2025年最新のProfessional-Data-Engineer試験解答最新版PassTest Professional-Data-EngineerのPDF問題集をダウンロードせよ（375問題と解答)
無料2025年最新のGoogle Cloud Certified Professional-Data-Engineer問題集を提供しております！PassTest
質問 # 121
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.
Given the record streams MJTelco is interested in ingesting per day, they are concerned about the cost of Google BigQuery increasing. MJTelco asks you to provide a design solution. They require a single large data table called tracking_table. Additionally, they want to minimize the cost of daily queries while performing fine-grained analysis of each day's events. They also want to use streaming ingestion. What should you do?
- A. Create sharded tables for each day following the pattern tracking_table_YYYYMMDD.
- B. Create a partitioned table called tracking_table and include a TIMESTAMP column.
- C. Create a table called tracking_table with a TIMESTAMP column to represent the day.
- D. Create a table called tracking_table and include a DATE column.
正解：B
質問 # 122
If you're running a performance test that depends upon Cloud Bigtable, all the choices except one below are recommended steps. Which is NOT a recommended step to follow?
- A. Run your test for at least 10 minutes.
- B. Use at least 300 GB of data.
- C. Do not use a production instance.
- D. Before you test, run a heavy pre-test for several minutes.
正解：C
解説：
If you're running a performance test that depends upon Cloud Bigtable, be sure to follow these steps as you plan and execute your test:
Use a production instance. A development instance will not give you an accurate sense of how a production instance performs under load.
Use at least 300 GB of data. Cloud Bigtable performs best with 1 TB or more of data. However, 300 GB of data is enough to provide reasonable results in a performance test on a 3-node cluster. On larger clusters, use 100 GB of data per node.
Before you test, run a heavy pre-test for several minutes. This step gives Cloud Bigtable a chance to balance data across your nodes based on the access patterns it observes. Run your test for at least 10 minutes. This step lets Cloud Bigtable further optimize your data, and it helps ensure that you will test reads from disk as well as cached reads from memory.
Reference: https://cloud.google.com/bigtable/docs/performance
質問 # 123
Cloud Dataproc is a managed Apache Hadoop and Apache _____ service.
- A. Blaze
- B. Fire
- C. Ignite
- D. Spark
正解：D
解説：
Cloud Dataproc is a managed Apache Spark and Apache Hadoop service that lets you use open source data tools for batch processing, querying, streaming, and machine learning.
質問 # 124
Business owners at your company have given you a database of bank transactions. Each row contains the
user ID, transaction type, transaction location, and transaction amount. They ask you to investigate what
type of machine learning can be applied to the data. Which three machine learning applications can you
use? (Choose three.)
- A. Supervised learning to determine which transactions are most likely to be fraudulent.
- B. Reinforcement learning to predict the location of a transaction.
- C. Unsupervised learning to determine which transactions are most likely to be fraudulent.
- D. Clustering to divide the transactions into N categories based on feature similarity.
- E. Supervised learning to predict the location of a transaction.
- F. Unsupervised learning to predict the location of a transaction.
正解：B、C、D
質問 # 125
You want to encrypt the customer data stored in BigQuery. You need to implement for-user crypto-deletion on data stored in your tables. You want to adopt native features in Google Cloud to avoid custom solutions. What should you do?
- A. Implement Authenticated Encryption with Associated Data (AEAD) BigQuery functions while storing your data in BigQuery.
- B. Create a customer-managed encryption key (CMEK) in Cloud KMS. Associate the key to the table while creating the table.
- C. Create a customer-managed encryption key (CMEK) in Cloud KMS. Use the key to encrypt data before storing in BigQuery.
- D. Encrypt your data during ingestion by using a cryptographic library supported by your ETL pipeline.
正解：B
解説：
To implement for-user crypto-deletion and ensure that customer data stored in BigQuery is encrypted, using native Google Cloud features, the best approach is to use Customer-Managed Encryption Keys (CMEK) with Cloud Key Management Service (KMS). Here's why:
Customer-Managed Encryption Keys (CMEK):
CMEK allows you to manage your own encryption keys using Cloud KMS. These keys provide additional control over data access and encryption management.
Associating a CMEK with a BigQuery table ensures that data is encrypted with a key you manage.
For-User Crypto-Deletion:
For-user crypto-deletion can be achieved by disabling or destroying the CMEK. Once the key is disabled or destroyed, the data encrypted with that key cannot be decrypted, effectively rendering it unreadable.
Native Integration:
Using CMEK with BigQuery is a native feature, avoiding the need for custom encryption solutions. This simplifies the management and implementation of encryption and decryption processes.
Steps to Implement:
Create a CMEK in Cloud KMS:
Set up a new customer-managed encryption key in Cloud KMS.
Associate the CMEK with BigQuery Tables:
When creating a new table in BigQuery, specify the CMEK to be used for encryption.
This can be done through the BigQuery console, CLI, or API.
Reference:
BigQuery and CMEK
Cloud KMS Documentation
Encrypting Data in BigQuery
質問 # 126
You are running a streaming pipeline with Dataflow and are using hopping windows to group the data as the data arrives. You noticed that some data is arriving late but is not being marked as late data, which is resulting in inaccurate aggregations downstream. You need to find a solution that allows you to capture the late data in the appropriate window. What should you do?
- A. Change your windowing function to tumbling windows to avoid overlapping window periods.
- B. Expand your hopping window so that the late data has more time to arrive within the grouping.
- C. Use watermarks to define the expected data arrival window Allow late data as it arrives.
- D. Change your windowing function to session windows to define your windows based on certain activity.
正解：C
解説：
Watermarks are a way of tracking the progress of time in a streaming pipeline. They are used to determine when a window can be closed and the results emitted. Watermarks can be either event-time based or processing-time based. Event-time watermarks track the progress of time based on the timestamps of the data elements, while processing-time watermarks track the progress of time based on the system clock. Event-time watermarks are more accurate, but they require the data source to provide reliable timestamps. Processing-time watermarks are simpler, but they can be affected by system delays or backlogs.
By using watermarks, you can define the expected data arrival window for each windowing function. You can also specify how to handle late data, which is data that arrives after the watermark has passed. You can either discard late data, or allow late data and update the results as new data arrives. Allowing late data requires you to use triggers to control when the results are emitted.
In this case, using watermarks and allowing late data is the best solution to capture the late data in the appropriate window. Changing the windowing function to session windows or tumbling windows will not solve the problem of late data, as they still rely on watermarks to determine when to close the windows. Expanding the hopping window might reduce the amount of late data, but it will also change the semantics of the windowing function and the results.
Reference:
Streaming pipelines | Cloud Dataflow | Google Cloud
Windowing | Apache Beam
質問 # 127
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 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 use snapshot decorators to restore the table to a time prior to the corruption.
正解：D
解説：
Explanation
質問 # 128
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.
正解：D
解説：
Reference https://support.google.com/datastudio/answer/7020039?hl=en
質問 # 129
You need to copy millions of sensitive patient records from a relational database to BigQuery. The total size of the database is 10 TB. You need to design a solution that is secure and time-efficient. What should you do?
- A. Export the records from the database as an Avro file. Create a public URL for the Avro file, and then use Storage Transfer Service to move the file to Cloud Storage. Load the Avro file into BigQuery using the BigQuery web UI in the GCP Console.
- B. Export the records from the database into a CSV file. Create a public URL for the CSV file, and then use Storage Transfer Service to move the file to Cloud Storage. Load the CSV file into BigQuery using the BigQuery web UI in the GCP Console.
- C. Export the records from the database as an Avro file. Upload the file to GCS using gsutil, and then load the Avro file into BigQuery using the BigQuery web UI in the GCP Console.
- D. Export the records from the database as an Avro file. Copy the file onto a Transfer Appliance and send it to Google, and then load the Avro file into BigQuery using the BigQuery web UI in the GCP Console.
正解：C
解説：
Google recommends that enterprises use Transfer Appliance in cases where it would take them over a week to upload data to the cloud via the internet, or when an enterprise needs to migrate over 60 TB of data.
質問 # 130
Your weather app queries a database every 15 minutes to get the current temperature. The frontend is powered by Google App Engine and server millions of users. How should you design the frontend to respond to a database failure?
- A. Reduce the query frequency to once every hour until the database comes back online.
- B. Retry the query every second until it comes back online to minimize staleness of data.
- C. Retry the query with exponential backoff, up to a cap of 15 minutes.
- D. Issue a command to restart the database servers.
正解：C
解説：
Explanation
https://cloud.google.com/sql/docs/mysql/manage-connections#backoff
質問 # 131
Which of these statements about BigQuery caching is true?
- A. Query results are cached even if you specify a destination table.
- B. BigQuery caches query results for 48 hours.
- C. By default, a query's results are not cached.
- D. There is no charge for a query that retrieves its results from cache.
正解：D
解説：
When query results are retrieved from a cached results table, you are not charged for the query.
BigQuery caches query results for 24 hours, not 48 hours.
Query results are not cached if you specify a destination table.
A query's results are always cached except under certain conditions, such as if you specify a destination table.
質問 # 132
Your neural network model is taking days to train. You want to increase the training speed. What can you
do?
- A. Increase the number of layers in your neural network.
- B. Subsample your training dataset.
- C. Increase the number of input features to your model.
- D. Subsample your test dataset.
正解：A
解説：
Explanation/Reference:
Reference: https://towardsdatascience.com/how-to-increase-the-accuracy-of-a-neural-network-
9f5d1c6f407d
質問 # 133
Which is not a valid reason for poor Cloud Bigtable performance?
- A. There are issues with the network connection.
- B. The workload isn't appropriate for Cloud Bigtable.
- C. The table's schema is not designed correctly.
- D. The Cloud Bigtable cluster has too many nodes.
正解：D
解説：
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
質問 # 134
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. Users are running too many concurrent queries in the system
- B. Either the state or the city columns in the [myproject:mydataset.mytable] table have too many NULL values
- C. The [myproject:mydataset.mytable] table has too many partitions
- D. Most rows in the [myproject:mydataset.mytable] table have the same value in the country column, causing data skew
正解：A
質問 # 135
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 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. Export the data into a Google Sheet for virtualization.
- B. Create a view on the table to present to the virtualization tool.
- C. Create an additional table with only the necessary columns.
- D. Create identity and access management (IAM) roles on the appropriate columns, so only they appear in a query.
正解：B
質問 # 136
You are running your BigQuery project in the on-demand billing model and are executing a change data capture (CDC) process that ingests dat a. The CDC process loads 1 GB of data every 10 minutes into a temporary table, and then performs a merge into a 10 TB target table. This process is very scan intensive and you want to explore options to enable a predictable cost model. You need to create a BigQuery reservation based on utilization information gathered from BigQuery Monitoring and apply the reservation to the CDC process. What should you do?
- A. Create a BigQuery reservation for the project.
- B. Create a BigQuery reservation for the job.
- C. Create a BigQuery reservation for the service account running the job.
- D. Create a BigQuery reservation for the dataset.
正解：A
解説：
https://cloud.google.com/blog/products/data-analytics/manage-bigquery-costs-with-custom-quotas.
Here's why creating a BigQuery reservation for the project is the most suitable solution:
Project-Level Reservation: BigQuery reservations are applied at the project level. This means that the reserved slots (processing capacity) are shared across all jobs and queries running within that project. Since your CDC process is a significant contributor to your BigQuery usage, reserving slots for the entire project ensures that your CDC process always has access to the necessary resources, regardless of other activities in the project.
Predictable Cost Model: Reservations provide a fixed, predictable cost model. Instead of paying the on-demand price for each query, you pay a fixed monthly fee for the reserved slots. This eliminates the variability of costs associated with on-demand billing, making it easier to budget and forecast your BigQuery expenses.
BigQuery Monitoring: You can use BigQuery Monitoring to analyze the historical usage patterns of your CDC process and other queries within your project. This information helps you determine the appropriate amount of slots to reserve, ensuring that you have enough capacity to handle your workload while optimizing costs.
Why other options are not suitable:
A . Create a BigQuery reservation for the job: BigQuery does not support reservations at the individual job level. Reservations are applied at the project or assignment level.
B . Create a BigQuery reservation for the service account running the job: While you can create reservations for assignments (groups of users or service accounts), it's less efficient than a project-level reservation in this scenario. A project-level reservation covers all jobs within the project, regardless of the service account used.
C . Create a BigQuery reservation for the dataset: BigQuery does not support reservations at the dataset level.
By creating a BigQuery reservation for your project based on your utilization analysis, you can achieve a predictable cost model while ensuring that your CDC process and other queries have the necessary resources to run smoothly.
質問 # 137
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 longtitude 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 numeric column from a feature cross of latitude and longtitude.
- B. Provide latitude and longtitude as input vectors to your neural net.
- C. Create a feature cross of latitude and longtitude, bucketize at the minute level and use L1 regularization during optimization.
- D. Create a feature cross of latitude and longtitude, bucketize it at the minute level and use L2 regularization during optimization.
正解：C
解説：
Reference https://cloud.google.com/bigquery/docs/gis-dataa
質問 # 138
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 data. What should you do?
- A. 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. - 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 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.
- 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.
正解：C
質問 # 139
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.
Given the record streams MJTelco is interested in ingesting per day, they are concerned about the cost of Google BigQuery increasing. MJTelco asks you to provide a design solution. They require a single large data table called tracking_table. Additionally, they want to minimize the cost of daily queries while performing fine-grained analysis of each day's events. They also want to use streaming ingestion. What should you do?
- A. Create sharded tables for each day following the pattern tracking_table_YYYYMMDD.
- B. Create a partitioned table called tracking_table and include a TIMESTAMP column.
- C. Create a table called tracking_table with a TIMESTAMP column to represent the day.
- D. Create a table called tracking_table and include a DATE column.
正解：B
質問 # 140
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. The [myproject:mydataset.mytable] table has too many partitions
- D. Users are running too many concurrent queries in the system
正解：A
質問 # 141
A shipping company has live package-tracking data that is sent to an Apache Kafka stream in real time.
This is then loaded into BigQuery. Analysts in your company want to query the tracking data in BigQuery to analyze geospatial trends in the lifecycle of a package. The table was originally created with ingest-date partitioning. Over time, the query processing time has increased. You need to implement a change that would improve query performance in BigQuery. What should you do?
- A. Implement clustering in BigQuery on the package-tracking ID column.
- B. Tier older data onto Cloud Storage files, and leverage extended tables.
- C. Implement clustering in BigQuery on the ingest date column.
- D. Re-create the table using data partitioning on the package delivery date.
正解：C
質問 # 142
You want to build a managed Hadoop system as your data lake. The data transformation process is composed of a series of Hadoop jobs executed in sequence. To accomplish the design of separating storage from compute, you decided to use the Cloud Storage connector to store all input data, output data, and intermediary dat
a. However, you noticed that one Hadoop job runs very slowly with Cloud Dataproc, when compared with the on-premises bare-metal Hadoop environment (8-core nodes with 100-GB RAM). Analysis shows that this particular Hadoop job is disk I/O intensive. You want to resolve the issue. What should you do?
- A. Allocate sufficient persistent disk space to the Hadoop cluster, and store the intermediate data of that particular Hadoop job on native HDFS
- B. Allocate additional network interface card (NIC), and configure link aggregation in the operating system to use the combined throughput when working with Cloud Storage
- C. Allocate sufficient memory to the Hadoop cluster, so that the intermediary data of that particular Hadoop job can be held in memory
- D. Allocate more CPU cores of the virtual machine instances of the Hadoop cluster so that the networking bandwidth for each instance can scale up
正解：C
質問 # 143
You work on a regression problem in a natural language processing domain, and you have 100M labeled exmaples in your dataset. You have randomly shuffled your data and split your dataset into train and test samples (in a 90/10 ratio). After you trained the neural network and evaluated your model on a test set, you discover that the root-mean-squared error (RMSE) of your model is twice as high on the train set as on the test set. How should you improve the performance of your model?
- A. Increase the share of the test sample in the train-test split.
- B. Increase the complexity of your model by, e.g., introducing an additional layer or increase sizing the size of vocabularies or n-grams used.
- C. Try out regularization techniques (e.g., dropout of batch normalization) to avoid overfitting.
- D. Try to collect more data and increase the size of your dataset.
正解：C
質問 # 144
One of your encryption keys stored in Cloud Key Management Service (Cloud KMS) was exposed. You need to re-encrypt all of your CMEK-protected Cloud Storage data that used that key. and then delete the compromised key. You also want to reduce the risk of objects getting written without customer-managed encryption key (CMEK protection in the future. What should you do?
- A. Rotate the Cloud KMS key version. Continue to use the same Cloud Storage bucket.
- B. Create a new Cloud KMS key. Create a new Cloud Storage bucket. Copy all objects from the old bucket to the new one bucket while specifying the new Cloud KMS key in the copy command.
- C. Create a new Cloud KMS key. Set the default CMEK key on the existing Cloud Storage bucket to the new one.
- D. Create a new Cloud KMS key. Create a new Cloud Storage bucket configured to use the new key as the default CMEK key. Copy all objects from the old bucket to the new bucket without specifying a key.
正解：B
解説：
To re-encrypt all of your CMEK-protected Cloud Storage data after a key has been exposed, and to ensure future writes are protected with a new key, creating a new Cloud KMS key and a new Cloud Storage bucket is the best approach. Here's why option C is the best choice:
Re-encryption of Data:
By creating a new Cloud Storage bucket and copying all objects from the old bucket to the new bucket while specifying the new Cloud KMS key, you ensure that all data is re-encrypted with the new key.
This process effectively re-encrypts the data, removing any dependency on the compromised key.
Ensuring CMEK Protection:
Creating a new bucket and setting the new CMEK as the default ensures that all future objects written to the bucket are automatically protected with the new key.
This reduces the risk of objects being written without CMEK protection.
Deletion of Compromised Key:
Once the data has been copied and re-encrypted, the old key can be safely deleted from Cloud KMS, eliminating the risk associated with the compromised key.
Steps to Implement:
Create a New Cloud KMS Key:
Create a new encryption key in Cloud KMS to replace the compromised key.
Create a New Cloud Storage Bucket:
Create a new Cloud Storage bucket and set the default CMEK to the new key.
Copy and Re-encrypt Data:
Use the gsutil tool to copy data from the old bucket to the new bucket while specifying the new CMEK key:
gsutil -o "GSUtil:gs_json_api_version=2" cp -r gs://old-bucket/* gs://new-bucket/ Delete the Old Key:
After ensuring all data is copied and re-encrypted, delete the compromised key from Cloud KMS.
Reference:
Cloud KMS Documentation
Cloud Storage Encryption
Re-encrypting Data in Cloud Storage
質問 # 145
You are architecting a data transformation solution for BigQuery. Your developers are proficient with SOL and want to use the ELT development technique. In addition, your developers need an intuitive coding environment and the ability to manage SQL as code. You need to identify a solution for your developers to build these pipelines. What should you do?
- A. Use Cloud Composer to load data and run SQL pipelines by using the BigQuery job operators.
- B. Use Dataform to build, manage, and schedule SQL pipelines.
- C. Use Data Fusion to build and execute ETL pipelines
- D. Use Dataflow jobs to read data from Pub/Sub, transform the data, and load the data to BigQuery.
正解：B
解説：
To architect a data transformation solution for BigQuery that aligns with the ELT development technique and provides an intuitive coding environment for SQL-proficient developers, Dataform is an optimal choice. Here's why:
ELT Development Technique:
ELT (Extract, Load, Transform) is a process where data is first extracted and loaded into a data warehouse, and then transformed using SQL queries. This is different from ETL, where data is transformed before being loaded into the data warehouse.
BigQuery supports ELT, allowing developers to write SQL transformations directly in the data warehouse.
Dataform:
Dataform is a development environment designed specifically for data transformations in BigQuery and other SQL-based warehouses.
It provides tools for managing SQL as code, including version control and collaborative development.
Dataform integrates well with existing development workflows and supports scheduling and managing SQL-based data pipelines.
Intuitive Coding Environment:
Dataform offers an intuitive and user-friendly interface for writing and managing SQL queries.
It includes features like SQLX, a SQL dialect that extends standard SQL with features for modularity and reusability, which simplifies the development of complex transformation logic.
Managing SQL as Code:
Dataform supports version control systems like Git, enabling developers to manage their SQL transformations as code.
This allows for better collaboration, code reviews, and version tracking.
Reference:
Dataform Documentation
BigQuery Documentation
Managing ELT Pipelines with Dataform
質問 # 146
......
Professional-Data-Engineer試験解答問題集：https://www.passtest.jp/Google/Professional-Data-Engineer-shiken.html（375問題と解答)
無料2025年最新のGoogle Cloud Certified Professional-Data-Engineer問題集を提供しております！PassTest：https://drive.google.com/open?id=1r-qGsiNK4mkOaTfmKsqTLcUAfjsZjAgu