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| Section | Weight | Objectives |
|---|---|---|
| Maintaining and automating data workloads (~15% of the exam) | 15% | - Automating data processes
|
| Ingesting and processing the data (~20% of the exam) | 20% | - Performing security considerations
|
| Storing the data (~20% of the exam) | 20% | - Planning for using a data warehouse
|
| Designing data processing systems (~30% of the exam) | 30% | - Selecting appropriate storage technologies
|
| Preparing and using data for analysis (~15% of the exam) | 15% | - Preparing data for visualization
|
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NEW QUESTION # 405
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 visualizations for operations teams with the following requirements:
* The report must include telemetry data from all 50,000 installations for the most resent 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.
Which approach meets the requirements?
Answer: B
NEW QUESTION # 406
Which of these is not a supported method of putting data into a partitioned table?
Answer: B
Explanation:
You cannot change an existing table into a partitioned table. You must create a partitioned table from scratch. Then you can either stream data into it every day and the data will automatically be put in the right partition, or you can load data into a specific partition by using "$YYYYMMDD" at the end of the table name.
NEW QUESTION # 407
You manage your company's BigQuery data warehouse. You need to implement a solution that enables the data science team to modify data for experiments without affecting the original tables, while minimizing additional storage costs. What should you do?
Answer: B
Explanation:
BigQuery Table Clones are specifically designed for the use case where you need a writable copy of a table that is storage-efficient.
* Writable and Independent: Unlike views (which are read-only) or snapshots (which are read-only until restored), a table clone is a lightweight, writable copy. The data science team can perform DML operations (INSERT, UPDATE, DELETE) on the clone without those changes reflecting in the production base table.
* Minimal Storage Costs: Table clones use a copy-on-write mechanism. Initially, the clone consumes zero additional storage because it points to the same underlying physical data blocks as the base table.
You are only billed for the data that differs between the clone and the base table (i.e., new or modified rows in the clone).
* Correcting other options:
* A (Authorized Views): Views do not allow the data science team to modify or "experiment" with the data; they only allow querying of the existing production data.
* B (Snapshots): Snapshots are read-only. To modify them, they must be restored into a standard table, at which point you are charged for the full storage of that restored table, failing the
"minimize storage costs" requirement.
* D (Full Copies): This is the most expensive option as it duplicates all physical data, leading to significantly higher storage costs.
Reference: Google Cloud Documentation on BigQuery Table Clones:
"A table clone is a lightweight, writable copy of another table (called the base table). You are only charged for storage of data in the table clone that differs from the base table, so initially there is no storage cost for a table clone... Common use cases include: Creating sandboxes for users to generate their own analytics and data manipulations, without physically copying all of the production data." (Source: Introduction to table clones)
NEW QUESTION # 408
Your neural network model is taking days to train. You want to increase the training speed. What can you do?
Answer: D
Explanation:
Reference: https://towardsdatascience.com/how-to-increase-the-accuracy-of-a-neural-network-9f5d1c6f407d
NEW QUESTION # 409
Your company built a TensorFlow neural-network model with a large number of neurons and layers. The model fits well for the training data. However, when tested against new data, it performs poorly. What method can you employ to address this?
Answer: B
Explanation:
Reference https://medium.com/mlreview/a-simple-deep-learning-model-for-stock-price-prediction-using- tensorflow-30505541d877
NEW QUESTION # 410
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