最新の2022年08月06日試験エンジン練習問題Professional-Data-Engineer最新の有効問題集を提供中です
試験解答はProfessional-Data-Engineer最新版テストエンジンをタダで提供します
Google Professional-Data-Engineer 認定試験の出題範囲：
| トピック | 出題範囲 |
|---|---|
| トピック 1 |
|
| トピック 2 |
|
| トピック 3 |
|
| トピック 4 |
|
| トピック 5 |
|
質問 115
Which of these are examples of a value in a sparse vector? (Select 2 answers.)
- A. [0, 0, 0, 1, 0, 0, 1]
- B. [0, 5, 0, 0, 0, 0]
- C. [1, 0, 0, 0, 0, 0, 0]
- D. [0, 1]
正解: C,D
解説:
Categorical features in linear models are typically translated into a sparse vector in which each possible value has a corresponding index or id. For example, if there are only three possible eye colors you can represent 'eye_color' as a length 3 vector: 'brown' would become [1, 0, 0], 'blue' would become [0, 1, 0] and 'green' would become [0, 0, 1]. These vectors are called "sparse" because they may be very long, with many zeros, when the set of possible values is very large (such as all English words).
[0, 0, 0, 1, 0, 0, 1] is not a sparse vector because it has two 1s in it. A sparse vector contains only a single
1.
[0, 5, 0, 0, 0, 0] is not a sparse vector because it has a 5 in it. Sparse vectors only contain 0s and 1s.
Reference: https://www.tensorflow.org/tutorials/linear#feature_columns_and_transformations
質問 116
You are building a model to make clothing recommendations. You know a user's fashion preference is likely to change over time, so you build a data pipeline to stream new data back to the model as it becomes available.
How should you use this data to train the model?
- A. Continuously retrain the model on a combination of existing data and the new data.
- B. Continuously retrain the model on just the new data.
- C. Train on the existing data while using the new data as your test set.
- D. Train on the new data while using the existing data as your test set.
正解: A
質問 117
Your company is migrating their 30-node Apache Hadoop cluster to the cloud. They want to re-use
Hadoop jobs they have already created and minimize the management of the cluster as much as possible.
They also want to be able to persist data beyond the life of the cluster. What should you do?
- A. Create a Google Cloud Dataproc cluster that uses persistent disks for HDFS.
- B. Create a Hadoop cluster on Google Compute Engine that uses Local SSD disks.
- C. Create a Google Cloud Dataflow job to process the data.
- D. Create a Cloud Dataproc cluster that uses the Google Cloud Storage connector.
- E. Create a Hadoop cluster on Google Compute Engine that uses persistent disks.
正解: C
質問 118
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 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 global windowing function, which causes the job to fail when the pipeline is
created
正解: D
質問 119
An online retailer has built their current application on Google App Engine. A new initiative at the company mandates that they extend their application to allow their customers to transact directly via the application.
They need to manage their shopping transactions and analyze combined data from multiple datasets using a business intelligence (BI) tool. They want to use only a single database for this purpose. Which Google Cloud database should they choose?
- A. BigQuery
- B. Cloud SQL
- C. Cloud Datastore
- D. Cloud BigTable
正解: D
解説:
Explanation/Reference:
Reference: https://cloud.google.com/solutions/business-intelligence/
質問 120
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 is rolling out their real-time inventory tracking system. The tracking devices will all send package-tracking messages, which will now go to a single Google Cloud Pub/Sub topic instead of the Apache Kafka cluster. A subscriber application will then process the messages for real-time reporting and store them in Google BigQuery for historical analysis. You want to ensure the package data can be analyzed over time.
Which approach should you take?
- A. Attach the timestamp on each message in the Cloud Pub/Sub subscriber application as they are received.
- B. Attach the timestamp and Package ID on the outbound message from each publisher device as they are sent to Clod Pub/Sub.
- C. Use the automatically generated timestamp from Cloud Pub/Sub to order the data.
- D. Use the NOW () function in BigQuery to record the event's time.
正解: B
質問 121
You have uploaded 5 years of log data to Cloud Storage A user reported that some data points in the log data are outside of their expected ranges, which indicates errors You need to address this issue and be able to run the process again in the future while keeping the original data for compliance reasons What should you do?
- A. Create a Cloud Dataflow workflow that reads the data from Cloud Storage, checks for values outside the expected range, sets the value to an appropriate default, and writes the updated records to a new dataset in Cloud Storage
- B. Import the data from Cloud Storage into BigQuery Create a new BigQuery table, and skip the rows with errors.
- C. Create a Compute Engine instance and create a new copy of the data in Cloud Storage Skip the rows with errors
- D. Create a Cloud Dataflow workflow that reads the data from Cloud Storage, checks for values outside the expected range, sets the value to an appropriate default, and writes the updated records to the same dataset in Cloud Storage
正解: A
質問 122
You are building a new application that you need to collect data from in a scalable way. Data arrives continuously from the application throughout the day, and you expect to generate approximately 150 GB of JSON data per day by the end of the year. Your requirements are:
* Decoupling producer from consumer
* Space and cost-efficient storage of the raw ingested data, which is to be stored indefinitely
* Near real-time SQL query
* Maintain at least 2 years of historical data, which will be queried with SQL Which pipeline should you use to meet these requirements?
- A. Create an application that publishes events to Cloud Pub/Sub, and create a Cloud Dataflow pipeline that transforms the JSON event payloads to Avro, writing the data to Cloud Storage and BigQuery.
- B. Create an application that provides an API. Write a tool to poll the API and write data to Cloud Storage as gzipped JSON files.
- C. Create an application that publishes events to Cloud Pub/Sub, and create Spark jobs on Cloud Dataproc to convert the JSON data to Avro format, stored on HDFS on Persistent Disk.
- D. Create an application that writes to a Cloud SQL database to store the data. Set up periodic exports of the database to write to Cloud Storage and load into BigQuery.
正解: B
質問 123
Which of these statements about exporting data from BigQuery is false?
- A. To export more than 1 GB of data, you need to put a wildcard in the destination filename.
- B. The only compression option available is GZIP.
- C. Data can only be exported in JSON or Avro format.
- D. The only supported export destination is Google Cloud Storage.
正解: C
解説:
Data can be exported in CSV, JSON, or Avro format. If you are exporting nested or repeated data, then CSV format is not supported.
https://cloud.google.com/bigquery/docs/exporting-data
質問 124
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 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 Load Balancing, Cloud Dataflow, and Cloud Storage
- B. Cloud Pub/Sub, Cloud SQL, and Cloud Storage
- C. Cloud Pub/Sub, Cloud Dataflow, and Cloud Storage
- D. Cloud Pub/Sub, Cloud Dataflow, and Local SSD
正解: C
解説:
Pub/Sub -> Dataflow for real time processing requirements.
https://codelabs.developers.google.com/codelabs/cpb104-pubsub/#0
質問 125
How can you get a neural network to learn about relationships between categories in a categorical feature?
- A. Create a hash bucket
- B. Create a one-hot column
- C. Create an embedding column
- D. Create a multi-hot column
正解: C
解説:
There are two problems with one-hot encoding. First, it has high dimensionality, meaning that instead of having just one value, like a continuous feature, it has many values, or dimensions. This makes computation more time-consuming, especially if a feature has a very large number of categories. The second problem is that it doesn't encode any relationships between the categories. They are completely independent from each other, so the network has no way of knowing which ones are similar to each other.
Both of these problems can be solved by representing a categorical feature with an embedding column.
The idea is that each category has a smaller vector with, let's say, 5 values in it. But unlike a one-hot vector, the values are not usually 0. The values are weights, similar to the weights that are used for basic features in a neural network. The difference is that each category has a set of weights (5 of them in this case).
You can think of each value in the embedding vector as a feature of the category. So, if two categories are very similar to each other, then their embedding vectors should be very similar too. Reference: https:// cloudacademy.com/google/introduction-to-google-cloud-machine-learning-engine-course/a-wide-and- deep-model.html
質問 126
When you store data in Cloud Bigtable, what is the recommended minimum amount of stored data?
- A. 500 TB
- B. 500 GB
- C. 1 GB
- D. 1 TB
正解: D
解説:
Cloud Bigtable is not a relational database. It does not support SQL queries, joins, or multi- row transactions. It is not a good solution for less than 1 TB of data.
Reference:
https://cloud.google.com/bigtable/docs/overview#title_short_and_other_storage_options
質問 127
As your organization expands its usage of GCP, many teams have started to create their own projects.
Projects are further multiplied to accommodate different stages of deployments and target audiences. Each project requires unique access control configurations. The central IT team needs to have access to all projects.
Furthermore, data from Cloud Storage buckets and BigQuery datasets must be shared for use in other projects in an ad hoc way. You want to simplify access control management by minimizing the number of policies.
Which two steps should you take? Choose 2 answers.
- A. Use Cloud Deployment Manager to automate access provision.
- B. For each Cloud Storage bucket or BigQuery dataset, decide which projects need access. Find all the active members who have access to these projects, and create a Cloud IAM policy to grant access to all these users.
- C. Introduce resource hierarchy to leverage access control policy inheritance.
- D. Create distinct groups for various teams, and specify groups in Cloud IAM policies.
- E. Only use service accounts when sharing data for Cloud Storage buckets and BigQuery datasets.
正解: A,D
質問 128
How would you query specific partitions in a BigQuery table?
- A. Use the DAY column in the WHERE clause
- B. Use DATE BETWEEN in the WHERE clause
- C. Use the __PARTITIONTIME pseudo-column in the WHERE clause
- D. Use the EXTRACT(DAY) clause
正解: C
解説:
Partitioned tables include a pseudo column named _PARTITIONTIME that contains a date- based timestamp for data loaded into the table. To limit a query to particular partitions (such as Jan 1st and 2nd of 2017), use a clause similar to this:
WHERE _PARTITIONTIME BETWEEN TIMESTAMP('2017-01-01') AND
TIMESTAMP('2017-01-02')
Reference: https://cloud.google.com/bigquery/docs/partitioned-
tables#the_partitiontime_pseudo_column
質問 129
Your company's customer and order databases are often under heavy load. This makes performing analytics against them difficult without harming operations. The databases are in a MySQL cluster, with nightly backups taken using mysqldump. You want to perform analytics with minimal impact on operations.
What should you do?
- A. Mount the backups to Google Cloud SQL, and then process the data using Google Cloud Dataproc.
- B. Connect an on-premises Apache Hadoop cluster to MySQL and perform ETL.
- C. Use an ETL tool to load the data from MySQL into Google BigQuery.
- D. Add a node to the MySQL cluster and build an OLAP cube there.
正解: B
質問 130
......
Professional-Data-Engineer試験問題集で無料サンプルは365日更新されます：https://jp.fast2test.com/Professional-Data-Engineer-premium-file.html
合格させるProfessional-Data-Engineer試験問題と最新のProfessional-Data-Engineerテスト問題集PDF：https://drive.google.com/open?id=1cIC1E7akzgrHSRItnvyUvBQ7MpVo8LJD