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[General] Starting Your Google Professional-Data-Engineer Exam Preparation? Get the Right

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【General】 Starting Your Google Professional-Data-Engineer Exam Preparation? Get the Right

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Google Certified Professional Data Engineer Exam Sample Questions (Q100-Q105):NEW QUESTION # 100
You are designing storage for two relational tables that are part of a 10-TB database on Google Cloud. You want to support transactions that scale horizontally. You also want to optimize data for range queries on nonkey columns. What should you do?
  • A. Use Cloud SQL for storage. Add secondary indexes to support query patterns.
  • B. Use Cloud Spanner for storage. Add secondary indexes to support query patterns.
  • C. Use Cloud SQL for storage. Use Cloud Dataflow to transform data to support query patterns.
  • D. Use Cloud Spanner for storage. Use Cloud Dataflow to transform data to support query patterns.
Answer: D
Explanation:
Reference: https://cloud.google.com/solutions/data-lifecycle-cloud-platform

NEW QUESTION # 101
You are developing an application on Google Cloud that will automatically generate subject labels for users' blog posts. You are under competitive pressure to add this feature quickly, and you have no additional developer resources. No one on your team has experience with machine learning. What should you do?
  • A. Build and train a text classification model using TensorFlow. Deploy the model using a Kubernetes Engine cluster. Call the model from your application and process the results as labels.
  • B. Build and train a text classification model using TensorFlow. Deploy the model using Cloud Machine
    Learning Engine. Call the model from your application and process the results as labels.
  • C. Call the Cloud Natural Language API from your application. Process the generated Entity Analysis as
    labels.
  • D. Call the Cloud Natural Language API from your application. Process the generated Sentiment Analysis as labels.
Answer: D

NEW QUESTION # 102
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. Supervised learning to predict the location of a transaction.
  • C. Unsupervised learning to determine which transactions are most likely to be fraudulent.
  • D. Unsupervised learning to predict the location of a transaction.
  • E. Reinforcement learning to predict the location of a transaction.
  • F. Supervised learning to determine which transactions are most likely to be fraudulent.
Answer: A,B,C

NEW QUESTION # 103
You need to create a data pipeline that copies time-series transaction data so that it can be queried from within BigQuery by your data science team for analysis. Every hour, thousands of transactions are updated with a new status. The size of the intitial dataset is 1.5 PB, and it will grow by 3 TB per day. The data is heavily structured, and your data science team will build machine learning models based on this data. You want to maximize performance and usability for your data science team. Which two strategies should you adopt?
(Choose two.)
  • A. Denormalize the data as must as possible.
  • B. Copy a daily snapshot of transaction data to Cloud Storage and store it as an Avro file. Use BigQuery's support for external data sources to query.
  • C. Preserve the structure of the data as much as possible.
  • D. Develop a data pipeline where status updates are appended to BigQuery instead of updated.
  • E. Use BigQuery UPDATE to further reduce the size of the dataset.
Answer: B,D

NEW QUESTION # 104
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 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. Create an additional table with only the necessary columns.
  • B. Export the data into a Google Sheet for virtualization.
  • 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.
Answer: C

NEW QUESTION # 105
......
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