[Aug 30, 2023] Fully Updated Professional-Data-Engineer Dumps - 100% Same Q&A In Your Real Exam
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NEW QUESTION # 124
An online retailer has built their current application on Google App Engine. A new initiative at the company mandates that they extend their application to allow their customers to transact directly via the application.
They need to manage their shopping transactions and analyze combined data from multiple datasets using a business intelligence (BI) tool. They want to use only a single database for this purpose. Which Google Cloud database should they choose?
- A. Cloud BigTable
- B. Cloud Datastore
- C. Cloud SQL
- D. BigQuery
Answer: A
Explanation:
ference: https://cloud.google.com/solutions/business-intelligence/
NEW QUESTION # 125
You want to rebuild your batch pipeline for structured data on Google Cloud You are using PySpark to conduct data transformations at scale, but your pipelines are taking over twelve hours to run To expedite development and pipeline run time, you want to use a serverless tool and SQL syntax You have already moved your raw data into Cloud Storage How should you build the pipeline on Google Cloud while meeting speed and processing requirements?
- A. Ingest your data into BigQuery from Cloud Storage, convert your PySpark commands into BigQuery SQL
queries to transform the data, and then write the transformations to a new table - B. Use Apache Beam Python SDK to build the transformation pipelines, and write the data into BigQuery
- C. Ingest your data into Cloud SQL, convert your PySpark commands into SparkSQL queries to transform the
data, and then use federated queries from BigQuery for machine learning. - D. Convert your PySpark commands into SparkSQL queries to transform the data; and then run your pipeline
on Dataproc to write the data into BigQuery
Answer: D
NEW QUESTION # 126
Google Cloud Bigtable indexes a single value in each row. This value is called the
_______.
- A. row key
- B. master key
- C. unique key
- D. primary key
Answer: A
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.
Reference: https://cloud.google.com/bigtable/docs/overview
NEW QUESTION # 127
You are deploying MariaDB SQL databases on GCE VM Instances and need to configure monitoring and alerting. You want to collect metrics including network connections, disk IO and replication status from MariaDB with minimal development effort and use StackDriver for dashboards and alerts.
What should you do?
- A. Install the StackDriver Logging Agent and configure fluentd in_tail plugin to read MariaDB logs.
- B. Install the StackDriver Agent and configure the MySQL plugin.
- C. Place the MariaDB instances in an Instance Group with a Health Check.
- D. Install the OpenCensus Agent and create a custom metric collection application with a StackDriver exporter.
Answer: A
NEW QUESTION # 128
You are working on a sensitive project involving private user data. You have set up a project on Google Cloud Platform to house your work internally. An external consultant is going to assist with coding a complex transformation in a Google Cloud Dataflow pipeline for your project. How should you maintain users' privacy?
- A. Grant the consultant the Cloud Dataflow Developer role on the project.
- B. Grant the consultant the Viewer role on the project.
- C. Create a service account and allow the consultant to log on with it.
- D. Create an anonymized sample of the data for the consultant to work with in a different project.
Answer: A
Explanation:
A service account is a special type of Google account intended to represent a non-human user that needs to authenticate and be authorized to access data in Google APIs.
https://cloud.google.com/iam/docs/understanding-service-accounts
NEW QUESTION # 129
You are a retailer that wants to integrate your online sales capabilities with different in-home assistants, such as Google Home. You need to interpret customer voice commands and issue an order to the backend systems.
Which solutions should you choose?
- A. Cloud Speech-to-Text API
- B. Cloud AutoML Natural Language
- C. Cloud Natural Language API
- D. Dialogflow Enterprise Edition
Answer: B
NEW QUESTION # 130
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 Pub/Sub, Cloud Dataflow, and Local SSD
- B. Cloud Pub/Sub, Cloud SQL, and Cloud Storage
- C. Cloud Pub/Sub, Cloud Dataflow, and Cloud Storage
- D. Cloud Load Balancing, Cloud Dataflow, and Cloud Storage
Answer: B
Explanation:
Topic 2, 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.
NEW QUESTION # 131
You are creating a new pipeline in Google Cloud to stream IoT data from Cloud Pub/Sub through Cloud Dataflow to BigQuery. While previewing the data, you notice that roughly 2% of the data appears to be corrupt. You need to modify the Cloud Dataflow pipeline to filter out this corrupt data. What should you do?
- A. Add a Partition transform in Cloud Dataflow to separate valid data from corrupt data.
- B. Add a SideInput that returns a Boolean if the element is corrupt.
- C. Add a GroupByKey transform in Cloud Dataflow to group all of the valid data together and discard the rest.
- D. Add a ParDo transform in Cloud Dataflow to discard corrupt elements.
Answer: D
NEW QUESTION # 132
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.
Answer: D
NEW QUESTION # 133
Why do you need to split a machine learning dataset into training data and test data?
- A. To allow you to create unit tests in your code
- B. So you can use one dataset for a wide model and one for a deep model
- C. So you can try two different sets of features
- D. To make sure your model is generalized for more than just the training data
Answer: D
Explanation:
The flaw with evaluating a predictive model on training data is that it does not inform you on how well the model has generalized to new unseen data. A model that is selected for its accuracy on the training dataset rather than its accuracy on an unseen test dataset is very likely to have lower accuracy on an unseen test dataset. The reason is that the model is not as generalized. It has specialized to the structure in the training dataset. This is called overfitting.
Reference: https://machinelearningmastery.com/a-simple-intuition-for-overfitting/
NEW QUESTION # 134
Which of the following is NOT true about Dataflow pipelines?
- A. Dataflow pipelines can be programmed in Java
- B. Dataflow pipelines use a unified programming model, so can work both with streaming and batch data sources
- C. Dataflow pipelines are tied to Dataflow, and cannot be run on any other runner
- D. Dataflow pipelines can consume data from other Google Cloud services
Answer: C
Explanation:
Dataflow pipelines can also run on alternate runtimes like Spark and Flink, as they are built using the Apache Beam SDKs
NEW QUESTION # 135
You are selecting services to write and transform JSON messages from Cloud Pub/Sub to BigQuery for a data pipeline on Google Cloud. You want to minimize service costs. You also want to monitor and accommodate input data volume that will vary in size with minimal manual intervention. What should you do?
- A. Use Cloud Dataflow to run your transformations. Monitor the total execution time for a sampling of jobs.
Configure the job to use non-default Compute Engine machine types when needed. - B. Use Cloud Dataflow to run your transformations. Monitor the job system lag with Stackdriver. Use the default autoscaling setting for worker instances.
- C. Use Cloud Dataproc to run your transformations. Use the diagnose command to generate an operational output archive. Locate the bottleneck and adjust cluster resources.
- D. Use Cloud Dataproc to run your transformations. Monitor CPU utilization for the cluster. Resize the number of worker nodes in your cluster via the command line.
Answer: B
Explanation:
Dataflow is good with autoscaling and stackdriver to monitor CPU and Storage.
NEW QUESTION # 136
You have enabled the free integration between Firebase Analytics and Google BigQuery. Firebase now
automatically creates a new table daily in BigQuery in the format app_events_YYYYMMDD.You want to
query all of the tables for the past 30 days in legacy SQL. What should you do?
- A. Use the TABLE_DATE_RANGEfunction
- B. Use WHEREdate BETWEEN YYYY-MM-DD AND YYYY-MM-DD
- C. Use the WHERE_PARTITIONTIMEpseudo column
- D. Use SELECT IF.(date >= YYYY-MM-DD AND date <= YYYY-MM-DD
Answer: A
Explanation:
Explanation/Reference:
Reference: https://cloud.google.com/blog/products/gcp/using-bigquery-and-firebase-analytics-to-
understand-your-mobile-app?hl=am
NEW QUESTION # 137
Which of these is not a supported method of putting data into a partitioned table?
- A. Create a partitioned table and stream new records to it every day.
- B. Run a query to get the records for a specific day from an existing table and for the destination table, specify a partitioned table ending with the day in the format "$YYYYMMDD".
- C. Use ORDER BY to put a table's rows into chronological order and then change the table's type to "Partitioned".
- D. If you have existing data in a separate file for each day, then create a partitioned table and upload each file into the appropriate partition.
Answer: C
Explanation:
You cannot change an existing table into a partitioned table. You must create a partitioned table from scratch. Then you can either stream data into it every day and the data will automatically be put in the right partition, or you can load data into a specific partition by using "$YYYYMMDD" at the end of the table name.
NEW QUESTION # 138
When creating a new Cloud Dataproc cluster with the projects.regions.clusters.create operation, these four values are required: project, region, name, and ____.
- A. node
- B. zone
- C. label
- D. type
Answer: B
Explanation:
Explanation
At a minimum, you must specify four values when creating a new cluster with the projects.regions.clusters.create operation:
The project in which the cluster will be created
The region to use
The name of the cluster
The zone in which the cluster will be created
You can specify many more details beyond these minimum requirements. For example, you can also specify the number of workers, whether preemptible compute should be used, and the network settings.
Reference:
https://cloud.google.com/dataproc/docs/tutorials/python-library-example#create_a_new_cloud_dataproc_cluste
NEW QUESTION # 139
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