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Google Professional-Data-Engineer certification exam is a comprehensive exam that requires detailed knowledge of data engineering concepts and technologies. It is designed to assess the candidate's ability to apply this knowledge to real-world scenarios, and to design and implement solutions that meet the needs of a wide range of users. Professional-Data-Engineer exam is intended for professionals who have experience working with data engineering technologies and who are looking to advance their careers in this field.
Google Professional-Data-Engineer Certification Exam is an exam offered by Google Cloud that provides individuals with the opportunity to demonstrate their proficiency in designing and building data processing systems on Google Cloud Platform. Google Certified Professional Data Engineer Exam certification is intended for individuals who have experience in data processing and have worked with Google Cloud Platform technologies. Google Certified Professional Data Engineer Exam certification exam measures a candidate's ability to design, build, operationalize, secure, and monitor data processing systems.
To prepare for the exam, candidates can take advantage of various resources provided by Google, such as online training courses, practice exams, and study guides. In addition, candidates can gain hands-on experience with Google Cloud Platform by working on real-world projects and labs. With the increasing demand for data engineers and the growing popularity of cloud-based solutions, the Google Professional-Data-Engineer certification can provide a significant boost to an individual's career prospects in the field of data engineering.
NEW QUESTION # 213
You are designing the architecture to process your data from Cloud Storage to BigQuery by using Dataflow.
The network team provided you with the Shared VPC network and subnetwork to be used by your pipelines.
You need to enable the deployment of the pipeline on the Shared VPC network. What should you do?
- A. Assign the compute. networkUser role to the Dataflow service agent.
- B. Assign the dataflow, admin role to the service account that executes the Dataflow pipeline.
- C. Assign the compute.networkUser role to the service account that executes the Dataflow pipeline.
- D. Assign the dataflow, admin role to the Dataflow service agent.
Answer: C
Explanation:
To use a Shared VPC network for a Dataflow pipeline, you need to specify the subnetwork parameter with the full URL of the subnetwork, and grant the service account that executes the pipeline the compute.
networkUser role in the host project. This role allows the service account to use the subnetworks in the Shared VPC network. The Dataflow service agent does not need this role, as it only creates and manages the resources for the pipeline, but does not execute it. The dataflow.admin role is not related to the network access, but to the permissions to create and delete Dataflow jobs and resources. References:
* Specify a network and subnetwork | Cloud Dataflow | Google Cloud
* How to config dataflow Pipeline to use a Shared VPC?
NEW QUESTION # 214
In order to securely transfer web traffic data from your computer's web browser to the Cloud Dataproc cluster you should use a(n) _____.
- A. SSH tunnel
- B. VPN connection
- C. FTP connection
- D. Special browser
Answer: A
Explanation:
To connect to the web interfaces, it is recommended to use an SSH tunnel to create a secure connection to the master node.
Reference:
https://cloud.google.com/dataproc/docs/concepts/cluster-web-interfaces#connecting_to_the_web_interfaces
NEW QUESTION # 215
You are creating a new pipeline in Google Cloud to stream IoT data from Cloud Pub/Sub through Cloud Dataflow to BigQuery. While previewing the data, you notice that roughly 2% of the data appears to be corrupt. You need to modify the Cloud Dataflow pipeline to filter out this corrupt data. What should you do?
- A. Add a ParDo transform in Cloud Dataflow to discard corrupt elements.
- B. Add a SideInput that returns a Boolean if the element is corrupt.
- C. Add a GroupByKey transform in Cloud Dataflow to group all of the valid data together and discard the rest.
- D. Add a Partition transform in Cloud Dataflow to separate valid data from corrupt data.
Answer: A
NEW QUESTION # 216
Your globally distributed auction application allows users to bid on items. Occasionally, users place identical bids at nearly identical times, and different application servers process those bids. Each bid event contains the item, amount, user, and timestamp. You want to collate those bid events into a single location in real time to determine which user bid first. What should you do?
- A. Set up a MySQL database for each application server to write bid events into. Periodically query each of those distributed MySQL databases and update a master MySQL database with bid event information.
- B. Create a file on a shared file and have the application servers write all bid events to that file. Process the file with Apache Hadoop to identify which user bid first.
- C. Have each application server write the bid events to Google Cloud Pub/Sub as they occur. Use a pull
- D. Have each application server write the bid events to Cloud Pub/Sub as they occur. Push the events from Cloud Pub/Sub to a custom endpoint that writes the bid event information into Cloud SQL.
Answer: A
Explanation:
subscription to pull the bid events using Google Cloud Dataflow. Give the bid for each item to the user in
the bid event that is processed first.
NEW QUESTION # 217
Which of the following is not possible using primitive roles?
- A. Give a user viewer access to BigQuery and owner access to Google Compute Engine instances.
- B. Give a user access to view all datasets in a project, but not run queries on them.
- C. Give GroupA owner access and GroupB editor access for all datasets in a project.
- D. Give UserA owner access and UserB editor access for all datasets in a project.
Answer: B
Explanation:
Explanation
Primitive roles can be used to give owner, editor, or viewer access to a user or group, but they can't be used to separate data access permissions from job-running permissions.
Reference: https://cloud.google.com/bigquery/docs/access-control#primitive_iam_roles
NEW QUESTION # 218
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_released or all movies with tag=Comedy ordered by date_released. How should you avoid a combinatorial explosion in the number of indexes?
- A. Option A
- B. Option B.
- C. Option D
- D. Option C
Answer: A
NEW QUESTION # 219
You are a head of BI at a large enterprise company with multiple business units that each have different priorities and budgets. You use on-demand pricing for BigQuery with a quota of 2K concurrent on-demand slots per project. Users at your organization sometimes don't get slots to execute their query and you need to correct this. You'd like to avoid introducing new projects to your account.
What should you do?
- A. Switch to flat-rate pricing and establish a hierarchical priority model for your projects.
- B. Convert your batch BQ queries into interactive BQ queries.
- C. Increase the amount of concurrent slots per project at the Quotas page at the Cloud Console.
- D. Create an additional project to overcome the 2K on-demand per-project quota.
Answer: A
Explanation:
Explanation
Reference https://cloud.google.com/blog/products/gcp/busting-12-myths-about-bigquery
NEW QUESTION # 220
You are developing an application on Google Cloud that will automatically generate subject labels for users' blog posts. You are under competitive pressure to add this feature quickly, and you have no additional developer resources. No one on your team has experience with machine learning. What should you do?
- A. Build and train a text classification model using TensorFlow. Deploy the model using a Kubernetes Engine cluster. Call the model from your application and process the results as labels.
- B. Build and train a text classification model using TensorFlow. Deploy the model using Cloud Machine Learning Engine. Call the model from your application and process the results as labels.
- C. Call the Cloud Natural Language API from your application. Process the generated Entity Analysis as labels.
- D. Call the Cloud Natural Language API from your application. Process the generated Sentiment Analysis as labels.
Answer: D
NEW QUESTION # 221
You are building an ELT solution in BigQuery by using Dataform. You need to perform uniqueness and null value checks on your final tables. What should you do to efficiently integrate these checks into your pipeline?
- A. Create Dataplex data quality tasks.
- B. Build BigQuery user-defined functions (UDFs).
- C. Write a Spark-based stored procedure.
- D. Build Dataform assertions into your code
Answer: D
Explanation:
Dataform assertions are data quality tests that find rows that violate one or more rules specified in the query.
If the query returns any rows, the assertion fails. Dataform runs assertions every time it updates your SQL workflow and alerts you if any assertions fail. You can create assertions for all Dataform table types: tables, incremental tables, views, and materialized views. You can add built-in assertions to the config block of a table, such as nonNull and rowConditions, or create manual assertions with SQLX for advanced use cases.
Dataform automatically creates views in BigQuery that contain the results of compiled assertion queries, which you can inspect to debug failing assertions. Dataform assertions are an efficient way to integrate data quality checks into your ELT solution in BigQuery by using Dataform. References: Test tables with assertions
|Dataform | Google Cloud, Test data quality with assertions | Dataform, Data quality tests and documenting datasets | Dataform, Data quality testing with SQL assertions | Dataform
NEW QUESTION # 222
You have a data processing application that runs on Google Kubernetes Engine (GKE). Containers need to be launched with their latest available configurations from a container registry. Your GKE nodes need to have GPUs. local SSDs, and 8 Gbps bandwidth. You want to efficiently provision the data processing infrastructure and manage the deployment process. What should you do?
- A. Use Cloud Build to schedule a job using Terraform build to provision the infrastructure and launch with the most current container images.
- B. Use Compute Engi.no startup scriots to pull container Images, and use gloud commands to provision the infrastructure.
- C. Use GKE to autoscale containers, and use gloud commands to provision the infrastructure.
- D. Use Dataflow to provision the data pipeline, and use Cloud Scheduler to run the job.
Answer: A
Explanation:
https://cloud.google.com/architecture/managing-infrastructure-as-code
NEW QUESTION # 223
Your team is responsible for developing and maintaining ETLs in your company. One of your Dataflow jobs is failing because of some errors in the input data, and you need to improve reliability of the pipeline (incl. being able to reprocess all failing data).
What should you do?
- A. Add a filtering step to skip these types of errors in the future, extract erroneous rows from logs.
- B. Add a try... catch block to your DoFn that transforms the data, write erroneous rows to PubSub directly from the DoFn.
- C. Add a try... catch block to your DoFn that transforms the data, extract erroneous rows from logs.
- D. Add a try... catch block to your sideOutput to create a PCollection that can be stored to PubSub later.
Answer: B
NEW QUESTION # 224
You've migrated a Hadoop job from an on-prem cluster to dataproc and GCS. Your Spark job is a complicated analytical workload that consists of many shuffing operations and initial data are parquet files (on average
200-400 MB size each). You see some degradation in performance after the migration to Dataproc, so you'd like to optimize for it. You need to keep in mind that your organization is very cost-sensitive, so you'd like to continue using Dataproc on preemptibles (with 2 non-preemptible workers only) for this workload.
What should you do?
- A. Switch to TFRecords formats (appr. 200MB per file) instead of parquet files.
- B. Increase the size of your parquet files to ensure them to be 1 GB minimum.
- C. Switch from HDDs to SSDs, copy initial data from GCS to HDFS, run the Spark job and copy results back to GCS.
- D. Switch from HDDs to SSDs, override the preemptible VMs configuration to increase the boot disk size.
Answer: C
NEW QUESTION # 225
You are integrating one of your internal IT applications and Google BigQuery, so users can query BigQuery from the application's interface. You do not want individual users to authenticate to BigQuery and you do not want to give them access to the dataset. You need to securely access BigQuery from your IT application. What should you do?
- A. Create groups for your users and give those groups access to the dataset
- B. Integrate with a single sign-on (SSO) platform, and pass each user's credentials along with the query request
- C. Create a service account and grant dataset access to that account. Use the service account's private key to access the dataset
- D. Create a dummy user and grant dataset access to that user. Store the username and password for that user in a file on the files system, and use those credentials to access the BigQuery dataset
Answer: C
NEW QUESTION # 226
Which is not a valid reason for poor Cloud Bigtable performance?
- A. There are issues with the network connection.
- B. The Cloud Bigtable cluster has too many nodes.
- C. The workload isn't appropriate for Cloud Bigtable.
- D. The table's schema is not designed correctly.
Answer: B
Explanation:
Explanation
The Cloud Bigtable cluster doesn't have enough nodes. If your Cloud Bigtable cluster is overloaded, adding more nodes can improve performance. Use the monitoring tools to check whether the cluster is overloaded.
Reference: https://cloud.google.com/bigtable/docs/performance
NEW QUESTION # 227
You are designing a Dataflow pipeline for a batch processing job. You want to mitigate multiple zonal failures at job submission time. What should you do?
- A. Set the pipeline staging location as a regional Cloud Storage bucket.
- B. Submit duplicate pipelines in two different zones by using the -zone flag.
- C. Create an Eventarc trigger to resubmit the job in case of zonal failure when submitting the job.
- D. Specify a worker region by using the -region flag.
Answer: A
Explanation:
By specifying a worker region, you can run your Dataflow pipeline in a multi-zone or multi-region configuration, which provides higher availability and resilience in case ofzonal failures1. The -region flag allows you to specify the regional endpoint for your pipeline, which determines the location of the Dataflow service and the default location of the Compute Engine resources1. If you do not specify a zone by using the
-zone flag, Dataflow automatically selects a zone within the region for your job workers1. This option is recommended over submitting duplicate pipelines in two different zones, which would incur additional costs and complexity. Setting the pipeline staging location as a regional Cloud Storage bucket does not affect the availability of your pipeline, as the staging location only stores the pipeline code and dependencies2. Creating an Eventarc trigger to resubmit the job in case of zonal failure is not a reliable solution, as it depends on the availability of the Eventarc service and the zonal resources at the time of resubmission. References:
* 1: Pipeline troubleshooting and debugging | Cloud Dataflow | Google Cloud
* 3: Regional endpoints | Cloud Dataflow | Google Cloud
NEW QUESTION # 228
Your company currently runs a large on-premises cluster using Spark Hive and Hadoop Distributed File System (HDFS) in a colocation facility. The duster is designed to support peak usage on the system, however, many jobs are batch n nature, and usage of the cluster fluctuates quite dramatically.
Your company is eager to move to the cloud to reduce the overhead associated with on-premises infrastructure and maintenance and to benefit from the cost savings. They are also hoping to modernize their existing infrastructure to use more servers offerings m order to take advantage of the cloud Because of the tuning of their contract renewal with the colocation facility they have only 2 months for their initial migration How should you recommend they approach thee upcoming migration strategy so they can maximize their cost savings in the cloud will still executing the migration in time?
- A. Modernize the Spark workload for Dataflow and the Hive workload for BigQuery
- B. Migrate the workloads to Dataproc plus HOPS, modernize later
- C. Migrate the workloads to Dataproc plus Cloud Storage modernize later
- D. Migrate the Spark workload to Dataproc plus HDFS, and modernize the Hive workload for BigQuery
Answer: A
NEW QUESTION # 229
Your team is responsible for developing and maintaining ETLs in your company. One of your Dataflow jobs is failing because of some errors in the input data, and you need to improve reliability of the pipeline (incl. being able to reprocess all failing data).
What should you do?
- A. Add a filtering step to skip these types of errors in the future, extract erroneous rows from logs.
- B. Add a try... catch block to your DoFn that transforms the data, write erroneous rows to PubSub directly from the DoFn.
- C. Add a try... catch block to your DoFn that transforms the data, extract erroneous rows from logs.
- D. Add a try... catch block to your DoFn that transforms the data, use a sideOutput to create a PCollection that can be stored to PubSub later.
Answer: B
Explanation:
Topic 2, Flowlogistic Case Study
Company Overview
Flowlogistic is a leading logistics and supply chain provider. They help businesses throughout the world manage their resources and transport them to their final destination. The company has grown rapidly, expanding their offerings to include rail, truck, aircraft, and oceanic shipping.
Company Background
The company started as a regional trucking company, and then expanded into other logistics market. Because they have not updated their infrastructure, managing and tracking orders and shipments has become a bottleneck. To improve operations, Flowlogistic developed proprietary technology for tracking shipments in real time at the parcel level. However, they are unable to deploy it because their technology stack, based on Apache Kafka, cannot support the processing volume. In addition, Flowlogistic wants to further analyze their orders and shipments to determine how best to deploy their resources.
Solution Concept
Flowlogistic wants to implement two concepts using the cloud:
Use their proprietary technology in a real-time inventory-tracking system that indicates the location of their loads Perform analytics on all their orders and shipment logs, which contain both structured and unstructured data, to determine how best to deploy resources, which markets to expand info. They also want to use predictive analytics to learn earlier when a shipment will be delayed.
Existing Technical Environment
Flowlogistic architecture resides in a single data center:
Databases
8 physical servers in 2 clusters
SQL Server - user data, inventory, static data
3 physical servers
Cassandra - metadata, tracking messages
10 Kafka servers - tracking message aggregation and batch insert
Application servers - customer front end, middleware for order/customs
60 virtual machines across 20 physical servers
Tomcat - Java services
Nginx - static content
Batch servers
Storage appliances
iSCSI for virtual machine (VM) hosts
Fibre Channel storage area network (FC SAN) - SQL server storage
Network-attached storage (NAS) image storage, logs, backups
Apache Hadoop /Spark servers
Core Data Lake
Data analysis workloads
20 miscellaneous servers
Jenkins, monitoring, bastion hosts,
Business Requirements
Build a reliable and reproducible environment with scaled panty of production.
Aggregate data in a centralized Data Lake for analysis
Use historical data to perform predictive analytics on future shipments Accurately track every shipment worldwide using proprietary technology Improve business agility and speed of innovation through rapid provisioning of new resources Analyze and optimize architecture for performance in the cloud Migrate fully to the cloud if all other requirements are met Technical Requirements Handle both streaming and batch data Migrate existing Hadoop workloads Ensure architecture is scalable and elastic to meet the changing demands of the company.
Use managed services whenever possible
Encrypt data flight and at rest
Connect a VPN between the production data center and cloud environment
SEO Statement
We have grown so quickly that our inability to upgrade our infrastructure is really hampering further growth and efficiency. We are efficient at moving shipments around the world, but we are inefficient at moving data around.
We need to organize our information so we can more easily understand where our customers are and what they are shipping.
CTO Statement
IT has never been a priority for us, so as our data has grown, we have not invested enough in our technology. I have a good staff to manage IT, but they are so busy managing our infrastructure that I cannot get them to do the things that really matter, such as organizing our data, building the analytics, and figuring out how to implement the CFO' s tracking technology.
CFO Statement
Part of our competitive advantage is that we penalize ourselves for late shipments and deliveries. Knowing where out shipments are at all times has a direct correlation to our bottom line and profitability. Additionally, I don't want to commit capital to building out a server environment.
NEW QUESTION # 230
Which action can a Cloud Dataproc Viewer perform?
- A. Submit a job.
- B. Create a cluster.
- C. List the jobs.
- D. Delete a cluster.
Answer: C
Explanation:
A Cloud Dataproc Viewer is limited in its actions based on its role. A viewer can only list clusters, get cluster details, list jobs, get job details, list operations, and get operation details.
NEW QUESTION # 231
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 disk size per worker
- B. The zone
- C. The maximum number of workers
- D. The number of workers
Answer: B
NEW QUESTION # 232
You're training a model to predict housing prices based on an available dataset with real estate properties.
Your plan is to train a fully connected neural net, and you've discovered that the dataset contains latitude and longitude of the property. Real estate professionals have told you that the location of the property is highly influential on price, so you'd like to engineer a feature that incorporates this physical dependency.
What should you do?
- A. Create a feature cross of latitude and longitude, bucketize it at the minute level and use L2 regularization during optimization.
- B. Provide latitude and longitude as input vectors to your neural net.
- C. Create a feature cross of latitude and longitude, bucketize at the minute level and use L1 regularization during optimization.
- D. Create a numeric column from a feature cross of latitude and longitude.
Answer: C
Explanation:
Use L1 regularization when you need to assign greater importance to more influential features. It shrinks less important feature to 0.
L2 regularization performs better when all input features influence the output & all with the weights are of equal size.
NEW QUESTION # 233
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