[2026年07月]更新のGoogle Professional-Data-Engineer実際のブレーン知能問題集
合格できるProfessional-Data-Engineer試験更新されたProfessional-Data-Engineer試験問題集PDFを獲得2026年更新
Google Professional-Data-Engineer 認定試験の出題範囲：
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Google認定の専門的なデータエンジニアになるには、候補者は、複数選択およびシナリオベースの質問で構成される厳格な認証試験に合格する必要があります。この試験は、候補者の知識とスキルをビッグデータとクラウドテクノロジーの操作、およびスケーラブルで効率的なデータ処理システムを設計および実装する能力をテストするように設計されています。この認定は、データパイプライン、データ倉庫、データ分析を扱っており、クラウドコンピューティングと分散システムを深く理解している専門家に最適です。この認定を取得することにより、データエンジニアはこの分野での専門知識を実証し、キャリアの機会を増やし、可能性を獲得できます。
Google Professional-Data-Engineer 資格認定は、データエンジニアリング分野において高く評価され、求められている資格の一つです。Google Cloud が提供するこの資格は、Google Cloud Platform 上でのデータ処理システムの設計と構築に熟練した専門家を対象としています。この試験では、データ収集、変換、ストレージ、分析などのデータエンジニアリング原則に関する知識が求められます。
質問 # 122
Your company's data platform ingests CSV file dumps of booking and user profile data from upstream sources into Cloud Storage. The data analyst team wants to join these datasets on the email field available in both the datasets to perform analysis. However, personally identifiable information (PII) should not be accessible to the analysts. You need to de-identify the email field in both the datasets before loading them into BigQuery for analysts. What should you do?
- A. 1. Create a pipeline to de-identify the email field by using recordTransformations in Cloud Data Loss Prevention (Cloud DLP) with masking as the de-identification transformations type.
2. Load the booking and user profile data into a BigQuery table. - B. 1. Load the CSV files from Cloud Storage into a BigQuery table, and enable dynamic data masking.
2. Create a policy tag with the email mask as the data masking rule.
3. Assign the policy to the email field in both tables. A
4. Assign the Identity and Access Management bigquerydatapolicy.maskedReader role for the BigQuery tables to the analysts. - C. 1. Create a pipeline to de-identify the email field by using recordTransformations in Cloud DLP with format-preserving encryption with FFX as the de-identification transformation type.
2. Load the booking and user profile data into a BigQuery table. - D. 1. Load the CSV files from Cloud Storage into a BigQuery table, and enable dynamic data masking.
2. Create a policy tag with the default masking value as the data masking rule.
3. Assign the policy to the email field in both tables.
4. Assign the Identity and Access Management bigquerydatapolicy.maskedReader role for the BigQuery tables to the analysts
正解：C
質問 # 123
You have historical data covering the last three years in BigQuery and a data pipeline that delivers new data to BigQuery daily. You have noticed that when the Data Science team runs a query filtered on a date column and limited to 30-90 days of data, the query scans the entire table. You also noticed that your bill is increasing more quickly than you expected. You want to resolve the issue as cost-effectively as possible while maintaining the ability to conduct SQL queries. What should you do?
- A. Modify your pipeline to maintain the last 30-90 days of data in one table and the longer history in a different table to minimize full table scans over the entire history.
- B. Re-create the tables using DDL. Partition the tables by a column containing a TIMESTAMP or DATE Type.
- C. Recommend that the Data Science team export the table to a CSV file on Cloud Storage and use Cloud Datalab to explore the data by reading the files directly.
- D. Write an Apache Beam pipeline that creates a BigQuery table per day. Recommend that the Data Science team use wildcards on the table name suffixes to select the data they need.
正解：A
質問 # 124
An online brokerage company requires a high volume trade processing architecture. You need to create a secure queuing system that triggers jobs. The jobs will run in Google Cloud and cat the company's Python API to execute trades. You need to efficiently implement a solution. What should you do?
- A. Write an application that makes a queue in a NoSQL database
- B. Use Cloud Composer to subscribe to a Pub/Sub tope and can the Python API.
- C. Use a Pub/Sub push subscription to trigger a Cloud Function to pass the data to tie Python API.
- D. Write an application hosted on a Compute Engine instance that makes a push subscription to the Pub/ Sub topic
正解：C
質問 # 125
You have designed an Apache Beam processing pipeline that reads from a Pub/Sub topic. The topic has a message retention duration of one day, and writes to a Cloud Storage bucket. You need to select a bucket location and processing strategy to prevent data loss in case of a regional outage with an RPO of 15 minutes. What should you do?
- A. 1. Use a dual-region Cloud Storage bucket.
2. Monitor Dataflow metrics with Cloud Monitoring to determine when an outage occurs
3 Seek the subscription back in time by 15 minutes to recover the acknowledged messages
4 Start the Dataflow job in a secondary region - B. 1. Use a dual-region Cloud Storage bucket with turbo replication enabled
2 Monitor Dataflow metrics with Cloud Monitoring to determine when an outage occurs
3 Seek the subscription back in time by 60 minutes to recover the acknowledged messages
4 Start the Dataflow job in a secondary region. - C. 1 Use a regional Cloud Storage bucket
2 Monitor Dataflow metrics with Cloud Monitoring to determine when an outage occurs
3 Seek the subscription back in time by one day to recover the acknowledged messages
4 Start the Dataflow job in a secondary region and write in a bucket in the same region - D. 1 Use a multi-regional Cloud Storage bucket
2 Monitor Dataflow metrics with Cloud Monitoring to determine when an outage occurs
3 Seek the subscription back in time by 60 minutes to recover the acknowledged messages
4 Start the Dataflow job in a secondary region
正解：A
解説：
A dual-region Cloud Storage bucket is a type of bucket that stores data redundantly across two regions within the same continent. This provides higher availability and durability than a regional bucket, which stores data in a single region. A dual-region bucket also provides lower latency and higher throughput than a multi-regional bucket, which stores data across multiple regions within a continent or across continents. A dual-region bucket with turbo replication enabled is a premium option that offers even faster replication across regions, but it is more expensive and not necessary for this scenario.
By using a dual-region Cloud Storage bucket, you can ensure that your data is protected from regional outages, and that you can access it from either region with low latency and high performance. You can also monitor the Dataflow metrics with Cloud Monitoring to determine when an outage occurs, and seek the subscription back in time by 15 minutes to recover the acknowledged messages. Seeking a subscription allows you to replay the messages from a Pub/Sub topic that were published within the message retention duration, which is one day in this case. By seeking the subscription back in time by 15 minutes, you can meet the RPO of 15 minutes, which means the maximum amount of data loss that is acceptable for your business. You can then start the Dataflow job in a secondary region and write to the same dual-region bucket, which will resume the processing of the messages and prevent data loss.
Option A is not a good solution, as using a regional Cloud Storage bucket does not provide any redundancy or protection from regional outages. If the region where the bucket is located experiences an outage, you will not be able to access your data or write new data to the bucket. Seeking the subscription back in time by one day is also unnecessary and inefficient, as it will replay all the messages from the past day, even though you only need to recover the messages from the past 15 minutes.
Option B is not a good solution, as using a multi-regional Cloud Storage bucket does not provide the best performance or cost-efficiency for this scenario. A multi-regional bucket stores data across multiple regions within a continent or across continents, which provides higher availability and durability than a dual-region bucket, but also higher latency and lower throughput. A multi-regional bucket is more suitable for serving data to a global audience, not for processing data with Dataflow within a single continent. Seeking the subscription back in time by 60 minutes is also unnecessary and inefficient, as it will replay more messages than needed to meet the RPO of 15 minutes.
Option D is not a good solution, as using a dual-region Cloud Storage bucket with turbo replication enabled does not provide any additional benefit for this scenario, but only increases the cost. Turbo replication is a premium option that offers faster replication across regions, but it is not required to meet the RPO of 15 minutes. Seeking the subscription back in time by 60 minutes is also unnecessary and inefficient, as it will replay more messages than needed to meet the RPO of 15 minutes. Reference: Storage locations | Cloud Storage | Google Cloud, Dataflow metrics | Cloud Dataflow | Google Cloud, Seeking a subscription | Cloud Pub/Sub | Google Cloud, Recovery point objective (RPO) | Acronis.
質問 # 126
You are running a pipeline in Cloud Dataflow that receives messages from a Cloud Pub/Sub topic and writes the results to a BigQuery dataset in the EU. Currently, your pipeline is located in europe-west4 and has a maximum of 3 workers, instance type n1-standard-1. You notice that during peak periods, your pipeline is struggling to process records in a timely fashion, when all 3 workers are at maximum CPU utilization. Which two actions can you take to increase performance of your pipeline? (Choose two.)
- A. Change the zone of your Cloud Dataflow pipeline to run in us-central1
- B. Create a temporary table in Cloud Spanner that will act as a buffer for new data. Create a new step in your pipeline to write to this table first, and then create a new pipeline to write from Cloud Spanner to BigQuery
- C. Create a temporary table in Cloud Bigtable that will act as a buffer for new data. Create a new step in your pipeline to write to this table first, and then create a new pipeline to write from Cloud Bigtable to BigQuery
- D. Increase the number of max workers
- E. Use a larger instance type for your Cloud Dataflow workers
正解：B、E
解説：
Explanation/Reference:
質問 # 127
You created an analytics environment on Google Cloud so that your data scientist team can explore data without impacting the on-premises Apache Hadoop solution. The data in the on-premises Hadoop Distributed File System (HDFS) cluster is in Optimized Row Columnar (ORC) formatted files with multiple columns of Hive partitioning. The data scientist team needs to be able to explore the data in a similar way as they used the on-premises HDFS cluster with SQL on the Hive query engine. You need to choose the most cost-effective storage and processing solution. What should you do?
- A. Import the ORC files lo Bigtable tables for the data scientist team.
- B. Copy the ORC files on Cloud Storage, then create external BigQuery tables for the data scientist team.
- C. Import the ORC files to BigOuery tables for the data scientist team.
- D. Copy the ORC files on Cloud Storage, then deploy a Dataproc cluster for the data scientist team.
正解：B
解説：
The requirements are:
* Explore ORC formatted files with Hive partitioning.
* Mimic the SQL on Hive query engine experience.
* Cost-effective storage and processing.
* Avoid impacting the on-premises Hadoop solution.
Let's analyze the options:
* Option A (Import to Bigtable):Bigtable is a NoSQL database, not suited for SQL-based exploration of ORC files or Hive-style partitioning directly. This would require significant data transformation and a different query paradigm. Not cost-effective for this use case.
* Option B (Import to BigQuery native tables):Importing data into BigQuery native storage is an option. BigQuery can load ORC files. This provides excellent query performance. However, it involves an ETL step (importing) and storage costs for the datawithin BigQuery, which might be higher than keeping it in its original format on Cloud Storage if query patterns are exploratory and not extremely frequent on all data.
* Option C (Copy to Cloud Storage, deploy Dataproc):Dataproc allows you to run Hadoop/Spark (and thus Hive) clusters on Google Cloud. This would provide a very similar experience ("SQL on the Hive query engine"). However, running a persistent Dataproc cluster incurs compute costs for the cluster nodes, even when not actively querying. While ephemeral clusters are possible, it adds operational overhead for exploratory queries. Storage on Cloud Storage is cost-effective.
* Option D (Copy to Cloud Storage, create external BigQuery tables):This is often the most cost- effective and straightforward solution for this scenario.
* Cost-effective Storage:Cloud Storage is a low-cost option for storing files like ORC.
* SQL Interface:BigQuery provides a familiar SQL interface.
* External Tables:BigQuery can query data directly from Cloud Storage (including ORC files) using external tables. This avoids the need to load data into BigQuery's managed storage, saving on storage costs and ETL effort.
* Hive Partitioning:BigQuery external tables support Hive partitioning layouts. When you define the external table, you can specify the partitioning scheme, and BigQuery will use partition pruning to scan only relevant partitions, improving performance and reducing costs for queries that filter on partition keys. This directly mimics the Hive experience.
* Processing Cost:You only pay for the data scanned by BigQuery queries, which aligns with exploratory analysis.
Comparing D with B: External tables are generally more cost-effective for storage and initial setup if the data is already in ORC and an ETL process into BigQuery native storage is to be avoided. Query performance might be slightly less than native tables but is often sufficient for exploration, especially with partitioning.
Comparing D with C: BigQuery external tables are serverless, meaning no cluster to manage or pay for when idle. Dataproc requires managing and paying for a cluster. For exploration, the serverless nature of BigQuery is usually more cost-effective.
Therefore, copying ORC files to Cloud Storage and using BigQuery external tables is the most cost-effective solution that meets all requirements.
Reference:
Google Cloud Documentation: BigQuery > External data sources > Querying Cloud Storage data. "You can query data in Cloud Storage by using external tables or federated queries. External tables are tables that read data directly from files in Cloud Storage." Google Cloud Documentation: BigQuery > External data sources > Supported formats and compression types. ORC is a supported format.
Google Cloud Documentation: BigQuery > Creating and using tables > Creating external tables. "External tables let you query data stored in Cloud Storage as if it were a standardBigQuery table. You can use external tables to query data in various formats, including... ORC..." Google Cloud Documentation: BigQuery > Creating and using tables > Querying partitioned external tables.
"You can create an external table that is partitioned on Hive partitioning keys. When you query a Hive partitioned external table, BigQuery performs partition pruning to skip reading unnecessary partitions." This directly addresses the "Hive partitioning" and "explore data in a similar way" requirements.
Google Cloud Blog: "Choosing the right data processing option on GCP: BigQuery vs. Dataproc" (and similar articles) often highlight BigQuery external tables as a cost-effective way to query data in place on Cloud Storage, especially for data lake scenarios.
質問 # 128
You have a variety of files in Cloud Storage that your data science team wants to use in their models Currently, users do not have a method to explore, cleanse, and validate the data in Cloud Storage. You are looking for a low code solution that can be used by your data science team to quickly cleanse and explore data within Cloud Storage. What should you do?
- A. Provide the data science team access to Dataflow to create a pipeline to prepare and validate the raw data and load data into BigQuery for data exploration.
- B. Load the data into BigQuery and use SQL to transform the data as necessary Provide the data science team access to staging tables to explore the raw data.
- C. Create an external table in BigQuery and use SQL to transform the data as necessary Provide the data science team access to the external tables to explore the raw data.
- D. Provide the data science team access to Dataprep to prepare, validate, and explore the data within Cloud Storage.
正解：D
解説：
Dataprep is a low code, serverless, and fully managed service that allows users to visually explore, cleanse, and validate data in Cloud Storage. It also provides features such as data profiling, data quality, data transformation, and data lineage. Dataprep is integrated with BigQuery, so users can easily export the prepared data to BigQuery for further analysis or modeling. Dataprep is a suitable solution for the data science team to quickly and easily work with the data in Cloud Storage, without having to write code or manage infrastructure. The other options are not as suitable as Dataprep for this use case, because they either require more coding, more infrastructure management, or more data movement. Loading the data into BigQuery, either directly or through Dataflow, would incur additional costs and latency, and may not provide the same level of data exploration and validation as Dataprep. Creating an external table in BigQuery would allow users to query the data in Cloud Storage, but would not provide the same level of data cleansing and transformation as Dataprep. References:
* Dataprep overview
* Dataprep features
* Dataprep and BigQuery integration
質問 # 129
Which Java SDK class can you use to run your Dataflow programs locally?
- A. MachineRunner
- B. LocalPipelineRunner
- C. DirectPipelineRunner
- D. LocalRunner
正解：C
解説：
DirectPipelineRunner allows you to execute operations in the pipeline directly, without any optimization.
Useful for small local execution and tests
Reference:
https://cloud.google.com/dataflow/java-sdk/JavaDoc/com/google/cloud/dataflow/sdk/runners/DirectPipelineRun
質問 # 130
Government regulations in your industry mandate that you have to maintain an auditable record of access to certain types of dat A.
Assuming that all expiring logs will be archived correctly, where should you store data that is subject to that mandate?
- A. In a BigQuery dataset that is viewable only by authorized personnel, with the Data Access log used to provide the auditability.
- B. In a bucket on Cloud Storage that is accessible only by an AppEngine service that collects user information and logs the access before providing a link to the bucket.
- C. Encrypted on Cloud Storage with user-supplied encryption keys. A separate decryption key will be given to each authorized user.
- D. In Cloud SQL, with separate database user names to each user. The Cloud SQL Admin activity logs will be used to provide the auditability.
正解：A
質問 # 131
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.
You need to compose visualizations for operations teams with the following requirements:
The report must include telemetry data from all 50,000 installations for the most resent 6 weeks
(sampling once every minute).
The report must not be more than 3 hours delayed from live data.
The actionable report should only show suboptimal links.
Most suboptimal links should be sorted to the top.
Suboptimal links can be grouped and filtered by regional geography.
User response time to load the report must be <5 seconds.
Which approach meets the requirements?
- A. Load the data into Google Sheets, use formulas to calculate a metric, and use filters/sorting to show only suboptimal links in a table.
- B. Load the data into Google BigQuery tables, write a Google Data Studio 360 report that connects to your data, calculates a metric, and then uses a filter expression to show only suboptimal rows in a table.
- C. Load the data into Google BigQuery tables, write Google Apps Script that queries the data, calculates the metric, and shows only suboptimal rows in a table in Google Sheets.
- D. Load the data into Google Cloud Datastore tables, write a Google App Engine Application that queries all rows, applies a function to derive the metric, and then renders results in a table using the Google charts and visualization API.
正解：D
質問 # 132
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 global windowing function, which causes the job to fail when the pipeline is created
- B. They have not assigned the timestamp, which causes the job to fail
- C. They have not set the triggers to accommodate the data coming in late, which causes the job to fail
- D. They have not applied a non-global windowing function, which causes the job to fail when the pipeline is created
正解：D
質問 # 133
You work for a shipping company that has distribution centers where packages move on delivery lines to route them properly. The company wants to add cameras to the delivery lines to detect and track any visual damage to the packages in transit. You need to create a way to automate the detection of damaged packages and flag them for human review in real time while the packages are in transit. Which solution should you choose?
- A. Use BigQuery machine learning to be able to train the model at scale, so you can analyze the packages in batches.
- B. Use TensorFlow to create a model that is trained on your corpus of images. Create a Python notebook in Cloud Datalab that uses this model so you can analyze for damaged packages.
- C. Train an AutoML model on your corpus of images, and build an API around that model to integrate with the package tracking applications.
- D. Use the Cloud Vision API to detect for damage, and raise an alert through Cloud Functions. Integrate the package tracking applications with this function.
正解：A
質問 # 134
Which of the following is not true about Dataflow pipelines?
- A. Pipelines are a set of operations
- B. Pipelines can share data between instances
- C. Pipelines represent a data processing job
- D. Pipelines represent a directed graph of steps
正解：B
解説：
The data and transforms in a pipeline are unique to, and owned by, that pipeline. While your program can create multiple pipelines, pipelines cannot share data or transforms
質問 # 135
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. Use at least 300 GB of data.
- B. Do not use a production instance.
- C. Run your test for at least 10 minutes.
- D. Before you test, run a heavy pre-test for several minutes.
正解：B
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 # 136
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 an additional table with only the necessary columns.
- C. Create a view on the table to present to the virtualization tool.
- D. Create identity and access management (IAM) roles on the appropriate columns, so only they appear in a query.
正解：C
質問 # 137
Your company operates in three domains: airlines, hotels, and ride-hailing services. Each domain has two teams: analytics and data science, which create data assets in BigQuery with the help of a central data platform team. However, as each domain is evolving rapidly, the central data platform team is becoming a bottleneck. This is causing delays in deriving insights from data, and resulting in stale data when pipelines are not kept up to date. You need to design a data mesh architecture by using Dataplex to eliminate the bottleneck. What should you do?
- A. 1. Create one lake for each domain. Inside each lake, create one zone for each team.
2. Attach each of the BigQuery datasets created by the individual teams as assets to the respective zone.
3. Direct each domain to manage their own lake's data assets. - B. 1. Create one lake for each team. Inside each lake, create one zone for each domain.
2. Attach each of the BigQuery datasets created by the individual teams as assets to the respective zone.
3. Direct each domain to manage their own zone's data assets. - C. 1. Create one lake for each team. Inside each lake, create one zone for each domain.
2. Attach each of the BigQuery datasets created by the individual teams as assets to the respective zone.
3. Have the central data platform team manage all zones' data assets. - D. 1. Create one lake for each domain. Inside each lake, create one zone for each team.
2. Attach each of the BigQuery datasets created by the individual teams as assets to the respective zone.
3. Have the central data platform team manage all lakes' data assets.
正解：A
質問 # 138
You have enabled the free integration between Firebase Analytics and Google BigQuery. Firebase now automatically creates a new table daily in BigQuery in the format app_events_YYYYMMDD. You want to query all of the tables for the past 30 days in legacy SQL. What should you do?
- A. Use the WHERE_PARTITIONTIME pseudo column
- B. Use SELECT IF.(date >= YYYY-MM-DD AND date <= YYYY-MM-DD
- C. Use WHERE date BETWEEN YYYY-MM-DD AND YYYY-MM-DD
- D. Use the TABLE_DATE_RANGE function
正解：D
解説：
Legacy sql uses table date range whereas standard sql uses table_sufix for wildcard.
質問 # 139
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