RELIABLE PROFESSIONAL-DATA-ENGINEER DUMPS FILES, STUDY PROFESSIONAL-DATA-ENGINEER TOOL

Reliable Professional-Data-Engineer Dumps Files, Study Professional-Data-Engineer Tool

Reliable Professional-Data-Engineer Dumps Files, Study Professional-Data-Engineer Tool

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Google Certified Professional Data Engineer Exam Sample Questions (Q177-Q182):

NEW QUESTION # 177
Cloud Bigtable is a recommended option for storing very large amounts of
____________________________?

  • A. multi-keyed data with very low latency
  • B. single-keyed data with very low latency
  • C. single-keyed data with very high latency
  • D. multi-keyed data with very high latency

Answer: B

Explanation:
Cloud Bigtable is a sparsely populated table that can scale to billions of rows and thousands of columns, allowing you to store terabytes or even petabytes of data. A single value in each row is indexed; this value is known as the row key. Cloud Bigtable is ideal for storing very large amounts of single-keyed data with very low latency. It supports high read and write throughput at low latency, and it is an ideal data source for MapReduce operations.
Reference: https://cloud.google.com/bigtable/docs/overview


NEW QUESTION # 178
Flowlogistic Case Study
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 management has determined that the current Apache Kafka servers cannot handle the data volume for their real-time inventory tracking system. You need to build a new system on Google Cloud Platform (GCP) that will feed the proprietary tracking software. The system must be able to ingest data from a variety of global sources, process and query in real-time, and store the data reliably. Which combination of GCP products should you choose?

  • A. Cloud Load Balancing, Cloud Dataflow, and Cloud Storage
  • B. Cloud Pub/Sub, Cloud SQL, and Cloud Storage
  • C. Cloud Pub/Sub, Cloud Dataflow, and Local SSD
  • D. Cloud Pub/Sub, Cloud Dataflow, and Cloud Storage

Answer: B


NEW QUESTION # 179
You're training a model to predict housing prices based on an available dataset with real estate properties. Your plan is to train a fully connected neural net, and you've discovered that the dataset contains latitude and longtitude of the property. Real estate professionals have told you that the location of the property is highly influential on price, so you'd like to engineer a feature that incorporates this physical dependency.
What should you do?

  • A. Create a numeric column from a feature cross of latitude and longtitude.
  • B. Create a feature cross of latitude and longtitude, bucketize at the minute level and use L1 regularization during optimization.
  • C. Provide latitude and longtitude as input vectors to your neural net.
  • D. Create a feature cross of latitude and longtitude, bucketize it at the minute level and use L2 regularization during optimization.

Answer: A

Explanation:
Feature Crosses:
Feature crosses combine multiple features into a single feature that captures the interaction between them. For location data, a feature cross of latitude and longitude can capture spatial dependencies that affect housing prices.
This approach allows the neural network to learn complex patterns related to geographic location more effectively than using raw latitude and longitude values.
Numerical Representation:
Converting the feature cross into a numeric column simplifies the input for the neural network and can improve the model's ability to learn from the data.
This method ensures that the model can leverage the combined information from both latitude and longitude in a meaningful way.
Model Training:
Using a numeric column for the feature cross helps in regularizing the model and prevents overfitting, which is crucial for achieving good generalization on unseen data.
Reference:
To engineer a feature that incorporates the physical dependency of location on housing prices for a neural network, creating a numeric column from a feature cross of latitude and longitude is the most effective approach. Here's why option B is the best choice:


NEW QUESTION # 180
Which TensorFlow function can you use to configure a categorical column if you don't know all of the possible values for that column?

  • A. categorical_column_with_vocabulary_list
  • B. categorical_column_with_hash_bucket
  • C. categorical_column_with_unknown_values
  • D. sparse_column_with_keys

Answer: B

Explanation:
Explanation
If you know the set of all possible feature values of a column and there are only a few of them, you can use categorical_column_with_vocabulary_list. Each key in the list will get assigned an auto-incremental ID starting from 0.
What if we don't know the set of possible values in advance? Not a problem. We can use categorical_column_with_hash_bucket instead. What will happen is that each possible value in the feature column occupation will be hashed to an integer ID as we encounter them in training.
Reference: https://www.tensorflow.org/tutorials/wide


NEW QUESTION # 181
You are building a model to make clothing recommendations. You know a user's fashion preference is
likely to change over time, so you build a data pipeline to stream new data back to the model as it
becomes available. How should you use this data to train the model?

  • A. Train on the existing data while using the new data as your test set.
  • B. Continuously retrain the model on just the new data.
  • C. Continuously retrain the model on a combination of existing data and the new data.
  • D. Train on the new data while using the existing data as your test set.

Answer: D


NEW QUESTION # 182
......

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