[Dec 31, 2024] Valid Professional-Data-Engineer Test Answers & Google Professional-Data-Engineer Exam PDF
Realistic Professional-Data-Engineer Exam Dumps with Accurate & Updated Questions
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.
To become a Google Professional-Data-Engineer, candidates need to pass the certification exam. Professional-Data-Engineer exam consists of multiple-choice and scenario-based questions that assess a candidate's ability to design, build, and manage data processing systems on the Google Cloud Platform. Professional-Data-Engineer exam can be taken online or in-person at a proctored testing center. Candidates have two hours to complete the exam, and they must score at least 70% to pass.
Preparing for the exam requires a combination of hands-on experience with Google Cloud Platform, as well as studying relevant documentation and training materials. Google offers a variety of resources for exam preparation, including online courses, hands-on labs, and practice exams.
NEW QUESTION # 199
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 a service account and grant dataset access to that account. Use the service account's private key to access the dataset
- 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. Integrate with a single sign-on (SSO) platform, and pass each user's credentials along with the query request
- D. Create groups for your users and give those groups access to the dataset
Answer: A
NEW QUESTION # 200
Your company is loading comma-separated values (CSV) files into Google BigQuery. The data is fully imported successfully; however, the imported data is not matching byte-to-byte to the source file. What is the most likely cause of this problem?
- A. The CSV data loaded in BigQuery is not using BigQuery's default encoding.
- B. The CSV data has not gone through an ETL phase before loading into BigQuery.
- C. The CSV data loaded in BigQuery is not flagged as CSV.
- D. The CSV data has invalid rows that were skipped on import.
Answer: D
NEW QUESTION # 201
Your company produces 20,000 files every hour. Each data file is formatted as a comma separated values
(CSV) file that is less than 4 KB. All files must be ingested on Google Cloud Platform before they can be
processed. Your company site has a 200 ms latency to Google Cloud, and your Internet connection
bandwidth is limited as 50 Mbps. You currently deploy a secure FTP (SFTP) server on a virtual machine in
Google Compute Engine as the data ingestion point. A local SFTP client runs on a dedicated machine to
transmit the CSV files as is. The goal is to make reports with data from the previous day available to the
executives by 10:00 a.m. each day. This design is barely able to keep up with the current volume, even
though the bandwidth utilization is rather low.
You are told that due to seasonality, your company expects the number of files to double for the next three
months. Which two actions should you take? (Choose two.)
- A. Redesign the data ingestion process to use gsutil tool to send the CSV files to a storage bucket in
parallel. - B. Create an S3-compatible storage endpoint in your network, and use Google Cloud Storage Transfer
Service to transfer on-premices data to the designated storage bucket. - C. Introduce data compression for each file to increase the rate file of file transfer.
- D. Assemble 1,000 files into a tape archive (TAR) file. Transmit the TAR files instead, and disassemble
the CSV files in the cloud upon receiving them. - E. Contact your internet service provider (ISP) to increase your maximum bandwidth to at least 100 Mbps.
Answer: A,B
NEW QUESTION # 202
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 D
- B. Option C
- C. Option A
- D. Option B.
Answer: C
NEW QUESTION # 203
You are selecting services to write and transform JSON messages from Cloud Pub/Sub to BigQuery for a data pipeline on Google Cloud. You want to minimize service costs. You also want to monitor and accommodate input data volume that will vary in size with minimal manual intervention. What should you do?
- A. Use Cloud Dataflow to run your transformations. Monitor the job system lag with Stackdriver. Use the default autoscaling setting for worker instances.
- B. Use Cloud Dataproc to run your transformations. Use the diagnose command to generate an operational output archive. Locate the bottleneck and adjust cluster resources.
- C. Use Cloud Dataproc to run your transformations. Monitor CPU utilization for the cluster. Resize the number of worker nodes in your cluster via the command line.
- D. Use Cloud Dataflow to run your transformations. Monitor the total execution time for a sampling of jobs.
Configure the job to use non-default Compute Engine machine types when needed.
Answer: A
Explanation:
Dataflow is good with autoscaling and stackdriver to monitor CPU and Storage.
NEW QUESTION # 204
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. Users are running too many concurrent queries in the system
- C. The [myproject:mydataset.mytable] table has too many partitions
- D. Either the state or the city columns in the [myproject:mydataset.mytable]table have too many NULL values
Answer: B
NEW QUESTION # 205
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 a service account and grant dataset access to that account. Use the service account's private key to access the dataset
- 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. Integrate with a single sign-on (SSO) platform, and pass each user's credentials along with the query request
- D. Create groups for your users and give those groups access to the dataset
Answer: A
Explanation:
Explanation
NEW QUESTION # 206
Your company is currently setting up data pipelines for their campaign. For all the Google Cloud Pub/Sub
streaming data, one of the important business requirements is to be able to periodically identify the inputs and their timings during their campaign. Engineers have decided to use windowing and transformation in Google Cloud Dataflow for this purpose. However, when testing this feature, they find that the Cloud Dataflow job fails for the all streaming insert. What is the most likely cause of this problem?
- A. They have not assigned the timestamp, which causes the job to fail
- B. They have not applied a global windowing function, which causes the job to fail when the pipeline is
created - C. They have not applied a non-global windowing function, which causes the job to fail when the pipeline is created
- D. They have not set the triggers to accommodate the data coming in late, which causes the job to fail
Answer: B
NEW QUESTION # 207
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 export, compress, and store the data in Cloud Storage.
- B. Organize your data in separate tables for each month, and duplicate your data on a separate dataset in BigQuery.
- C. Organize your data in separate tables for each month, and use snapshot decorators to restore the table to a time prior to the corruption.
- D. Organize your data in a single table, export, and compress and store the BigQuery data in Cloud Storage.
Answer: A
NEW QUESTION # 208
You have a Standard Tier Memorystore for Redis instance deployed in a production environment. You need to simulate a Redis instance failover in the most accurate disaster recovery situation, and ensure that the failover has no impact on production dat a. What should you do?
- A. Initiate a manual tailover by using the limited-data-loss data protection mode to the Memorystore for Redis instance in the production environment.
- B. Create a Standard Tier Memorystore for Redis instance in the development environment. Initiate a manual failover by using the limited-data-loss data protection mode.
- C. Increase one replica to Redis instance in production environment. Initiate a manual failover by using the force-data-loss data protection mode.
- D. Create a Standard Tier Memorystore for Redis instance in a development environment. Initiate a manual failover by using the force-data-loss data protection mode.
Answer: B
Explanation:
To simulate a Redis instance failover in a production-like environment without impacting production data, the best approach is to use a development environment. Here's why option D is the best choice:
Standard Tier Memorystore for Redis:
The Standard Tier provides high availability and automatic failover capabilities. It's suitable for testing failover scenarios in a controlled environment.
Development Environment:
Using a development environment ensures that any potential data loss or impact from the failover simulation does not affect production data, maintaining the integrity and availability of the production system.
Limited-Data-Loss Mode:
The limited-data-loss mode for manual failover ensures that data loss is minimized during the failover process, making it a realistic simulation of a production failover scenario.
Steps to Implement:
Create a Development Environment:
Set up a development environment with a Standard Tier Memorystore for Redis instance that mirrors the configuration of your production instance.
Initiate Manual Failover:
Initiate a manual failover using the limited-data-loss data protection mode to simulate a failover scenario:
gcloud redis instances failover INSTANCE_ID --data-protection-mode=limited-data-loss Verify Failover:
Monitor and verify the failover process to ensure it behaves as expected, simulating the disaster recovery scenario accurately.
Reference:
Memorystore for Redis Documentation
Manual Failover in Memorystore
NEW QUESTION # 209
You are designing storage for two relational tables that are part of a 10-TB database on Google Cloud. You want to support transactions that scale horizontally. You also want to optimize data for range queries on nonkey columns. What should you do?
- A. Use Cloud SQL for storage. Add secondary indexes to support query patterns.
- B. Use Cloud SQL for storage. Use Cloud Dataflow to transform data to support query patterns.
- C. Use Cloud Spanner for storage. Add secondary indexes to support query patterns.
- D. Use Cloud Spanner for storage. Use Cloud Dataflow to transform data to support query patterns.
Answer: D
Explanation:
Reference: https://cloud.google.com/solutions/data-lifecycle-cloud-platform
NEW QUESTION # 210
Which of the following IAM roles does your Compute Engine account require to be able to run pipeline jobs?
- A. dataflow.developer
- B. dataflow.compute
- C. dataflow.worker
- D. dataflow.viewer
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 # 211
Which of the following IAM roles does your Compute Engine account require to be able to run pipeline jobs?
- A. dataflow.developer
- B. dataflow.compute
- C. dataflow.worker
- D. dataflow.viewer
Answer: C
Explanation:
Explanation
The dataflow.worker role provides the permissions necessary for a Compute Engine service account to execute work units for a Dataflow pipeline Reference: https://cloud.google.com/dataflow/access-control
NEW QUESTION # 212
You work for an economic consulting firm that helps companies identify economic trends as they happen. As part of your analysis, you use Google BigQuery to correlate customer data with the average prices of the 100 most common goods sold, including bread, gasoline, milk, and others. The average prices of these goods are updated every 30 minutes. You want to make sure this data stays up to date so you can combine it with other data in BigQuery as cheaply as possible. What should you do?
- A. Load the data every 30 minutes into a new partitioned table in BigQuery.
- B. Store the data in a file in a regional Google Cloud Storage bucket. Use Cloud Dataflow to query BigQuery and combine the data programmatically with the data stored in Google Cloud Storage.
- C. Store the data in Google Cloud Datastore. Use Google Cloud Dataflow to query BigQuery and combine the data programmatically with the data stored in Cloud Datastore
- D. Store and update the data in a regional Google Cloud Storage bucket and create a federated data source in BigQuery
Answer: A
NEW QUESTION # 213
You work for an economic consulting firm that helps companies identify economic trends as they happen. As part of your analysis, you use Google BigQuery to correlate customer data with the average prices of the 100 most common goods sold, including bread, gasoline, milk, and others. The average prices of these goods are updated every 30 minutes. You want to make sure this data stays up to date so you can combine it with other data in BigQuery as cheaply as possible. What should you do?
- A. Store the data in Google Cloud Datastore. Use Google Cloud Dataflow to query BigQuery and combine the data programmatically with the data stored in Cloud Datastore
- B. Store the data in a file in a regional Google Cloud Storage bucket. Use Cloud Dataflow to query BigQuery and combine the data programmatically with the data stored in Google Cloud Storage.
- C. Load the data every 30 minutes into a new partitioned table in BigQuery.
- D. Store and update the data in a regional Google Cloud Storage bucket and create a federated data source in BigQuery
Answer: A
Explanation:
Explanation
NEW QUESTION # 214
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