Your company uses a proprietary system to send inventory data every 6 hours to a data ingestion service in the cloud. Transmitted data includes a payload of several fields and the timestamp of the transmission. If there are any concerns about a transmission, the system re-transmits the data. How should you deduplicate the data most efficiency?
A. Assign global unique identifiers (GUID) to each data entry.
B. Compute the hash value of each data entry, and compare it with all historical data.
C. Store each data entry as the primary key in a separate database and apply an index.
D. Maintain a database table to store the hash value and other metadata for each data entry.
Your company has hired a new data scientist who wants to perform complicated analyses across very large datasets stored in Google Cloud Storage and in a Cassandra cluster on Google Compute Engine. The scientist primarily wants to create labelled data sets for machine learning projects, along with some visualization tasks. She reports that her laptop is not powerful enough to perform her tasks and it is slowing her down. You want to help her perform her tasks. What should you do?
A. Run a local version of Jupiter on the laptop.
B. Grant the user access to Google Cloud Shell.
C. Host a visualization tool on a VM on Google Compute Engine.
D. Deploy Google Cloud Datalab to a virtual machine (VM) on Google Compute Engine.
You are creating a model to predict housing prices. Due to budget constraints, you must run it on a single resource-constrained virtual machine. Which learning algorithm should you use?
A. Linear regression
B. Logistic classification
C. Recurrent neural network
D. Feedforward neural network
You need to compose visualizations for operations teams with the following requirements:
1.
The report must include telemetry data from all 50,000 installations for the most resent 6 weeks (sampling once every minute).
2.
The report must not be more than 3 hours delayed from live data.
3.
The actionable report should only show suboptimal links.
4.
Most suboptimal links should be sorted to the top.
5.
Suboptimal links can be grouped and filtered by regional geography.
6.
User response time to load the report must be <5 seconds.
Which approach meets the requirements?
A. Load the data into Google Sheets, use formulas to calculate a metric, and use filters/sorting to show only suboptimal links in a table.
B. Load the data into Google BigQuery tables, write Google Apps Script that queries the data, calculates the metric, and shows only suboptimal rows in a table in Google Sheets.
C. Load the data into Google Cloud Datastore tables, write a Google App Engine Application that queries all rows, applies a function to derive the metric, and then renders results in a table using the Google charts and visualization API.
D. Load the data into Google BigQuery tables, write a Google Data Studio 360 report that connects to your data, calculates a metric, and then uses a filter expression to show only suboptimal rows in a table.
You create a new report for your large team in Google Data Studio 360. The report uses Google BigQuery as its data source. It is company policy to ensure employees can view only the data associated with their region, so you create and populate a table for each region. You need to enforce the regional access policy to the data.
Which two actions should you take? (Choose two.)
A. Ensure all the tables are included in global dataset.
B. Ensure each table is included in a dataset for a region.
C. Adjust the settings for each table to allow a related region-based security group view access.
D. Adjust the settings for each view to allow a related region-based security group view access.
E. Adjust the settings for each dataset to allow a related region-based security group view access.
You need to compose visualization for operations teams with the following requirements:
1.
Telemetry must include data from all 50,000 installations for the most recent 6 weeks (sampling once every minute)
2.
The report must not be more than 3 hours delayed from live data.
3.
The actionable report should only show suboptimal links.
4.
Most suboptimal links should be sorted to the top.
5.
Suboptimal links can be grouped and filtered by regional geography.
6.
User response time to load the report must be <5 seconds.
You create a data source to store the last 6 weeks of data, and create visualizations that allow viewers to see multiple date ranges, distinct geographic regions, and unique installation types. You always show the latest data without any changes to your visualizations. You want to avoid creating and updating new visualizations each month.
What should you do?
A. Look through the current data and compose a series of charts and tables, one for each possible combination of criteria.
B. Look through the current data and compose a small set of generalized charts and tables bound to criteria filters that allow value selection.
C. Export the data to a spreadsheet, compose a series of charts and tables, one for each possible combination of criteria, and spread them across multiple tabs.
D. Load the data into relational database tables, write a Google App Engine application that queries all rows, summarizes the data across each criteria, and then renders results using the Google Charts and visualization API.
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 partitioned table called tracking_table and include a TIMESTAMP column.
C. Create sharded tables for each day following the pattern tracking_table_YYYYMMDD.
D. Create a table called tracking_table with a TIMESTAMP column to represent the day.
MJTelco is building a custom interface to share data. They have these requirements:
1.
They need to do aggregations over their petabyte-scale datasets.
2.
They need to scan specific time range rows with a very fast response time (milliseconds). Which combination of Google Cloud Platform products should you recommend?
A. Cloud Datastore and Cloud Bigtable
B. Cloud Bigtable and Cloud SQL
C. BigQuery and Cloud Bigtable
D. BigQuery and Cloud Storage
MJTelco needs you to create a schema in Google Bigtable that will allow for the historical analysis of the last 2 years of records. Each record that comes in is sent every 15 minutes, and contains a unique identifier of the device and a data record. The most common query is for all the data for a given device for a given day.
Which schema should you use?
A. Rowkey: date#device_idColumn data: data_point
B. Rowkey: dateColumn data: device_id, data_point
C. Rowkey: device_idColumn data: date, data_point
D. Rowkey: data_pointColumn data: device_id, date
E. Rowkey: date#data_pointColumn data: device_id
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 zone
B. The number of workers
C. The disk size per worker
D. The maximum number of workers
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