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To become a Google Certified Professional Data Engineer, candidates need to pass the Professional-Data-Engineer exam. Professional-Data-Engineer exam is designed to test the knowledge and skills of professionals in designing, building, and maintaining data processing systems. Professional-Data-Engineer exam consists of multiple-choice and scenario-based questions that test the candidate's ability to analyze and solve real-world problems related to data engineering.

The Google Professional-Data-Engineer exam consists of multiple-choice questions and requires candidates to demonstrate their knowledge of various data engineering concepts and technologies. The questions are designed to test the candidate's ability to analyze data, transform data, and implement data processing solutions using Google Cloud Platform's tools and services. Professional-Data-Engineer exam is two hours long, and candidates must achieve a passing score of 70% or higher to earn the certification.

Google Professional-Data-Engineer exam is a certification exam offered by Google for professionals who work with data engineering. Professional-Data-Engineer exam is designed to test the individual's knowledge and skills in using Google Cloud Platform tools and services for data engineering. Professional-Data-Engineer exam is intended to validate the candidate's ability to design, build, operationalize, and secure data processing systems using Google Cloud Platform technologies.

 

NEW QUESTION # 133
Which of the following IAM roles does your Compute Engine account require to be able to run pipeline jobs?

• A. dataflow.developer
• B. dataflow.viewer
• C. dataflow.worker
• D. dataflow.compute

Answer: C

Explanation:
The dataflow.worker role provides the permissions necessary for a Compute Engine service
account to execute work units for a Dataflow pipeline

NEW QUESTION # 134
You are developing an application that uses a recommendation engine on Google Cloud. Your solution should display new videos to customers based on past views. Your solution needs to generate labels for the entities in videos that the customer has viewed. Your design must be able to provide very fast filtering suggestions based on data from other customer preferences on several TB of dat
a. What should you do?

• A. Build an application that calls the Cloud Video Intelligence API to generate labels. Store data in Cloud
  Bigtable, and filter the predicted labels to match the user's viewing history to generate preferences.
• B. Build and train a classification model with Spark MLlib to generate labels. Build and train a second
  classification model with Spark MLlib to filter results to match customer preferences. Deploy the models
  using Cloud Dataproc. Call the models from your application.
• C. Build and train a complex classification model with Spark MLlib to generate labels and filter the results.
  Deploy the models using Cloud Dataproc. Call the model from your application.
• D. Build an application that calls the Cloud Video Intelligence API to generate labels. Store data in Cloud
  SQL, and join and filter the predicted labels to match the user's viewing history to generate preferences.

Answer: A

NEW QUESTION # 135
To give a user read permission for only the first three columns of a table, which access control method would you use?

• A. It's not possible to give access to only the first three columns of a table.
• B. Authorized view
• C. Primitive role
• D. Predefined role

Answer: B

Explanation:
An authorized view allows you to share query results with particular users and groups without giving them read access to the underlying tables. Authorized views can only be created in a dataset that does not contain the tables queried by the view.
When you create an authorized view, you use the view's SQL query to restrict access to only the rows and columns you want the users to see.

NEW QUESTION # 136
You designed a database for patient records as a pilot project to cover a few hundred patients in three clinics. Your design used a single database table to represent all patients and their visits, and you used self-joins to generate reports. The server resource utilization was at 50%. Since then, the scope of the project has expanded. The database must now store 100 times more patient records. You can no longer run the reports, because they either take too long or they encounter errors with insufficient compute resources. How should you adjust the database design?

• A. Add capacity (memory and disk space) to the database server by the order of 200.
• B. Partition the table into smaller tables, with one for each clinic. Run queries against the smaller table pairs, and use unions for consolidated reports.
• C. Normalize the master patient-record table into the patient table and the visits table, and create other necessary tables to avoid self-join.
• D. Shard the tables into smaller ones based on date ranges, and only generate reports with prespecified date ranges.

Answer: C

Explanation:
It provides the least amount of inconvenience over using pre-specified date ranges or one table per clinic while also increasing performance due to avoiding self-joins.

NEW QUESTION # 137
You are migrating your data warehouse to Google Cloud and decommissioning your on-premises data center Because this is a priority for your company, you know that bandwidth will be made available for the initial data load to the cloud. The files being transferred are not large in number, but each file is 90 GB Additionally, you want your transactional systems to continually update the warehouse on Google Cloud in real time What tools should you use to migrate the data and ensure that it continues to write to your warehouse?

• A. gsutil for the migration; Pub/Sub and Dataflow for the real-time updates
• B. Storage Transfer Service for the migration, Pub/Sub and Cloud Data Fusion for the real-time updates
• C. BigQuery Data Transfer Service lor the migration, Pub/Sub and Dataproc for the real-time updates
• D. gsutil for both the migration and the real-time updates

Answer: A

NEW QUESTION # 138
You need to migrate a 2TB relational database to Google Cloud Platform. You do not have the resources to significantly refactor the application that uses this database and cost to operate is of primary concern.
Which service do you select for storing and serving your data?

• A. Cloud Firestore
• B. Cloud SQL
• C. Cloud Bigtable
• D. Cloud Spanner

Answer: B

NEW QUESTION # 139
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 and share a new dataset and view that provides the aggregate results.
• B. Create and share a new dataset and table that contains the aggregate results.
• C. Create dataViewer Identity and Access Management (IAM) roles on the dataset to enable sharing.
• D. Create and share an authorized view that provides the aggregate results.

Answer: C

NEW QUESTION # 140
You are collecting loT sensor data from millions of devices across the world and storing the data in BigQuery. Your access pattern is based on recent data tittered by location_id and device_version with the following query:

You want to optimize your queries for cost and performance. How should you structure your data?

• A. Partition table data by create_date cluster table data by tocation_id and device_version
• B. Cluster table data by create_date location_id and device_version
• C. Partition table data by create_date, location_id and device_version
• D. Cluster table data by create_date, partition by location and device_version

Answer: B

NEW QUESTION # 141
Your software uses a simple JSON format for all messages. These messages are published to Google Cloud Pub/Sub, then processed with Google Cloud Dataflow to create a real-time dashboard for the CFO.
During testing, you notice that some messages are missing in the dashboard. You check the logs, and all messages are being published to Cloud Pub/Sub successfully. What should you do next?

• A. Check the dashboard application to see if it is not displaying correctly.
• B. Switch Cloud Dataflow to pull messages from Cloud Pub/Sub instead of Cloud Pub/Sub pushing messages to Cloud Dataflow.
• C. Run a fixed dataset through the Cloud Dataflow pipeline and analyze the output.
• D. Use Google Stackdriver Monitoring on Cloud Pub/Sub to find the missing messages.

Answer: D

Explanation:
Stackdriver can be used to check the error like number of unack messages, publisher pushing messages faster.

NEW QUESTION # 142
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 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 he common data in the HDFS storage for a Google Cloud Dataproc cluster.
• B. Store the common data in BigQuery as partitioned tables.
• C. Store the common data encoded as Avro in Google Cloud Storage.
• D. Store the common data in BigQuery and expose authorized views.

Answer: D

NEW QUESTION # 143
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: https://cloud.google.com/solutions/business-intelligence/

NEW QUESTION # 144
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.
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 maximum number of workers
• B. The zone
• C. The disk size per worker
• D. The number of workers

Answer: B

NEW QUESTION # 145
Your company has hired a new data scientist who wants to perform complicated analyses across very large datasets stored in Google Cloud Storage and in a Cassandra cluster on Google Compute Engine.
The scientist primarily wants to create labelled data sets for machine learning projects, along with some visualization tasks. She reports that her laptop is not powerful enough to perform her tasks and it is slowing her down. You want to help her perform her tasks. What should you do?

• A. Deploy Google Cloud Datalab to a virtual machine (VM) on Google Compute Engine.
• B. Run a local version of Jupiter on the laptop.
• C. Host a visualization tool on a VM on Google Compute Engine.
• D. Grant the user access to Google Cloud Shell.

Answer: A

Explanation:
Datalab provides Jupyter for this kind of work.

NEW QUESTION # 146
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. 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.
• 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. 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.
• D. 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.
• 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: D

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 # 147
A data scientist has created a BigQuery ML model and asks you to create an ML pipeline to serve predictions.
You have a REST API application with the requirement to serve predictions for an individual user ID with latency under 100 milliseconds. You use the following query to generate predictions: SELECT predicted_label, user_id FROM ML.PREDICT (MODEL 'dataset.model', table user_features). How should you create the ML pipeline?

• A. Create an Authorized View with the provided query. Share the dataset that contains the view with the application service account.
• B. Add a WHERE clause to the query, and grant the BigQuery Data Viewer role to the application service account.
• C. Create a Cloud Dataflow pipeline using BigQueryIO to read results from the query. Grant the Dataflow Worker role to the application service account.
• D. Create a Cloud Dataflow pipeline using BigQueryIO to read predictions for all users from the query.
  Write the results to Cloud Bigtable using BigtableIO. Grant the Bigtable Reader role to the application service account so that the application can read predictions for individual users from Cloud Bigtable.

Answer: D

NEW QUESTION # 148
Your company handles data processing for a number of different clients. Each client prefers to use their own suite of analytics tools, with some allowing direct query access via Google BigQuery. You need to secure the data so that clients cannot see each other's dat
a. You want to ensure appropriate access to the data. Which three steps should you take? (Choose three.)

• A. Load data into different partitions.
• B. Restrict a client's dataset to approved users.
• C. Load data into a different dataset for each client.
• D. Only allow a service account to access the datasets.
• E. Use the appropriate identity and access management (IAM) roles for each client's users.
• F. Put each client's BigQuery dataset into a different table.

Answer: B,C,E

NEW QUESTION # 149
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.
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 maximum number of workers
• B. The zone
• C. The disk size per worker
• D. The number of workers

Answer: B

NEW QUESTION # 150
Your infrastructure includes a set of YouTube channels. You have been tasked with creating a process for
sending the YouTube channel data to Google Cloud for analysis. You want to design a solution that allows
your world-wide marketing teams to perform ANSI SQL and other types of analysis on up-to-date YouTube
channels log data. How should you set up the log data transfer into Google Cloud?

• A. Use BigQuery Data Transfer Service to transfer the offsite backup files to a Cloud Storage Multi-
  Regional storage bucket as a final destination.
• B. Use BigQuery Data Transfer Service to transfer the offsite backup files to a Cloud Storage Regional
  storage bucket as a final destination.
• C. Use Storage Transfer Service to transfer the offsite backup files to a Cloud Storage Multi-Regional
  storage bucket as a final destination.
• D. Use Storage Transfer Service to transfer the offsite backup files to a Cloud Storage Regional bucket as
  a final destination.

Answer: D

NEW QUESTION # 151
Which is the preferred method to use to avoid hotspotting in time series data in Bigtable?

• A. Hashing
• B. Field promotion
• C. Randomization
• D. Salting

Answer: B

Explanation:
Explanation
By default, prefer field promotion. Field promotion avoids hotspotting in almost all cases, and it tends to make it easier to design a row key that facilitates queries.
Reference:
https://cloud.google.com/bigtable/docs/schema-design-time-series#ensure_that_your_row_key_avoids_hotspotti

NEW QUESTION # 152
Which of these rules apply when you add preemptible workers to a Dataproc cluster (select
2 answers)?

• A. Preemptible workers cannot store data.
• B. A Dataproc cluster cannot have only preemptible workers.
• C. Preemptible workers cannot use persistent disk.
• D. If a preemptible worker is reclaimed, then a replacement worker must be added manually.

Answer: A,B

Explanation:
The following rules will apply when you use preemptible workers with a Cloud Dataproc cluster:
Processing only-Since preemptibles can be reclaimed at any time, preemptible workers do not store data. Preemptibles added to a Cloud Dataproc cluster only function as processing nodes.
No preemptible-only clusters-To ensure clusters do not lose all workers, Cloud Dataproc cannot create preemptible-only clusters.
Persistent disk size-As a default, all preemptible workers are created with the smaller of
100GB or the primary worker boot disk size. This disk space is used for local caching of data and is not available through HDFS.
The managed group automatically re-adds workers lost due to reclamation as capacity permits.
Reference: https://cloud.google.com/dataproc/docs/concepts/preemptible-vms

NEW QUESTION # 153
......

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