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質問 # 135
You want to use a BigQuery table as a data sink. In which writing mode(s) can you use BigQuery as a sink?
- A. BigQuery cannot be used as a sink
- B. Only batch
- C. Both batch and streaming
- D. Only streaming
正解：C
解説：
When you apply a BigQueryIO.Write transform in batch mode to write to a single table, Dataflow invokes a BigQuery load job. When you apply a BigQueryIO.Write transform in streaming mode or in batch mode using a function to specify the destination table, Dataflow uses BigQuery's streaming inserts Reference: https://cloud.google.com/dataflow/model/bigquery-io
質問 # 136
What are two methods that can be used to denormalize tables in BigQuery?
- A. 1) Split table into multiple tables; 2) Use a partitioned table
- B. 1) Use nested repeated fields; 2) Use a partitioned table
- C. 1) Join tables into one table; 2) Use nested repeated fields
- D. 1) Use a partitioned table; 2) Join tables into one table
正解：C
解説：
Explanation
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 # 137
You have a table that contains millions of rows of sales data, partitioned by date Various applications and users query this data many times a minute. The query requires aggregating values by using avg. max. and sum, and does not require joining to other tables. The required aggregations are only computed over the past year of data, though you need to retain full historical data in the base tables You want to ensure that the query results always include the latest data from the tables, while also reducing computation cost, maintenance overhead, and duration. What should you do?
- A. Create a view to aggregate the base table data Include a filter clause to specify the last year of partitions.
- B. Create a materialized view to aggregate the base table data Configure a partition expiration on the base table to retain only the last one year of partitions.
- C. Create a new table that aggregates the base table data include a filter clause to specify the last year of partitions. Set up a scheduled query to recreate the new table every hour.
- D. Create a materialized view to aggregate the base table data include a filter clause to specify the last one year of partitions.
正解：C
解説：
A materialized view is a database object that contains the results of a query, which can be updated periodically. It can improve the performance and efficiency of queries that involve aggregations, joins, or filters. By creating a materialized view to aggregate the base table data and include a filter clause to specify the last one year of partitions, you can ensure that the query results always include the latest data from the tables, while also reducing computation cost, maintenance overhead, and duration. The materialized view will automatically refresh when the base table data changes, and will only use the partitions that match the filter clause. Option A is incorrect because it will delete the historical data from the base table, which is not desired. Option C is incorrect because it will create a redundant table that needs to be updated manually by a scheduled query, which is more complex and costly than using a materialized view. Option D is incorrect because a view does not store any data, but only references the base table data, which means it will not reduce the computation cost or duration of the query. Reference:
Materialized views, ML models in data warehouse - Google Cloud
Data Engineering with Google Cloud Platform - Packt Subscription
質問 # 138
You are migrating your data warehouse to BigQuery. You have migrated all of your data into tables in a dataset. Multiple users from your organization will be using the data. They should only see certain tables based on their team membership. How should you set user permissions?
- A. Assign the users/groups data viewer access at the table level for each table
- B. Create authorized views for each team in the same dataset in which the data resides, and assign the users/groups data viewer access to the authorized views
- C. Create SQL views for each team in the same dataset in which the data resides, and assign the users/ groups data viewer access to the SQL views
- D. Create authorized views for each team in datasets created for each team. Assign the authorized views data viewer access to the dataset in which the data resides. Assign the users/groups data viewer access to the datasets in which the authorized views reside
正解：D
解説：
https://cloud.google.com/bigquery/docs/share-access-views#introduction
質問 # 139
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 two.)
- A. Introduce resource hierarchy to leverage access control policy inheritance.
- B. Use Cloud Deployment Manager to automate access provision.
- C. Only use service accounts when sharing data for Cloud Storage buckets and BigQuery datasets.
- D. Create distinct groups for various teams, and specify groups in Cloud IAM policies.
- E. 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.
正解：B、D
質問 # 140
Which of these is not a supported method of putting data into a partitioned table?
- A. Create a partitioned table and stream new records to it every day.
- B. Run a query to get the records for a specific day from an existing table and for the destination table, specify a partitioned table ending with the day in the format "$YYYYMMDD".
- C. If you have existing data in a separate file for each day, then create a partitioned table and upload each file into the appropriate partition.
- D. Use ORDER BY to put a table's rows into chronological order and then change the table's type to
"Partitioned".
正解：D
解説：
You cannot change an existing table into a partitioned table. You must create a partitioned table from scratch. Then you can either stream data into it every day and the data will automatically be put in the right partition, or you can load data into a specific partition by using "$YYYYMMDD" at the end of the table name.
Reference: https://cloud.google.com/bigquery/docs/partitioned-tables
質問 # 141
You work for a manufacturing company that sources up to 750 different components, each from a different supplier. You've collected a labeled dataset that has on average 1000 examples for each unique component. Your team wants to implement an app to help warehouse workers recognize incoming components based on a photo of the component. You want to implement the first working version of this app (as Proof-Of-Concept) within a few working days. What should you do?
- A. Use Cloud Vision API by providing custom labels as recognition hints.
- B. Train your own image recognition model leveraging transfer learning techniques.
- C. Use Cloud Vision AutoML, but reduce your dataset twice.
- D. Use Cloud Vision AutoML with the existing dataset.
正解：D
質問 # 142
You need to migrate a Redis database from an on-premises data center to a Memorystore for Redis instance.
You want to follow Google-recommended practices and perform the migration for minimal cost. time, and effort. What should you do?
- A. Write a shell script to migrate the Redis data, and create a new Memorystore for Redis instance.
- B. Make a secondary instance of the Redis database on a Compute Engine instance, and then perform a live cutover.
- C. Make an RDB backup of the Redis database, use the gsutil utility to copy the RDB file into a Cloud Storage bucket, and then import the RDB tile into the Memorystore for Redis instance.
- D. Create a Dataflow job to road the Redis database from the on-premises data center. and write the data to a Memorystore for Redis instance
正解：C
解説：
The import and export feature uses the native RDB snapshot feature of Redis to import data into or export data out of a Memorystore for Redis instance. The use of the native RDB format prevents lock-in and makes it very easy to move data within Google Cloud or outside of Google Cloud. Import and export uses Cloud Storage buckets to store RDB files. Reference:
https://cloud.google.com/memorystore/docs/redis/import-export-overview
質問 # 143
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. Create an authorized view in BigQuery to restrict access to tables with sensitive data.
- C. 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. - D. Use Stackdriver logging to analyze the data passed through the total pipeline to identify transactions that may contain sensitive information.
正解：C
質問 # 144
You want to archive data in Cloud Storage. Because some data is very sensitive, you want to use the "Trust No One" (TNO) approach to encrypt your data to prevent the cloud provider staff from decrypting your dat
a. What should you do?
- A. Use gcloud kms keys create to create a symmetric key. Then use gcloud kms encrypt to encrypt each archival file with the key. Use gsutil cp to upload each encrypted file to the Cloud Storage bucket. Manually destroy the key previously used for encryption, and rotate the key once and rotate the key once.
- B. Use gcloud kms keys create to create a symmetric key. Then use gcloud kms encrypt to encrypt each archival file with the key and unique additional authenticated data (AAD). Use gsutil cp to upload each encrypted file to the Cloud Storage bucket, and keep the AAD outside of Google Cloud.
- C. Specify customer-supplied encryption key (CSEK) in the .boto configuration file. Use gsutil cp to upload each archival file to the Cloud Storage bucket. Save the CSEK in a different project that only the security team can access.
- D. Specify customer-supplied encryption key (CSEK) in the .boto configuration file. Use gsutil cp to upload each archival file to the Cloud Storage bucket. Save the CSEK in Cloud Memorystore as permanent storage of the secret.
正解：A
質問 # 145
You want to process payment transactions in a point-of-sale application that will run on Google Cloud Platform. Your user base could grow exponentially, but you do not want to manage infrastructure scaling.
Which Google database service should you use?
- A. Cloud Bigtable
- B. Cloud Datastore
- C. BigQuery
- D. Cloud SQL
正解：D
質問 # 146
What are the minimum permissions needed for a service account used with Google Dataproc?
- A. Read and write to Google Cloud Storage; write to Google Cloud Logging
- B. Execute to Google Cloud Storage; write to Google Cloud Logging
- C. Write to Google Cloud Storage; read to Google Cloud Logging
- D. Execute to Google Cloud Storage; execute to Google Cloud Logging
正解：A
解説：
Service accounts authenticate applications running on your virtual machine instances to other Google Cloud Platform services. For example, if you write an application that reads and writes files on Google Cloud Storage, it must first authenticate to the Google Cloud Storage API. At a minimum, service accounts used with Cloud Dataproc need permissions to read and write to Google Cloud Storage, and to write to Google Cloud Logging.
質問 # 147
You issue a new batch job to Dataflow. The job starts successfully, processes a few elements, and then suddenly fails and shuts down. You navigate to the Dataflow monitoring interface where you find errors related to a particular DoFn in your pipeline. What is the most likely cause of the errors?
- A. Graph or pipeline construction
- B. Exceptions in worker code
- C. Insufficient permissions
- D. Job validation
正解：B
解説：
https://cloud.google.com/dataflow/docs/guides/troubleshooting-your-pipeline#detect_an_exception_in_worker_code While your job is running, you might encounter errors or exceptions in your worker code. These errors generally mean that the DoFns in your pipeline code have generated unhandled exceptions, which result in failed tasks in your Dataflow job. Exceptions in user code (for example, your DoFn instances) are reported in the Dataflow monitoring interface.
質問 # 148
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. Clustering to divide the transactions into N categories based on feature similarity.
- B. Unsupervised learning to predict the location of a transaction.
- C. Unsupervised learning to determine which transactions are most likely to be fraudulent.
- D. Reinforcement learning to predict the location of a transaction.
- E. Supervised learning to determine which transactions are most likely to be fraudulent.
- F. Supervised learning to predict the location of a transaction.
正解：A、C、D
質問 # 149
Case Study 2 - MJTelco
Company Overview
MJTelco is a startup that plans to build networks in rapidly growing, underserved markets around the world.
The company has patents for innovative optical communications hardware. Based on these patents, they can create many reliable, high-speed backbone links with inexpensive hardware.
Company Background
Founded by experienced telecom executives, MJTelco uses technologies originally developed to overcome communications challenges in space. Fundamental to their operation, they need to create a distributed data infrastructure that drives real-time analysis and incorporates machine learning to continuously optimize their topologies. Because their hardware is inexpensive, they plan to overdeploy the network allowing them to account for the impact of dynamic regional politics on location availability and cost.
Their management and operations teams are situated all around the globe creating many-to-many relationship between data consumers and provides in their system. After careful consideration, they decided public cloud is the perfect environment to support their needs.
Solution Concept
MJTelco is running a successful proof-of-concept (PoC) project in its labs. They have two primary needs:
* Scale and harden their PoC to support significantly more data flows generated when they ramp to more than 50,000 installations.
* Refine their machine-learning cycles to verify and improve the dynamic models they use to control topology definition.
MJTelco will also use three separate operating environments - development/test, staging, and production - to meet the needs of running experiments, deploying new features, and serving production customers.
Business Requirements
* Scale up their production environment with minimal cost, instantiating resources when and where needed in an unpredictable, distributed telecom user community.
* Ensure security of their proprietary data to protect their leading-edge machine learning and analysis.
* Provide reliable and timely access to data for analysis from distributed research workers
* Maintain isolated environments that support rapid iteration of their machine-learning models without affecting their customers.
Technical Requirements
* Ensure secure and efficient transport and storage of telemetry data
* Rapidly scale instances to support between 10,000 and 100,000 data providers with multiple flows each.
* Allow analysis and presentation against data tables tracking up to 2 years of data storing approximately
100m records/day
* Support rapid iteration of monitoring infrastructure focused on awareness of data pipeline problems both in telemetry flows and in production learning cycles.
CEO Statement
Our business model relies on our patents, analytics and dynamic machine learning. Our inexpensive hardware is organized to be highly reliable, which gives us cost advantages. We need to quickly stabilize our large distributed data pipelines to meet our reliability and capacity commitments.
CTO Statement
Our public cloud services must operate as advertised. We need resources that scale and keep our data secure. We also need environments in which our data scientists can carefully study and quickly adapt our models. Because we rely on automation to process our data, we also need our development and test environments to work as we iterate.
CFO Statement
The project is too large for us to maintain the hardware and software required for the data and analysis.
Also, we cannot afford to staff an operations team to monitor so many data feeds, so we will rely on automation and infrastructure. Google Cloud's machine learning will allow our quantitative researchers to work on our high-value problems instead of problems with our data pipelines.
Given the record streams MJTelco is interested in ingesting per day, they are concerned about the cost of Google BigQuery increasing. MJTelco asks you to provide a design solution. They require a single large data table called tracking_table. Additionally, they want to minimize the cost of daily queries while performing fine-grained analysis of each day's events. They also want to use streaming ingestion. What should you do?
- A. Create a table called tracking_table and include a DATE column.
- B. Create a table called tracking_table with a TIMESTAMP column to represent the day.
- C. Create sharded tables for each day following the pattern tracking_table_YYYYMMDD.
- D. Create a partitioned table called tracking_table and include a TIMESTAMP column.
正解：D
質問 # 150
You are creating a data model in BigQuery that will hold retail transaction data. Your two largest tables, sales_transation_header and sales_transation_line. have a tightly coupled immutable relationship. These tables are rarely modified after load and are frequently joined when queried. You need to model the sales_transation_header and sales_transation_line tables to improve the performance of data analytics queries.
What should you do?
- A. Create a sale_transaction table that holds the sales_transaction_header and sales_transaction_line information as rows, duplicating the sales_transaction_header data for each line.
- B. Create a sal es_transaction table that Stores the sales_tran3action_header and sales_transaction_line data as a JSON data type.
- C. Create separate sales_transation_header and sales_transation_line tables and. when querying, specify the sales transition line first in the WHERE clause.
- D. Create a sale3_transaction table that holds the sales_transaction_header information as rows and the sales_transaction_line rows as nested and repeated fields.
正解：D
解説：
BigQuery supports nested and repeated fields, which are complex data types that can represent hierarchical and one-to-many relationships within a single table. By using nested and repeated fields, you can denormalize your data model and reduce the number of joins required for your queries. This can improve the performance and efficiency of your data analytics queries, as joins can be expensive and require shuffling data across nodes.
Nested and repeated fields also preserve the data integrity and avoid data duplication. In this scenario, the sales_transaction_header and sales_transaction_line tables have a tightly coupled immutable relationship, meaning that each header row corresponds to one or more line rows, and the data is rarely modified after load.
Therefore, it makes sense to create a single sales_transaction table that holds the sales_transaction_header information as rows and the sales_transaction_line rows as nested and repeated fields. This way, you can query the sales transaction data without joining two tables, and use dot notation or array functions to access the nested and repeated fields. For example, the sales_transaction table could have the following schema:
Table
Field name
Type
Mode
id
INTEGER
NULLABLE
order_time
TIMESTAMP
NULLABLE
customer_id
INTEGER
NULLABLE
line_items
RECORD
REPEATED
line_items.sku
STRING
NULLABLE
line_items.quantity
INTEGER
NULLABLE
line_items.price
FLOAT
NULLABLE
To query the total amount of each order, you could use the following SQL statement:
SQL
SELECT id, SUM(line_items.quantity * line_items.price) AS total_amount
FROM sales_transaction
GROUP BY id;
AI-generated code. Review and use carefully. More info on FAQ.
References:
* Use nested and repeated fields
* BigQuery explained: Working with joins, nested & repeated data
* Arrays in BigQuery - How to improve query performance and optimise storage
質問 # 151
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