Google Professional-Data-Engineer最新問題集[2025]高得点を掴み取れ
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質問 # 50
You are planning to migrate your current on-premises Apache Hadoop deployment to the cloud. You need to ensure that the deployment is as fault-tolerant and cost-effective as possible for long-running batch jobs.
You want to use a managed service. What should you do?
- A. Deploy a Cloud Dataproc cluster. Use a standard persistent disk and 50% preemptible workers. Store data in Cloud Storage, and change references in scripts from hdfs:// to gs://
- B. Deploy a Cloud Dataproc cluster. Use an SSD persistent disk and 50% preemptible workers. Store data in Cloud Storage, and change references in scripts from hdfs:// to gs://
- C. Install Hadoop and Spark on a 10-node Compute Engine instance group with preemptible instances.
Store data in HDFS. Change references in scripts from hdfs:// to gs:// - D. Install Hadoop and Spark on a 10-node Compute Engine instance group with standard instances.
Install the Cloud Storage connector, and store the data in Cloud Storage. Change references in scripts from hdfs:// to gs://
正解：A
質問 # 51
Your infrastructure includes a set of YouTube channels. You have been tasked with creating a process for sending the YouTube channel data to Google Cloud for analysis. You want to design a solution that allows your world-wide marketing teams to perform ANSI SQL and other types of analysis on up-to-date YouTube channels log data. How should you set up the log data transfer into Google Cloud?
- A. Use BigQuery Data Transfer Service to transfer the offsite backup files to a Cloud Storage Regional storage bucket as a final destination.
- B. Use Storage Transfer Service to transfer the offsite backup files to a Cloud Storage Regional bucket as a final destination.
- C. Use Storage Transfer Service to transfer the offsite backup files to a Cloud Storage Multi-Regional storage bucket as a final destination.
- D. Use BigQuery Data Transfer Service to transfer the offsite backup files to a Cloud Storage Multi- Regional storage bucket as a final destination.
正解：D
質問 # 52
What are two of the benefits of using denormalized data structures in BigQuery?
- A. Reduces the amount of data processed, increases query speed
- B. Increases query speed, makes queries simpler
- C. Reduces the amount of data processed, reduces the amount of storage required
- D. Reduces the amount of storage required, increases query speed
正解：B
解説：
Denormalization increases query speed for tables with billions of rows because BigQuery's performance degrades when doing JOINs on large tables, but with a denormalized data
structure, you don't have to use JOINs, since all of the data has been combined into one table. Denormalization also makes queries simpler because you do not have to use JOIN clauses.
Denormalization increases the amount of data processed and the amount of storage required because it creates redundant data.
Reference:
https://cloud.google.com/solutions/bigquery-data-warehouse#denormalizing_data
質問 # 53
You are designing storage for very large text files for a data pipeline on Google Cloud. You want to support ANSI SQL queries. You also want to support compression and parallel load from the input locations using Google recommended practices. What should you do?
- A. Transform text files to compressed Avro using Cloud Dataflow. Use Cloud Storage and BigQuery permanent linked tables for query.
- B. Compress text files to gzip using the Grid Computing Tools. Use Cloud Storage, and then import into Cloud Bigtable for query.
- C. Transform text files to compressed Avro using Cloud Dataflow. Use BigQuery for storage and query.
- D. Compress text files to gzip using the Grid Computing Tools. Use BigQuery for storage and query.
正解：B
質問 # 54
Which TensorFlow function can you use to configure a categorical column if you don't know all of the possible values for that column?
- A. categorical_column_with_unknown_values
- B. categorical_column_with_vocabulary_list
- C. sparse_column_with_keys
- D. categorical_column_with_hash_bucket
正解：D
解説：
Explanation
If you know the set of all possible feature values of a column and there are only a few of them, you can use categorical_column_with_vocabulary_list. Each key in the list will get assigned an auto-incremental ID starting from 0.
What if we don't know the set of possible values in advance? Not a problem. We can use categorical_column_with_hash_bucket instead. What will happen is that each possible value in the feature column occupation will be hashed to an integer ID as we encounter them in training.
Reference: https://www.tensorflow.org/tutorials/wide
質問 # 55
To run a TensorFlow training job on your own computer using Cloud Machine Learning Engine, what would your command start with?
- A. gcloud ml-engine local train
- B. You can't run a TensorFlow program on your own computer using Cloud ML Engine .
- C. gcloud ml-engine jobs submit training
- D. gcloud ml-engine jobs submit training local
正解：A
解説：
Explanation
gcloud ml-engine local train - run a Cloud ML Engine training job locally This command runs the specified module in an environment similar to that of a live Cloud ML Engine Training Job.
This is especially useful in the case of testing distributed models, as it allows you to validate that you are properly interacting with the Cloud ML Engine cluster configuration.
Reference: https://cloud.google.com/sdk/gcloud/reference/ml-engine/local/train
質問 # 56
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
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 management has determined that the current Apache Kafka servers cannot handle the data volume for their real-time inventory tracking system. You need to build a new system on Google Cloud Platform (GCP) that will feed the proprietary tracking software. The system must be able to ingest data from a variety of global sources, process and query in real-time, and store the data reliably. Which combination of GCP products should you choose?
- A. Cloud Pub/Sub, Cloud Dataflow, and Cloud Storage
- B. Cloud Load Balancing, Cloud Dataflow, and Cloud Storage
- C. Cloud Pub/Sub, Cloud SQL, and Cloud Storage
- D. Cloud Pub/Sub, Cloud Dataflow, and Local SSD
- E. Cloud Dataflow, Cloud SQL, and Cloud Storage
正解：C
質問 # 57
Your company needs to upload their historic data to Cloud Storage. The security rules don't allow access from external IPs to their on-premises resources. After an initial upload, they will add new data from existing on-premises applications every day. What should they do?
- A. Install an FTP server on a Compute Engine VM to receive the files and move them to Cloud Storage.
- B. Use Cloud Dataflow and write the data to Cloud Storage.
- C. Write a job template in Cloud Dataproc to perform the data transfer.
- D. Execute gsutil rsync from the on-premises servers.
正解：D
質問 # 58
Your analytics team wants to build a simple statistical model to determine which customers are most likely
to work with your company again, based on a few different metrics. They want to run the model on Apache
Spark, using data housed in Google Cloud Storage, and you have recommended using Google Cloud
Dataproc to execute this job. Testing has shown that this workload can run in approximately 30 minutes on
a 15-node cluster, outputting the results into Google BigQuery. The plan is to run this workload weekly.
How should you optimize the cluster for cost?
- A. Use a higher-memory node so that the job runs faster
- B. Use SSDs on the worker nodes so that the job can run faster
- C. Migrate the workload to Google Cloud Dataflow
- D. Use pre-emptible virtual machines (VMs) for the cluster
正解：C
質問 # 59
You are configuring networking for a Dataflow job. The data pipeline uses custom container images with the libraries that are required for the transformation logic preinstalled. The data pipeline reads the data from Cloud Storage and writes the data to BigQuery. You need to ensure cost-effective and secure communication between the pipeline and Google APIs and services. What should you do?
- A. Enable Cloud NAT to provide outbound internet connectivity while enforcing firewall rules.
- B. Disable external IP addresses and establish a Private Service Connect endpoint IP address.
- C. Leave external IP addresses assigned to worker VMs while enforcing firewall rules.
- D. Disable external IP addresses from worker VMs and enable Private Google Access.
正解：D
解説：
Private Google Access allows VMs without external IP addresses to communicate with Google APIs and services over internal routes. This reduces the cost and increases the security of the data pipeline. Custom container images can be stored in Container Registry, which supports Private Google Access. Dataflow supports Private Google Access for both batch and streaming jobs. References:
* Private Google Access overview
* Using Private Google Access and Cloud NAT
* Using custom containers with Dataflow
質問 # 60
Your company is performing data preprocessing for a learning algorithm in Google Cloud Dataflow.
Numerous data logs are being are being generated during this step, and the team wants to analyze them.
Due to the dynamic nature of the campaign, the data is growing exponentially every hour. The data scientists have written the following code to read the data for a new key features in the logs.
BigQueryIO.Read
.named("ReadLogData")
.from("clouddataflow-readonly:samples.log_data")
You want to improve the performance of this data read. What should you do?
- A. Use .fromQuery operation to read specific fields from the table.
- B. Call a transform that returns TableRow objects, where each element in the PCollexction represents a single row in the table.
- C. Use of both the Google BigQuery TableSchema and TableFieldSchema classes.
- D. Specify the Tableobject in the code.
正解：B
質問 # 61
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. Unsupervised learning to determine which transactions are most likely to be fraudulent.
- B. Clustering to divide the transactions into N categories based on feature similarity.
- C. Supervised learning to determine which transactions are most likely to be fraudulent.
- D. Supervised learning to predict the location of a transaction.
- E. Unsupervised learning to predict the location of a transaction.
- F. Reinforcement learning to predict the location of a transaction.
正解：A、B、D
質問 # 62
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. 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. - B. Contact your internet service provider (ISP) to increase your maximum bandwidth to at least 100 Mbps.
- C. 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. - D. Redesign the data ingestion process to use gsutil tool to send the CSV files to a storage bucket in
parallel. - E. Introduce data compression for each file to increase the rate file of file transfer.
正解：A、D
質問 # 63
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 applied a non-global windowing function, which causes the job to fail when the pipeline is created
- B. They have not applied a global windowing function, which causes the job to fail when the pipeline is created
- C. They have not assigned the timestamp, which causes the job to fail
- D. They have not set the triggers to accommodate the data coming in late, which causes the job to fail
正解：B
質問 # 64
You are designing storage for 20 TB of text files as part of deploying a data pipeline on Google Cloud. Your input data is in CSV format. You want to minimize the cost of querying aggregate values for multiple users who will query the data in Cloud Storage with multiple engines. Which storage service and schema design should you use?
- A. Use Cloud Storage for storage. Link as permanent tables in BigQuery for query.
- B. Use Cloud Bigtable for storage. Link as permanent tables in BigQuery for query.
- C. Use Cloud Bigtable for storage. Install the HBase shell on a Compute Engine instance to query the Cloud Bigtable data.
- D. Use Cloud Storage for storage. Link as temporary tables in BigQuery for query.
正解：A
質問 # 65
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 has not gone through an ETL phase before loading into BigQuery.
- B. The CSV data loaded in BigQuery is not flagged as CSV.
- C. The CSV data has invalid rows that were skipped on import.
- D. The CSV data loaded in BigQuery is not using BigQuery's default encoding.
正解：C
質問 # 66
What is the general recommendation when designing your row keys for a Cloud Bigtable schema?
- A. Keep your row key reasonably short
- B. Include multiple time series values within the row key
- C. Keep the row keep as an 8 bit integer
- D. Keep your row key as long as the field permits
正解：A
解説：
A general guide is to, keep your row keys reasonably short. Long row keys take up additional memory and storage and increase the time it takes to get responses from the Cloud Bigtable server.
質問 # 67
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. Switch from HDDs to SSDs, copy initial data from GCS to HDFS, run the Spark job and copy results back to GCS.
- C. Switch from HDDs to SSDs, override the preemptible VMs configuration to increase the boot disk size.
- D. Increase the size of your parquet files to ensure them to be 1 GB minimum.
正解：B
質問 # 68
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