[May 23, 2024] Professional-Data-Engineer Exam Dumps - Try Best Professional-Data-Engineer Exam Questions - PremiumVCEDump
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What is the duration, language, and format of Google Professional Data Engineer Exam
- Passing score: 80%
- Length of Examination: 120 minutes
- Cost: $200
- Format: Multiple choices, multiple answers
Google Professional-Data-Engineer exam is a certification exam offered by Google Cloud Platform for data professionals who want to demonstrate their expertise in designing, building, and managing data processing systems on Google Cloud Platform. It is a highly valued certification in the industry and is especially relevant for those looking to work with Big Data. Professional-Data-Engineer exam tests a candidate's knowledge of various data engineering tools and technologies, and passing the exam demonstrates that the candidate has the skills and knowledge to design and implement data solutions on Google Cloud Platform.
NEW QUESTION # 108
When running a pipeline that has a BigQuery source, on your local machine, you continue to get permission denied errors. What could be the reason for that?
- A. Pipelines cannot be run locally
- B. You are missing gcloud on your machine
- C. Your gcloud does not have access to the BigQuery resources
- D. BigQuery cannot be accessed from local machines
Answer: C
Explanation:
When reading from a Dataflow source or writing to a Dataflow sink using DirectPipelineRunner, the Cloud Platform account that you configured with the gcloud executable will need access to the corresponding source/sink Reference: https://cloud.google.com/dataflow/java- sdk/JavaDoc/com/google/cloud/dataflow/sdk/runners/DirectPipelineRunner
NEW QUESTION # 109
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. Integrate with a single sign-on (SSO) platform, and pass each user's credentials along with the query request
- B. 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
- 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 groups for your users and give those groups access to the dataset
Answer: C
NEW QUESTION # 110
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. BigQuery Data Transfer Service lor the migration, Pub/Sub and Dataproc for the real-time updates
- C. gsutil for both the migration and the real-time updates
- D. Storage Transfer Service for the migration, Pub/Sub and Cloud Data Fusion for the real-time updates
Answer: A
NEW QUESTION # 111
You are on the data governance team and are implementing security requirements to deploy resources. You need to ensure that resources are limited to only the europe-west 3 region You want to follow Google-recommended practices What should you do?
- A. Create a Cloud Function to monitor all resources created and automatically destroy the ones created outside the europe-west3 region.
- B. Set the constraints/gcp. resourceLocations organization policy constraint to in:eu-locations.
- C. Deploy resources with Terraform and implement a variable validation rule to ensure that the region is set to the europe-west3 region for all resources.
- D. Set the constraints/gcp. resourceLocations organization policy constraint to in: europe-west3-locations.
Answer: D
Explanation:
To ensure that resources are limited to only the europe-west3 region, you should set the organization policy constraint constraints/gcp.resourceLocations to in:europe-west3-locations. This policy restricts the deployment of resources to the specified locations, which in this case is the europe-west3 region. By setting this policy, you enforce location compliance across your Google Cloud resources, aligning with the best practices for data governance and regulatory compliance.
References:
* Professional Data Engineer Certification Exam Guide | Learn - Google Cloud1.
* Preparing for Google Cloud Certification: Cloud Data Engineer2.
* Professional Data Engineer Certification | Learn | Google Cloud3.
3: Professional Data Engineer Certification | Learn | Google Cloud 2: Preparing for Google Cloud Certification: Cloud Data Engineer 1: Professional Data Engineer Certification Exam Guide | Learn - Google Cloud
NEW QUESTION # 112
Case Study: 3,
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.
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 number of workers
- B. The zone
- C. The disk size per worker
- D. The maximum number of workers
Answer: B
NEW QUESTION # 113
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. Refresh your browser tab showing the visualizations.
- B. Disable caching in BigQuery by editing table details.
- C. Clear your browser history for the past hour then reload the tab showing the virtualizations.
- D. Disable caching by editing the report settings.
Answer: D
NEW QUESTION # 114
Dataproc clusters contain many configuration files. To update these files, you will need to use the --properties option. The format for the option is: file_prefix:property=_____.
- A. null
- B. details
- C. value
- D. id
Answer: C
Explanation:
Explanation
To make updating files and properties easy, the --properties command uses a special format to specify the configuration file and the property and value within the file that should be updated. The formatting is as follows: file_prefix:property=value.
Reference: https://cloud.google.com/dataproc/docs/concepts/cluster-properties#formatting
NEW QUESTION # 115
Does Dataflow process batch data pipelines or streaming data pipelines?
- A. Both Batch and Streaming Data Pipelines
- B. None of the above
- C. Only Batch Data Pipelines
- D. Only Streaming Data Pipelines
Answer: A
Explanation:
Explanation
Dataflow is a unified processing model, and can execute both streaming and batch data pipelines Reference: https://cloud.google.com/dataflow/
NEW QUESTION # 116
Suppose you have a table that includes a nested column called "city" inside a column called "person", but when you try to submit the following query in BigQuery, it gives you an error.
SELECT person FROM `project1.example.table1` WHERE city = "London"
How would you correct the error?
- A. Add ", UNNEST(person)" before the WHERE clause.
- B. Add ", UNNEST(city)" before the WHERE clause.
- C. Change "person" to "city.person".
- D. Change "person" to "person.city".
Answer: A
Explanation:
To access the person.city column, you need to "UNNEST(person)" and JOIN it to table1 using a comma.
Reference:
https://cloud.google.com/bigquery/docs/reference/standard-sql/migrating-from-legacy-sql#nested_repeated_resu
NEW QUESTION # 117
You are implementing several batch jobs that must be executed on a schedule. These jobs have many interdependent steps that must be executed in a specific order. Portions of the jobs involve executing shell scripts, running Hadoop jobs, and running queries in BigQuery. The jobs are expected to run for many minutes up to several hours. If the steps fail, they must be retried a fixed number of times. Which service should you use to manage the execution of these jobs?
- A. Cloud Functions
- B. Cloud Dataflow
- C. Cloud Composer
- D. Cloud Scheduler
Answer: D
NEW QUESTION # 118
Which is not a valid reason for poor Cloud Bigtable performance?
- A. The workload isn't appropriate for Cloud Bigtable.
- B. The Cloud Bigtable cluster has too many nodes.
- C. The table's schema is not designed correctly.
- D. There are issues with the network connection.
Answer: B
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 # 119
You are updating the code for a subscriber to a Put/Sub feed. You are concerned that upon deployment the subscriber may erroneously acknowledge messages, leading to message loss. You subscriber is not set up to retain acknowledged messages. What should you do to ensure that you can recover from errors after deployment?
- A. Enable dead-lettering on the Pub/Sub topic to capture messages that aren't successful acknowledged if an error occurs after deployment, re-deliver any messages captured by the dead-letter queue
- B. Set up the Pub/Sub emulator on your local machine Validate the behavior of your new subscriber togs before deploying it to production
- C. Use Cloud Build for your deployment if an error occurs after deployment, use a Seek operation to locate a tmestamp logged by Cloud Build at the start of the deployment
- D. Create a Pub/Sub snapshot before deploying new subscriber code. Use a Seek operation to re-deliver messages that became available after the snapshot was created
Answer: D
NEW QUESTION # 120
Your company is migrating their 30-node Apache Hadoop cluster to the cloud. They want to re-use Hadoop jobs they have already created and minimize the management of the cluster as much as possible. They also want to be able to persist data beyond the life of the cluster. What should you do?
- A. Create a Cloud Dataproc cluster that uses the Google Cloud Storage connector.
- B. Create a Google Cloud Dataflow job to process the data.
- C. Create a Hadoop cluster on Google Compute Engine that uses Local SSD disks.
- D. Create a Hadoop cluster on Google Compute Engine that uses persistent disks.
- E. Create a Google Cloud Dataproc cluster that uses persistent disks for HDFS.
Answer: A
Explanation:
Dataproc is used to migrate Hadoop and Spark jobs on GCP. Dataproc with GCS connected through Google Cloud Storage connector helps store data after the life of the cluster. When the job is high I/O intensive, then we need to create a small persistent disk.
NEW QUESTION # 121
You use BigQuery as your centralized analytics platform. New data is loaded every day, and an ETL pipeline modifies the original data and prepares it for the final users. This ETL pipeline is regularly modified and can generate errors, but sometimes the errors are detected only after 2 weeks. You need to provide a method to recover from these errors, and your backups should be optimized for storage costs. How should you organize your data in BigQuery and store your backups?
- A. Organize your data in separate tables for each month, and use snapshot decorators to restore the table to a time prior to the corruption.
- B. Organize your data in separate tables for each month, and export, compress, and store the data in Cloud Storage.
- C. Organize your data in a single table, export, and compress and store the BigQuery data in Cloud Storage.
- D. Organize your data in separate tables for each month, and duplicate your data on a separate dataset in BigQuery.
Answer: B
NEW QUESTION # 122
Suppose you have a table that includes a nested column called "city" inside a column called "person", but when you try to submit the following query in BigQuery, it gives you an error.
SELECT person FROM `project1.example.table1` WHERE city = "London"
How would you correct the error?
- A. Add ", UNNEST(person)" before the WHERE clause.
- B. Add ", UNNEST(city)" before the WHERE clause.
- C. Change "person" to "city.person".
- D. Change "person" to "person.city".
Answer: A
Explanation:
Explanation
To access the person.city column, you need to "UNNEST(person)" and JOIN it to table1 using a comma.
Reference:
https://cloud.google.com/bigquery/docs/reference/standard-sql/migrating-from-legacy-sql#nested_repeated_resu
NEW QUESTION # 123
The Dataflow SDKs have been recently transitioned into which Apache service?
- A. Apache Hadoop
- B. Apache Beam
- C. Apache Kafka
- D. Apache Spark
Answer: B
Explanation:
Dataflow SDKs are being transitioned to Apache Beam, as per the latest Google directive
https://cloud.google.com/dataflow/docs/
NEW QUESTION # 124
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