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| Section | Weight | Objectives |
|---|---|---|
| Ensuring solution quality | 28% | - Reliability and performance
|
| Operationalizing machine learning models | 26% | - ML pipeline integration
|
| Designing data processing systems | 22% | - Batch and streaming data processing design
|
| Building and operationalizing data processing systems | 24% | - Data ingestion and integration
|
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69. Frage
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?
Antwort: C
Begründung:
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. References:
* Materialized views, ML models in data warehouse - Google Cloud
* Data Engineering with Google Cloud Platform - Packt Subscription
70. Frage
Your car factory is pushing machine measurements as messages into a Pub/Sub topic in your Google Cloud project. A Dataflow streaming job. that you wrote with the Apache Beam SDK, reads these messages, sends acknowledgment lo Pub/Sub. applies some custom business logic in a Doffs instance, and writes the result to BigQuery. You want to ensure that if your business logic fails on a message, the message will be sent to a Pub
/Sub topic that you want to monitor for alerting purposes. What should you do?
Antwort: B
Begründung:
To ensure that messages failing to process in your Dataflow job are sent to a Pub/Sub topic for monitoring and alerting, the best approach is to use Pub/Sub's dead-letter topic feature. Here's why option C is the best choice:
Dead-Letter Topic:
Pub/Sub's dead-letter topic feature allows messages that fail to be processed successfully to be redirected to a specified topic. This ensures that these messages are not lost and can be reviewed for debugging and alerting purposes.
Monitoring and Alerting:
By specifying a new Pub/Sub topic as the dead-letter topic, you can use Cloud Monitoring to track metrics such as subscription/dead_letter_message_count, providing visibility into the number of failed messages.
This allows you to set up alerts based on these metrics to notify the appropriate teams when failures occur.
Steps to Implement:
Enable Dead-Letter Topic:
Configure your Pub/Sub pull subscription to enable dead lettering and specify the new Pub/Sub topic for dead- letter messages.
Set Up Monitoring:
Use Cloud Monitoring to monitor the subscription/dead_letter_message_count metric on your pull subscription.
Configure alerts based on this metric to notify the team of any processing failures.
Reference Links:
Pub/Sub Dead Letter Policy
Cloud Monitoring with Pub/Sub
71. Frage
Which of the following statements about the Wide & Deep Learning model are true? (Select
2 answers.)
Antwort: B,D
Begründung:
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
72. Frage
You use a dataset in BigQuery for analysis. You want to provide third-party companies with access to the same dataset. You need to keep the costs of data sharing low and ensure that the data is current. Which solution should you choose?
Antwort: D
Begründung:
By creating an authorized view one assures that the data is current and avoids taking more storage space (and cost) in order to share a dataset. B and D are not cost optimal and C does not guarantee that the data is kept updated.
73. Frage
You need to store and analyze social media postings in Google BigQuery at a rate of 10,000 messages per minute in near real-time. Initially, design the application to use streaming inserts for individual postings. Your application also performs data aggregations right after the streaming inserts. You discover that the queries after streaming inserts do not exhibit strong consistency, and reports from the queries might miss in-flight data. How can you adjust your application design?
Antwort: A
Begründung:
The data is first comes to buffer and then written to Storage. If we are running queries in buffer we will face above mentioned issues. If we wait for the bigquery to write the data to storage then we won't face the issue.
So We need to wait till it's written tio storage
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 toexpand 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.
74. Frage
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