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2024年最新の有効なProfessional-Data-Engineerリアル試験問題（更新された）100%問題集と練習試験合格させます [Q134-Q156]

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2024年最新の有効なProfessional-Data-Engineerリアル試験問題（更新された）100%問題集と練習試験合格させます

[更新されたのは2024年]Google Professional-Data-Engineer問題準備には無料サンプルのPDF

質問 # 134
Your company built a TensorFlow neutral-network model with a large number of neurons and layers. The model fits well for the training data. However, when tested against new data, it performs poorly. What method can you employ to address this?

  • A. Dropout Methods
  • B. Threading
  • C. Dimensionality Reduction
  • D. Serialization

正解：A

解説：
Explanation
Reference
https://medium.com/mlreview/a-simple-deep-learning-model-for-stock-price-prediction-using-tensorflow-30505
Topic 1, 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
* 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.


質問 # 135
What are two methods that can be used to denormalize tables in BigQuery?

  • A. 1) Join tables into one table; 2) Use nested repeated fields
  • B. 1) Use a partitioned table; 2) Join tables into one table
  • C. 1) Use nested repeated fields; 2) Use a partitioned table
  • D. 1) Split table into multiple tables; 2) Use a partitioned table

正解：A

解説：
The conventional method of denormalizing data involves simply writing a fact, along with all its dimensions, into a flat table structure. For example, if you are dealing with sales transactions, you would write each individual fact to a record, along with the accompanying dimensions such as order and customer information.
The other method for denormalizing data takes advantage of BigQuery's native support for nested and repeated structures in JSON or Avro input data. Expressing records using nested and repeated structures can provide a more natural representation of the underlying data. In the case of the sales order, the outer part of a JSON structure would contain the order and customer information, and the inner part of the structure would contain the individual line items of the order, which would be represented as nested, repeated elements.
Reference: https://cloud.google.com/solutions/bigquery-data-
warehouse#denormalizing_data


質問 # 136
Which of the following is NOT one of the three main types of triggers that Dataflow supports?

  • A. Trigger based on time
  • B. Trigger based on element count
  • C. Trigger that is a combination of other triggers
  • D. Trigger based on element size in bytes

正解：D

解説：
There are three major kinds of triggers that Dataflow supports: 1. Time-based triggers 2. Data-driven triggers. You can set a trigger to emit results from a window when that window has received a certain number of data elements. 3. Composite triggers. These triggers combine multiple time-based or data-driven triggers in some logical way


質問 # 137
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 Datastore
  • C. Cloud BigTable
  • D. Cloud SQL

正解：D


質問 # 138
You need to create a near real-time inventory dashboard that reads the main inventory tables in your BigQuery data warehouse. Historical inventory data is stored as inventory balances by item and location.
You have several thousand updates to inventory every hour. You want to maximize performance of the dashboard and ensure that the data is accurate. What should you do?

  • A. Leverage BigQuery UPDATE statements to update the inventory balances as they are changing.
  • B. Use the BigQuery streaming the stream changes into a daily inventory movement table. Calculate balances in a view that joins it to the historical inventory balance table. Update the inventory balance table nightly.
  • C. Partition the inventory balance table by item to reduce the amount of data scanned with each inventory update.
  • D. Use the BigQuery bulk loader to batch load inventory changes into a daily inventory movement table.
    Calculate balances in a view that joins it to the historical inventory balance table. Update the inventory balance table nightly.

正解：B


質問 # 139
You need to create a near real-time inventory dashboard that reads the main inventory tables in your BigQuery data warehouse. Historical inventory data is stored as inventory balances by item and location. You have several thousand updates to inventory every hour. You want to maximize performance of the dashboard and ensure that the data is accurate. What should you do?

  • A. Leverage BigQuery UPDATE statements to update the inventory balances as they are changing.
  • B. Partition the inventory balance table by item to reduce the amount of data scanned with each inventory update.
  • C. Use the BigQuery streaming the stream changes into a daily inventory movement table. Calculate balances in a view that joins it to the historical inventory balance table. Update the inventory balance table nightly.
  • D. Use the BigQuery bulk loader to batch load inventory changes into a daily inventory movement table.
    Calculate balances in a view that joins it to the historical inventory balance table. Update the inventory balance table nightly.

正解：A


質問 # 140
Which of these is NOT a way to customize the software on Dataproc cluster instances?

  • A. Set initialization actions
  • B. Log into the master node and make changes from there
  • C. Configure the cluster using Cloud Deployment Manager
  • D. Modify configuration files using cluster properties

正解：C

解説：
You can access the master node of the cluster by clicking the SSH button next to it in the Cloud Console.
You can easily use the --properties option of the dataproc command in the Google Cloud SDK to modify many common configuration files when creating a cluster. When creating a Cloud Dataproc cluster, you can specify initialization actions in executables and/or scripts that Cloud Dataproc will run on all nodes in your Cloud Dataproc cluster immediately after the cluster is set up. [https://cloud.google.com/dataproc/ docs/concepts/configuring-clusters/init-actions] Reference: https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/cluster-properties


質問 # 141
You have spent a few days loading data from comma-separated values (CSV) files into the Google BigQuery table CLICK_STREAM. The column DT stores the epoch time of click events. For convenience, you chose a simple schema where every field is treated as the STRING type. Now, you want to compute web session durations of users who visit your site, and you want to change its data type to the TIMESTAMP. You want to minimize the migration effort without making future queries computationally expensive. What should you do?

  • A. Create a view CLICK_STREAM_V, where strings from the column DT are cast into TIMESTAMP values. Reference the view CLICK_STREAM_V instead of the table CLICK_STREAM from now on.
  • B. Construct a query to return every row of the table CLICK_STREAM, while using the built-in function to cast strings from the column DT into TIMESTAMP values. Run the query into a destination table NEW_CLICK_STREAM, in which the column TS is the TIMESTAMP type. Reference the table NEW_CLICK_STREAM instead of the table CLICK_STREAM from now on. In the future, new data is loaded into the table NEW_CLICK_STREAM.
  • C. Add two columns to the table CLICK STREAM: TS of the TIMESTAMP type and IS_NEW of the BOOLEAN type. Reload all data in append mode. For each appended row, set the value of IS_NEW to true. For future queries, reference the column TS instead of the column DT, with the WHERE clause ensuring that the value of IS_NEW must be true.
  • D. Delete the table CLICK_STREAM, and then re-create it such that the column DT is of the TIMESTAMP type. Reload the data.
  • E. Add a column TS of the TIMESTAMP type to the table CLICK_STREAM, and populate the numeric values from the column TS for each row. Reference the column TS instead of the column DT from now on.

正解：C

解説：
Topic 1, 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
* 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.


質問 # 142
You are building an ELT solution in BigQuery by using Dataform. You need to perform uniqueness and null value checks on your final tables. What should you do to efficiently integrate these checks into your pipeline?

  • A. Create Dataplex data quality tasks.
  • B. Write a Spark-based stored procedure.
  • C. Build Dataform assertions into your code
  • D. Build BigQuery user-defined functions (UDFs).

正解：C

解説：
Dataform assertions are data quality tests that find rows that violate one or more rules specified in the query. If the query returns any rows, the assertion fails. Dataform runs assertions every time it updates your SQL workflow and alerts you if any assertions fail. You can create assertions for all Dataform table types: tables, incremental tables, views, and materialized views. You can add built-in assertions to the config block of a table, such as nonNull and rowConditions, or create manual assertions with SQLX for advanced use cases.
Dataform automatically creates views in BigQuery that contain the results of compiled assertion queries, which you can inspect to debug failing assertions. Dataform assertions are an efficient way to integrate data quality checks into your ELT solution in BigQuery by using Dataform. References: Test tables with assertions
| Dataform | Google Cloud, Test data quality with assertions | Dataform, Data quality tests and documenting datasets | Dataform, Data quality testing with SQL assertions | Dataform


質問 # 143
How would you query specific partitions in a BigQuery table?

  • A. Use DATE BETWEEN in the WHERE clause
  • B. Use the DAY column 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 # 144
You are collecting loT sensor data from millions of devices across the world and storing the data in BigQuery.
Your access pattern is based on recent data tittered by location_id and device_version with the following query:

You want to optimize your queries for cost and performance. How should you structure your data?

  • A. Partition table data by create_date, location_id and device_version
  • B. Cluster table data by create_date, partition by location and device_version
  • C. Partition table data by create_date cluster table data by tocation_id and device_version
  • D. Cluster table data by create_date location_id and device_version

正解：D


質問 # 145
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. Write an application hosted on a Compute Engine instance that makes a push subscription to the Pub/Sub topic
  • C. Use a Pub/Sub push subscription to trigger a Cloud Function to pass the data to tie Python API.
  • D. Use Cloud Composer to subscribe to a Pub/Sub tope and can the Python API.

正解：A


質問 # 146
Which of these is NOT a way to customize the software on Dataproc cluster instances?

  • A. Set initialization actions
  • B. Log into the master node and make changes from there
  • C. Configure the cluster using Cloud Deployment Manager
  • D. Modify configuration files using cluster properties

正解：C


質問 # 147
You are training a spam classifier. You notice that you are overfitting the training data. Which three actions can you take to resolve this problem? (Choose three.)

  • A. Get more training examples
  • B. Use a smaller set of features
  • C. Decrease the regularization parameters
  • D. Use a larger set of features
  • E. Increase the regularization parameters
  • F. Reduce the number of training examples

正解：A、C、D


質問 # 148
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.
MJTelco's Google Cloud Dataflow pipeline is now ready to start receiving data from the 50,000 installations.
You want to allow Cloud Dataflow to scale its compute power up as required. Which Cloud Dataflow pipeline configuration setting should you update?

  • A. The number of workers
  • B. The maximum number of workers
  • C. The disk size per worker
  • D. The zone

正解：D


質問 # 149
You are administering a BigQuery on-demand environment. Your business intelligence tool is submitting hundreds of queries each day that aggregate a large (50 TB) sales history fact table at the day and month levels. These queries have a slow response time and are exceeding cost expectations. You need to decrease response time, lower query costs, and minimize maintenance. What should you do?

  • A. Enable Bl Engine and add your sales table as a preferred table.
  • B. Create a scheduled query to build sales day and sales month aggregate tables on an hourly basis.
  • C. Build materialized views on top of the sales table to aggregate data at the day and month level.
  • D. Build authorized views on top of the sales table to aggregate data at the day and month level.

正解：C

解説：
To improve response times and reduce costs for frequent queries aggregating a large sales history fact table, materialized views are a highly effective solution. Here's why option A is the best choice:
Materialized Views:
Materialized views store the results of a query physically and update them periodically, offering faster query responses for frequently accessed data.
They are designed to improve performance for repetitive and expensive aggregation queries by precomputing the results.
Efficiency and Cost Reduction:
By building materialized views at the day and month level, you significantly reduce the computation required for each query, leading to faster response times and lower query costs.
Materialized views also reduce the need for on-demand query execution, which can be costly when dealing with large datasets.
Minimized Maintenance:
Materialized views in BigQuery are managed automatically, with updates handled by the system, reducing the maintenance burden on your team.
Steps to Implement:
Identify Aggregation Queries:
Analyze the existing queries to identify common aggregation patterns at the day and month levels.
Create Materialized Views:
Create materialized views in BigQuery for the identified aggregation patterns. For example CREATE MATERIALIZED VIEW project.dataset.sales_daily_summary AS SELECT DATE(transaction_time) AS day, SUM(amount) AS total_sales FROM project.dataset.sales GROUP BY day; CREATE MATERIALIZED VIEW project.dataset.sales_monthly_summary AS SELECT EXTRACT(YEAR FROM transaction_time) AS year, EXTRACT(MONTH FROM transaction_time) AS month, SUM(amount) AS total_sales FROM project.dataset.sales GROUP BY year, month; Query Using Materialized Views:
Update existing queries to use the materialized views instead of directly querying the base table.
Reference:
BigQuery Materialized Views
Optimizing Query Performance


質問 # 150
You are creating a new pipeline in Google Cloud to stream IoT data from Cloud Pub/Sub through Cloud Dataflow to BigQuery. While previewing the data, you notice that roughly 2% of the data appears to be corrupt.
You need to modify the Cloud Dataflow pipeline to filter out this corrupt data. What should you do?

  • A. Add a GroupByKey transform in Cloud Dataflow to group all of the valid data together and discard the rest.
  • B. Add a Partition transform in Cloud Dataflow to separate valid data from corrupt data.
  • C. Add a SideInput that returns a Boolean if the element is corrupt.
  • D. Add a ParDo transform in Cloud Dataflow to discard corrupt elements.

正解：D


質問 # 151
You work for a shipping company that uses handheld scanners to read shipping labels. Your company has strict data privacy standards that require scanners to only transmit recipients' personally identifiable information (PII) to analytics systems, which violates user privacy rules. You want to quickly build a scalable solution using cloud-native managed services to prevent exposure of PII to the analytics systems.
What should you do?

  • A. Install a third-party data validation tool on Compute Engine virtual machines to check the incoming data for sensitive information.
  • B. Build a Cloud Function that reads the topics and makes a call to the Cloud Data Loss Prevention API.
    Use the tagging and confidence levels to either pass or quarantine the data in a bucket for review.
  • C. Use Stackdriver logging to analyze the data passed through the total pipeline to identify transactions that may contain sensitive information.
  • D. Create an authorized view in BigQuery to restrict access to tables with sensitive data.

正解：D


質問 # 152
You have an Apache Kafka cluster on-prem with topics containing web application logs. You need to replicate the data to Google Cloud for analysis in BigQuery and Cloud Storage. The preferred replication method is mirroring to avoid deployment of Kafka Connect plugins.
What should you do?

  • A. Deploy a Kafka cluster on GCE VM Instances. Configure your on-prem cluster to mirror your topics to the cluster running in GCE. Use a Dataproc cluster or Dataflow job to read from Kafka and write to GCS.
  • B. Deploy a Kafka cluster on GCE VM Instances with the PubSub Kafka connector configured as a Sink connector. Use a Dataproc cluster or Dataflow job to read from Kafka and write to GCS.
  • C. Deploy the PubSub Kafka connector to your on-prem Kafka cluster and configure PubSub as a Sink connector. Use a Dataflow job to read from PubSub and write to GCS.
  • D. Deploy the PubSub Kafka connector to your on-prem Kafka cluster and configure PubSub as a Source connector. Use a Dataflow job to read from PubSub and write to GCS.

正解：A


質問 # 153
You need (o give new website users a globally unique identifier (GUID) using a service that takes in data points and returns a GUID This data is sourced from both internal and external systems via HTTP calls that you will make via microservices within your pipeline There will be tens of thousands of messages per second and that can be multithreaded, and you worry about the backpressure on the system How should you design your pipeline to minimize that backpressure?

  • A. Create the pipeline statically in the class definition
  • B. Call out to the service via HTTP
  • C. Batch the job into ten-second increments
  • D. Create a new object in the startBundle method of DoFn

正解：B


質問 # 154
Which of the following statements about the Wide & Deep Learning model are true? (Select 2 answers.)

  • A. A good use for the wide and deep model is a recommender system.
  • B. A good use for the wide and deep model is a small-scale linear regression problem.
  • C. The wide model is used for generalization, while the deep model is used for memorization.
  • D. The wide model is used for memorization, while the deep model is used for generalization.

正解：A、D

解説：
Can we teach computers to learn like humans do, by combining the power of memorization and generalization? It's not an easy question to answer, but by jointly training a wide linear model (for memorization) alongside a deep neural network (for generalization), one can combine the strengths of both to bring us one step closer. At Google, we call it Wide & Deep Learning. It's useful for generic large-scale regression and classification problems with sparse inputs (categorical features with a large number of possible feature values), such as recommender systems, search, and ranking problems.
Reference: https://research.googleblog.com/2016/06/wide-deep-learning-better-together-with.html


質問 # 155
Google Cloud Bigtable indexes a single value in each row. This value is called the _______.

  • A. master key
  • B. primary key
  • C. unique key
  • D. row key

正解：D

解説：
Explanation
Cloud Bigtable is a sparsely populated table that can scale to billions of rows and thousands of columns, allowing you to store terabytes or even petabytes of data. A single value in each row is indexed; this value is known as the row key.
Reference: https://cloud.google.com/bigtable/docs/overview


質問 # 156
......

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