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NEW QUESTION 53
Which is the preferred method to use to avoid hotspotting in time series data in Bigtable?
- A. Randomization
- B. Salting
- C. Field promotion
- D. Hashing
By default, prefer field promotion. Field promotion avoids hotspotting in almost all cases, and it tends to make it easier to design a row key that facilitates queries.
NEW QUESTION 54
Cloud Bigtable is Google’s ______ Big Data database service.
- A. mySQL
- B. SQL Server
- C. Relational
- D. NoSQL
Cloud Bigtable is Google’s NoSQL Big Data database service. It is the same database that Google uses for services, such as Search, Analytics, Maps, and Gmail. It is used for requirements that are low latency and high throughput including Internet of Things (IoT), user analytics, and financial data analysis.
NEW QUESTION 55
Your company handles data processing for a number of different clients. Each client prefers to use their own suite of analytics tools, with some allowing direct query access via Google BigQuery. You need to secure the data so that clients cannot see each other’s dat
a. You want to ensure appropriate access to the data. Which three steps should you take? (Choose three.)
- A. Put each client’s BigQuery dataset into a different table.
- B. Load data into a different dataset for each client.
- C. Use the appropriate identity and access management (IAM) roles for each client’s users.
- D. Restrict a client’s dataset to approved users.
- E. Load data into different partitions.
- F. Only allow a service account to access the datasets.
NEW QUESTION 56
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. Cloud BigTable
- B. Cloud SQL
- C. BigQuery
- D. Cloud Datastore
NEW QUESTION 57
You have developed three data processing jobs. One executes a Cloud Dataflow pipeline that transforms data uploaded to Cloud Storage and writes results to BigQuery. The second ingests data from on- premises servers and uploads it to Cloud Storage. The third is a Cloud Dataflow pipeline that gets information from third-party data providers and uploads the information to Cloud Storage. You need to be able to schedule and monitor the execution of these three workflows and manually execute them when needed. What should you do?
- A. Create a Direct Acyclic Graph in Cloud Composer to schedule and monitor the jobs.
- B. Develop an App Engine application to schedule and request the status of the jobs using GCP API calls.
- C. Use Stackdriver Monitoring and set up an alert with a Webhook notification to trigger the jobs.
- D. Set up cron jobs in a Compute Engine instance to schedule and monitor the pipelines using GCP API calls.
NEW QUESTION 58
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