[Mar 07, 2024] Professional-Data-Engineer PDF Dumps is essential on your Professional-Data-Engineer Exam Questions Certain Success! [Q17-Q41]

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[Mar 07, 2024] Professional-Data-Engineer PDF Dumps is essential on your Professional-Data-Engineer Exam Questions Certain Success!

Professional-Data-Engineer PDF Questions – Perfect Prospect To Go With Professional-Data-Engineer Practice Exam

Understanding functional and technical aspects of Google Professional Data Engineer Exam Operationalizing machine learning models

The following will be discussed here:

Leveraging pre-built ML models as a service
Machine learning terminology (e.g., features, labels, models, regression, classification, recommendation, supervised and unsupervised learning, evaluation metrics)
Hardware accelerators (e.g., GPU, TPU)
Ingesting appropriate data
Retraining of machine learning models (Cloud Machine Learning Engine, BigQuery ML, Kubeflow, Spark ML)
Customizing ML APIs (e.g., AutoML Vision, Auto ML text)
Deploying an ML pipeline
Operationalizing machine learning models
Continuous evaluation
Impact of dependencies of machine learning models
ML APIs (e.g., Vision API, Speech API)

Google Certified Professional Data Engineer exam is a certification exam offered by Google for individuals who want to demonstrate their expertise in designing and building data processing systems on the Google Cloud Platform. Professional-Data-Engineer exam is designed to test candidates on their knowledge of data processing systems, machine learning, and data analysis tools on Google Cloud Platform.

NEW QUESTION 17
Your weather app queries a database every 15 minutes to get the current temperature. The frontend is
powered by Google App Engine and server millions of users. How should you design the frontend to
respond to a database failure?

Issue a command to restart the database servers.

Retry the query with exponential backoff, up to a cap of 15 minutes.

Retry the query every second until it comes back online to minimize staleness of data.

Reduce the query frequency to once every hour until the database comes back online.

NEW QUESTION 18
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 data. 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.

NEW QUESTION 19
When running a pipeline that has a BigQuery source, on your local machine, you continue to get permission denied errors. What could be the reason for that?

Your gcloud does not have access to the BigQuery resources

BigQuery cannot be accessed from local machines

You are missing gcloud on your machine

Pipelines cannot be run locally

NEW QUESTION 20
You are building a streaming Dataflow pipeline that ingests noise level data from hundreds of sensors placed near construction sites across a city. The sensors measure noise level every ten seconds, and send that data to the pipeline when levels reach above 70 dBA. You need to detect the average noise level from a sensor when data is received for a duration of more than 30 minutes, but the window ends when no data has been received for 15 minutes What should you do?

Use session windows with a 30-mmute gap duration.

Use tumbling windows with a 15-mmute window and a fifteen-minute. withAllowedLateness operator.

Use session windows with a 15-minute gap duration.

Use hopping windows with a 15-mmute window, and a thirty-minute period.

NEW QUESTION 21
Your company has a hybrid cloud initiative. You have a complex data pipeline that moves data between cloud provider services and leverages services from each of the cloud providers. Which cloud-native service should you use to orchestrate the entire pipeline?

Cloud Dataflow

Cloud Composer

Cloud Dataprep

Cloud Dataproc

NEW QUESTION 22
Which of these numbers are adjusted by a neural network as it learns from a training dataset (select 2 answers)?

Weights

Biases

Continuous features

Input values

NEW QUESTION 23
Your organization has been collecting and analyzing data in Google BigQuery for 6 months. The majority of the data analyzed is placed in a time-partitioned table named events_partitioned. To reduce the cost of queries, your organization created a view called events, which queries only the last 14 days of dat
a. The view is described in legacy SQL. Next month, existing applications will be connecting to BigQuery to read the events data via an ODBC connection. You need to ensure the applications can connect. Which two actions should you take? (Choose two.)

Create a new view over events using standard SQL

Create a new partitioned table using a standard SQL query

Create a new view over events_partitioned using standard SQL

Create a service account for the ODBC connection to use for authentication

Create a Google Cloud Identity and Access Management (Cloud IAM) role for the ODBC connection and shared “events”

NEW QUESTION 24
Case Study: 1 – Flowlogistic
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 wants to use Google BigQuery as their primary analysis system, but they still have Apache Hadoop and Spark workloads that they cannot move to BigQuery. Flowlogistic does not know how to store the data that is common to both workloads. What should they do?

Store the common data in BigQuery as partitioned tables.

Store the common data in BigQuery and expose authorized views.

Store the common data encoded as Avro in Google Cloud Storage.

Store he common data in the HDFS storage for a Google Cloud Dataproc cluster.

NEW QUESTION 25
What are two of the characteristics of using online prediction rather than batch prediction?

It is optimized to handle a high volume of data instances in a job and to run more complex models.

Predictions are returned in the response message.

Predictions are written to output files in a Cloud Storage location that you specify.

It is optimized to minimize the latency of serving predictions.

NEW QUESTION 26
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>.

NEW QUESTION 27
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

NEW QUESTION 28
You have a data pipeline that writes data to Cloud Bigtable using well-designed row keys. You want to monitor your pipeline to determine when to increase the size of you Cloud Bigtable cluster. Which two actions can you take to accomplish this? (Choose two.)

Review Key Visualizer metrics. Increase the size of the Cloud Bigtable cluster when the Read pressure index is above 100.

Review Key Visualizer metrics. Increase the size of the Cloud Bigtable cluster when the Write pressure index is above 100.

Monitor the latency of write operations. Increase the size of the Cloud Bigtable cluster when there is a sustained increase in write latency.

Monitor storage utilization. Increase the size of the Cloud Bigtable cluster when utilization increases above 70% of max capacity.

Monitor latency of read operations. Increase the size of the Cloud Bigtable cluster of read operations take longer than 100 ms.

NEW QUESTION 29
You operate a logistics company, and you want to improve event delivery reliability for vehicle-based sensors. You operate small data centers around the world to capture these events, but leased lines that provide connectivity from your event collection infrastructure to your event processing infrastructure are unreliable, with unpredictable latency. You want to address this issue in the most cost-effective way. What should you do?

Deploy small Kafka clusters in your data centers to buffer events.

Have the data acquisition devices publish data to Cloud Pub/Sub.

Establish a Cloud Interconnect between all remote data centers and Google.

Write a Cloud Dataflow pipeline that aggregates all data in session windows.

NEW QUESTION 30
Which of the following are examples of hyperparameters? (Select 2 answers.)

Number of hidden layers

Number of nodes in each hidden layer

Biases

Weights

NEW QUESTION 31
If a dataset contains rows with individual people and columns for year of birth, country, and income, how many of the columns are continuous and how many are categorical?

1 continuous and 2 categorical

3 categorical

3 continuous

2 continuous and 1 categorical

NEW QUESTION 32
You architect a system to analyze seismic dat
a. Your extract, transform, and load (ETL) process runs as a series of MapReduce jobs on an Apache Hadoop cluster. The ETL process takes days to process a data set because some steps are computationally expensive. Then you discover that a sensor calibration step has been omitted. How should you change your ETL process to carry out sensor calibration systematically in the future?

Modify the transformMapReduce jobs to apply sensor calibration before they do anything else.

Introduce a new MapReduce job to apply sensor calibration to raw data, and ensure all other MapReduce jobs are chained after this.

Add sensor calibration data to the output of the ETL process, and document that all users need to apply sensor calibration themselves.

Develop an algorithm through simulation to predict variance of data output from the last MapReduce job based on calibration factors, and apply the correction to all data.

NEW QUESTION 33
You are implementing security best practices on your data pipeline. Currently, you are manually executing
jobs as the Project Owner. You want to automate these jobs by taking nightly batch files containing non-
public information from Google Cloud Storage, processing them with a Spark Scala job on a Google Cloud
Dataproc cluster, and depositing the results into Google BigQuery.
How should you securely run this workload?

Restrict the Google Cloud Storage bucket so only you can see the files

Grant the Project Owner role to a service account, and run the job with it

Use a service account with the ability to read the batch files and to write to BigQuery

Use a user account with the Project Viewer role on the Cloud Dataproc cluster to read the batch files
and write to BigQuery

NEW QUESTION 34
Your company’s on-premises Apache Hadoop servers are approaching end-of-life, and IT has decided to migrate the cluster to Google Cloud Dataproc. A like-for-like migration of the cluster would require 50 TB of Google Persistent Disk per node. The CIO is concerned about the cost of using that much block storage.
You want to minimize the storage cost of the migration. What should you do?

Put the data into Google Cloud Storage.

Use preemptible virtual machines (VMs) for the Cloud Dataproc cluster.

Tune the Cloud Dataproc cluster so that there is just enough disk for all data.

Migrate some of the cold data into Google Cloud Storage, and keep only the hot data in Persistent Disk.

NEW QUESTION 35
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
10 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?

Cloud Pub/Sub, Cloud Dataflow, and Cloud Storage

Cloud Pub/Sub, Cloud Dataflow, and Local SSD

Cloud Pub/Sub, Cloud SQL, and Cloud Storage

Cloud Load Balancing, Cloud Dataflow, and Cloud Storage

Cloud Dataflow, Cloud SQL, and Cloud Storage

NEW QUESTION 36
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?

Run a local version of Jupiter on the laptop.

Grant the user access to Google Cloud Shell.

Host a visualization tool on a VM on Google Compute Engine.

Deploy Google Cloud Datalab to a virtual machine (VM) on Google Compute Engine.

NEW QUESTION 37
Your company produces 20,000 files every hour. Each data file is formatted as a comma separated values (CSV) file that is less than 4 KB. All files must be ingested on Google Cloud Platform before they can be processed. Your company site has a 200 ms latency to Google Cloud, and your Internet connection bandwidth is limited as 50 Mbps. You currently deploy a secure FTP (SFTP) server on a virtual machine in Google Compute Engine as the data ingestion point. A local SFTP client runs on a dedicated machine to transmit the CSV files as is. The goal is to make reports with data from the previous day available to the executives by 10:00 a.m. each day. This design is barely able to keep up with the current volume, even though the bandwidth utilization is rather low. You are told that due to seasonality, your company expects the number of files to double for the next three months. Which two actions should you take? (choose two.)

Introduce data compression for each file to increase the rate file of file transfer.

Contact your internet service provider (ISP) to increase your maximum bandwidth to at least 100 Mbps.

Redesign the data ingestion process to use gsutil tool to send the CSV files to a storage bucket in parallel.

Assemble 1,000 files into a tape archive (TAR) file. Transmit the TAR files instead, and disassemble the CSV files in the cloud upon receiving them.

Create an S3-compatible storage endpoint in your network, and use Google Cloud Storage Transfer Service to transfer on-premices data to the designated storage bucket.

NEW QUESTION 38
When a Cloud Bigtable node fails, ____ is lost.

all data

no data

the last transaction

the time dimension

NEW QUESTION 39
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?

Rowkey: date#device_idColumn data: data_point

Rowkey: dateColumn data: device_id, data_point

Rowkey: device_idColumn data: date, data_point

Rowkey: data_pointColumn data: device_id, date

Rowkey: date#data_pointColumn data: device_id

NEW QUESTION 40
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

NEW QUESTION 41
You work for an economic consulting firm that helps companies identify economic trends as they happen.
As part of your analysis, you use Google BigQuery to correlate customer data with the average prices of the 100 most common goods sold, including bread, gasoline, milk, and others. The average prices of these goods are updated every 30 minutes. You want to make sure this data stays up to date so you can combine it with other data in BigQuery as cheaply as possible. What should you do?

Load the data every 30 minutes into a new partitioned table in BigQuery.

Store and update the data in a regional Google Cloud Storage bucket and create a federated data source in BigQuery

Store the data in Google Cloud Datastore. Use Google Cloud Dataflow to query BigQuery and combine the data programmatically with the data stored in Cloud Datastore

Store the data in a file in a regional Google Cloud Storage bucket. Use Cloud Dataflow to query BigQuery and combine the data programmatically with the data stored in Google Cloud Storage.

Professional-Data-Engineer Exam with Accurate Google Certified Professional Data Engineer Exam PDF Questions: https://www.prepawaytest.com/Google/Professional-Data-Engineer-practice-exam-dumps.html

Understanding functional and technical aspects of Google Professional Data Engineer Exam Operationalizing machine learning models

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