Study with Professional-Data-Engineer Actual Test Questions

You are building an application to share financial market data with consumers, who will receive data feeds. Data is collected from the markets in real time. Consumers will receive the data in the following ways:
- Real-time event stream
- ANSI SQL access to real-time stream and historical data
- Batch historical exports
Which solution should you use?

• A. Cloud Dataflow, Cloud SQL, Cloud Spanner
• B. Cloud Pub/Sub, Cloud Dataproc, Cloud SQL
• C. Cloud Dataproc, Cloud Dataflow, BigQuery
• D. Cloud Pub/Sub, Cloud Storage, BigQuery

You are designing storage for very large text files for a data pipeline on Google Cloud. You want to support ANSI SQL queries. You also want to support compression and parallel load from the input locations using Google recommended practices. What should you do?

• A. Compress text files to gzip using the Grid Computing Tools. Use BigQuery for storage and query.
• B. Transform text files to compressed Avro using Cloud Dataflow. Use BigQuery for storage and query.
• C. Compress text files to gzip using the Grid Computing Tools. Use Cloud Storage, and then import into Cloud Bigtable for query.
• D. Transform text files to compressed Avro using Cloud Dataflow. Use Cloud Storage and BigQuery permanent linked tables for query.

A TensorFlow machine learning model on Compute Engine virtual machines (n2-standard -32) takes two days to complete framing. The model has custom TensorFlow operations that must run partially on a CPU. You want to reduce the training time in a cost-effective manner. What should you do?

• A. Change the VM type to e2 standard-32
• B. Train the model using a VM with a GPU hardware accelerator
• C. Change the VM type to n2-highmem-32
• D. Train the model using a VM with a TPU hardware accelerator

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 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.
• B. 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.
• C. Create a materialized view to aggregate the base table data. Include a filter clause to specify the last one year of partitions.
• D. Create a view to aggregate the base table data. Include a filter clause to specify the last year of partitions.

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.
You need to compose visualization for operations teams with the following requirements:
* Telemetry must include data from all 50,000 installations for the most recent 6 weeks (sampling once every minute)
* The report must not be more than 3 hours delayed from live data.
* The actionable report should only show suboptimal links.
* Most suboptimal links should be sorted to the top.
* Suboptimal links can be grouped and filtered by regional geography.
* 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. 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.
• B. Look through the current data and compose a series of charts and tables, one for each possible combination of criteria.
• 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. Look through the current data and compose a small set of generalized charts and tables bound to criteria filters that allow value selection.