(2022) Professional-Data-Engineer Dumps and Practice Test (270 Questions) [Q97-Q113]

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(2022) Professional-Data-Engineer Dumps and Practice Test (270 Questions)

Guide (New 2022) Actual Google Professional-Data-Engineer Exam Questions

Operationalizing Machine Learning Models

Here the candidates need to demonstrate their expertise in using pre-built Machine Learning models as a service, including Machine Learning APIs (for instance, Speech API, Vision API, etc.), customizing Machine Learning APIs (for instance, Auto ML text, AutoML Vision, etc.), conversational experiences (for instance, Dialogflow). The applicants should also have the skills in deploying the Machine Learning pipeline. This involves the ability to ingest relevant data, perform retraining of machine learning models (BigQuery ML, Cloud Machine Learning Engine, Spark ML, Kubeflow), as well as execute continuous evaluation. Additionally, the students should be able to choose the relevant training & serving infrastructure as well as know how to fulfill measuring, monitoring, and troubleshooting of Machine Learning models.

QUESTION 97
Your company is streaming real-time sensor data from their factory floor into Bigtable and they have
noticed extremely poor performance. How should the row key be redesigned to improve Bigtable
performance on queries that populate real-time dashboards?

Use a row key of the form <timestamp>.

Use a row key of the form <sensorid>.

Use a row key of the form <timestamp>#<sensorid>.

Use a row key of the form >#<sensorid>#<timestamp>.

QUESTION 98
When you design a Google Cloud Bigtable schema it is recommended that you _________.

Avoid schema designs that are based on NoSQL concepts

Create schema designs that are based on a relational database design

Avoid schema designs that require atomicity across rows

Create schema designs that require atomicity across rows

QUESTION 99
Which action can a Cloud Dataproc Viewer perform?

Submit a job.

Create a cluster.

Delete a cluster.

List the jobs.

QUESTION 100
You are responsible for writing your company’s ETL pipelines to run on an Apache Hadoop cluster. The pipeline will require some checkpointing and splitting pipelines. Which method should you use to write the pipelines?

PigLatin using Pig

HiveQL using Hive

Java using MapReduce

Python using MapReduce

QUESTION 101
Which of the following is NOT one of the three main types of triggers that Dataflow supports?

Trigger based on element size in bytes

Trigger that is a combination of other triggers

Trigger based on element count

Trigger based on time

QUESTION 102
Which of these is NOT a way to customize the software on Dataproc cluster instances?

Modify configuration files using cluster properties

Set initialization actions

Configure the cluster using Cloud Deployment Manager

Log into the master node and make changes from there

QUESTION 103
Business owners at your company have given you a database of bank transactions. Each row contains the user ID, transaction type, transaction location, and transaction amount. They ask you to investigate what type of machine learning can be applied to the dat

Which three machine learning applications can you use? (Choose three.)

Supervised learning to determine which transactions are most likely to be fraudulent.

Unsupervised learning to determine which transactions are most likely to be fraudulent.

Clustering to divide the transactions into N categories based on feature similarity.

Supervised learning to predict the location of a transaction.

Reinforcement learning to predict the location of a transaction.

Unsupervised learning to predict the location of a transaction.

QUESTION 104
Which SQL keyword can be used to reduce the number of columns processed by BigQuery?

BETWEEN

WHERE

SELECT

LIMIT

QUESTION 105
Government regulations in the banking industry mandate the protection of client’s personally identifiable information (PII). Your company requires PII to be access controlled encrypted and compliant with major data protection standards In addition to using Cloud Data Loss Prevention (Cloud DIP) you want to follow Google-recommended practices and use service accounts to control access to PII. What should you do?

Assign the required identity and Access Management (IAM) roles to every employee, and create a single service account to access protect resources

Use one service account to access a Cloud SQL database and use separate service accounts for each human user

Use Cloud Storage to comply with major data protection standards. Use one service account shared by all users

Use Cloud Storage to comply with major data protection standards. Use multiple service accounts attached to IAM groups to grant the appropriate access to each group

QUESTION 106
You use BigQuery as your centralized analytics platform. New data is loaded every day, and an ETL pipeline modifies the original data and prepares it for the final users. This ETL pipeline is regularly modified and can generate errors, but sometimes the errors are detected only after 2 weeks. You need to provide a method to recover from these errors, and your backups should be optimized for storage costs. How should you organize your data in BigQuery and store your backups?

Organize your data in a single table, export, and compress and store the BigQuery data in Cloud Storage.

Organize your data in separate tables for each month, and export, compress, and store the data in Cloud Storage.

Organize your data in separate tables for each month, and duplicate your data on a separate dataset in BigQuery.

Organize your data in separate tables for each month, and use snapshot decorators to restore the table to a time prior to the corruption.

QUESTION 107
You have a data pipeline with a Cloud Dataflow job that aggregates and writes time series metrics to Cloud Bigtable. This data feeds a dashboard used by thousands of users across the organization. You need to support additional concurrent users and reduce the amount of time required to write the dat
a. Which two actions should you take? (Choose two.)

Configure your Cloud Dataflow pipeline to use local execution

Increase the maximum number of Cloud Dataflow workers by setting maxNumWorkers in PipelineOptions

Increase the number of nodes in the Cloud Bigtable cluster

Modify your Cloud Dataflow pipeline to use the Flatten transform before writing to Cloud Bigtable

Modify your Cloud Dataflow pipeline to use the CoGroupByKey transform before writing to Cloud Bigtable

QUESTION 108
Which Cloud Dataflow / Beam feature should you use to aggregate data in an unbounded data source every hour based on the time when the data entered the pipeline?

An hourly watermark

An event time trigger

The with Allowed Lateness method

A processing time trigger

QUESTION 109
Case Study: 3,
MJTelco Case Study
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.
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?

The zone

The number of workers

The disk size per worker

The maximum number of workers

QUESTION 110
You are developing an application that uses a recommendation engine on Google Cloud. Your solution should display new videos to customers based on past views. Your solution needs to generate labels for the entities in videos that the customer has viewed. Your design must be able to provide very fast filtering suggestions based on data from other customer preferences on several TB of data. What should you do?

Build and train a complex classification model with Spark MLlib to generate labels and filter the results.
Deploy the models using Cloud Dataproc. Call the model from your application.

Build and train a classification model with Spark MLlib to generate labels. Build and train a second classification model with Spark MLlib to filter results to match customer preferences. Deploy the models using Cloud Dataproc. Call the models from your application.

Build an application that calls the Cloud Video Intelligence API to generate labels. Store data in Cloud Bigtable, and filter the predicted labels to match the user’s viewing history to generate preferences.

Build an application that calls the Cloud Video Intelligence API to generate labels. Store data in Cloud SQL, and join and filter the predicted labels to match the user’s viewing history to generate preferences.

QUESTION 111
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?

Continuously retrain the model on just the new data.

Continuously retrain the model on a combination of existing data and the new data.

Train on the existing data while using the new data as your test set.

Train on the new data while using the existing data as your test set.

QUESTION 112
You are building a new data pipeline to share data between two different types of applications: jobs generators and job runners. Your solution must scale to accommodate increases in usage and must accommodate the addition of new applications without negatively affecting the performance of existing ones. What should you do?

Create an API using App Engine to receive and send messages to the applications

Use a Cloud Pub/Sub topic to publish jobs, and use subscriptions to execute them

Create a table on Cloud SQL, and insert and delete rows with the job information

Create a table on Cloud Spanner, and insert and delete rows with the job information

QUESTION 113
How can you get a neural network to learn about relationships between categories in a categorical feature?

Create a multi-hot column

Create a one-hot column

Create a hash bucket

Create an embedding column

Understanding functional and technical aspects of Google Professional Data Engineer Exam Building and operationalizing data processing systems

The following will be discussed here:

Batch and streaming
Transformation
Lifecycle management of data
Adjusting pipelines
Building and operationalizing processing infrastructure
Testing and quality control
Effective use of managed services (Cloud Bigtable, Cloud Spanner, Cloud SQL, BigQuery, Cloud Storage, Cloud Datastore, Cloud Memorystore)
Migrating from on-premises to cloud (Data Transfer Service, Transfer Appliance, Cloud Networking)
Building and operationalizing data processing systems
Awareness of current state and how to migrate a design to a future state