likely cause of the delay for this query?

• A. Most rows in the [myproject:mydataset.mytable] table have the same value in the country column, causing data skew
• B. Either the state or the city columns in the [myproject:mydataset.mytable] table have too many
  NULL values
• C. Users are running too many concurrent queries in the system
• D. The [myproject:mydataset.mytable] table has too many partitions

Answer: C

NEW QUESTION # 109
You work for an advertising company, and you've developed a Spark ML model to predict click-through rates at advertisement blocks. You've been developing everything at your on-premises data center, and now your company is migrating to Google Cloud. Your data center will be migrated to BigQuery. You periodically retrain your Spark ML models, so you need to migrate existing training pipelines to Google Cloud. What should you do?

• A. Use Cloud Dataproc for training existing Spark ML models, but start reading data directly from BigQuery
• B. Spin up a Spark cluster on Compute Engine, and train Spark ML models on the data exported from BigQuery
• C. Rewrite your models on TensorFlow, and start using Cloud ML Engine
• D. Use Cloud ML Engine for training existing Spark ML models

Answer: D

NEW QUESTION # 110
You have several Spark jobs that run on a Cloud Dataproc cluster on a schedule. Some of the jobs run in sequence, and some of the jobs run concurrently. You need to automate this process. What should you do?

• A. Create an initialization action to execute the jobs
• B. Create a Bash script that uses the Cloud SDK to create a cluster, execute jobs, and then tear down the cluster
• C. Create a Directed Acyclic Graph in Cloud Composer
• D. Create a Cloud Dataproc Workflow Template

Answer: D

Explanation:
Explanation/Reference: https://cloud.google.com/dataproc/docs/concepts/workflows/using-workflows

NEW QUESTION # 111
You are building a data pipeline on Google Cloud. You need to prepare data using a casual method for a
machine-learning process. You want to support a logistic regression model. You also need to monitor and
adjust for null values, which must remain real-valued and cannot be removed. What should you do?

• A. Use Cloud Dataprep to find null values in sample source data. Convert all nulls to 0 using a Cloud
  Dataprep job.
• B. Use Cloud Dataflow to find null values in sample source data. Convert all nulls to using a custom script.
• C. Use Cloud Dataflow to find null values in sample source data. Convert all nulls to 'none' using a Cloud
  Dataprep job.
• D. Use Cloud Dataprep to find null values in sample source data. Convert all nulls to 'none' using a Cloud
  Dataproc job.

Answer: C

NEW QUESTION # 112
Case Study 1 - Flowlogistic
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
* 10 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 Cloud Storage
• B. Cloud Pub/Sub, Cloud SQL, and Cloud Storage
• C. Cloud Pub/Sub, Cloud Dataflow, and Local SSD
• D. Cloud Load Balancing, Cloud Dataflow, and Cloud Storage

Answer: A

Explanation:
Pub/Sub -> Dataflow for real time processing requirements.
https://codelabs.developers.google.com/codelabs/cpb104-pubsub/#0

NEW QUESTION # 113
An online retailer has built their current application on Google App Engine. A new initiative at the company
mandates that they extend their application to allow their customers to transact directly via the application.
They need to manage their shopping transactions and analyze combined data from multiple datasets using
a business intelligence (BI) tool. They want to use only a single database for this purpose. Which Google
Cloud database should they choose?

• A. BigQuery
• B. Cloud BigTable
• C. Cloud SQL
• D. Cloud Datastore

Answer: B

Explanation:
Explanation/Reference:
Reference: https://cloud.google.com/solutions/business-intelligence/

NEW QUESTION # 114
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 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. Create a view on the table to present to the virtualization tool.
• B. Create identity and access management (IAM) roles on the appropriate columns, so only they appear in a query.
• C. Create an additional table with only the necessary columns.
• D. Export the data into a Google Sheet for virtualization.

Answer: A

NEW QUESTION # 115
Which row keys are likely to cause a disproportionate number of reads and/or writes on a particular node in a Bigtable cluster (select 2 answers)?

• A. A stock symbol followed by a timestamp
• B. A sequential numeric ID
• C. A timestamp followed by a stock symbol
• D. A non-sequential numeric ID

Answer: B,C

Explanation:
...using a timestamp as the first element of a row key can cause a variety of problems.
In brief, when a row key for a time series includes a timestamp, all of your writes will target a single node; fill that node; and then move onto the next node in the cluster, resulting in hotspotting.
Suppose your system assigns a numeric ID to each of your application's users. You might be tempted to use the user's numeric ID as the row key for your table. However, since new users are more likely to be active users, this approach is likely to push most of your traffic to a small number of nodes. [https://cloud.google.com/bigtable/docs/schema-design]

NEW QUESTION # 116
......

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