Professional-Data-Engineer Exam Dumps - Google Professional Data Engineer Exam

Searching for workable clues to ace the Google Professional-Data-Engineer Exam? You’re on the right place! ExamCert has realistic, trusted and authentic exam prep tools to help you achieve your desired credential. ExamCert’s Professional-Data-Engineer PDF Study Guide, Testing Engine and Exam Dumps follow a reliable exam preparation strategy, providing you the most relevant and updated study material that is crafted in an easy to learn format of questions and answers. ExamCert’s study tools aim at simplifying all complex and confusing concepts of the exam and introduce you to the real exam scenario and practice it with the help of its testing engine and real exam dumps

Go to page:

<< First
Prev
1
2
3
4
5
6
7
8
Next
Last >>

Question # 17

You are creating a model to predict housing prices. Due to budget constraints, you must run it on a single resource-constrained virtual machine. Which learning algorithm should you use?

Linear regression

Logistic classification

Recurrent neural network

Feedforward neural network

Full Access

Question # 18

You are deploying 10,000 new Internet of Things devices to collect temperature data in your warehouses globally. You need to process, store and analyze these very large datasets in real time. What should you do?

Send the data to Google Cloud Datastore and then export to BigQuery.

Send the data to Google Cloud Pub/Sub, stream Cloud Pub/Sub to Google Cloud Dataflow, and store the data in Google BigQuery.

Send the data to Cloud Storage and then spin up an Apache Hadoop cluster as needed in Google Cloud Dataproc whenever analysis is required.

Export logs in batch to Google Cloud Storage and then spin up a Google Cloud SQL instance, import the data from Cloud Storage, and run an analysis as needed.

Full Access

Question # 19

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.

Full Access

Question # 20

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.

Full Access

Question # 21

An aerospace company uses a proprietary data format to store its night data. You need to connect this new data source to BigQuery and stream the data into BigQuery. You want to efficiency import the data into BigQuery where consuming as few resources as possible. What should you do?

Use a standard Dataflow pipeline to store the raw data in BigQuery and then transform the format later when the data is used.

Write a shell script that triggers a Cloud Function that performs periodic ETL batch jobs on the new data source

Use Apache Hive to write a Dataproc job that streams the data into BigQuery in CSV format

Use an Apache Beam custom connector to write a Dataflow pipeline that streams the data into BigQuery in Avro format

Full Access

Question # 22

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

Full Access

Question # 23

You work for a manufacturing plant that batches application log files together into a single log file once a day at 2:00 AM. You have written a Google Cloud Dataflow job to process that log file. You need to make sure the log file in processed once per day as inexpensively as possible. What should you do?

Change the processing job to use Google Cloud Dataproc instead.

Manually start the Cloud Dataflow job each morning when you get into the office.

Create a cron job with Google App Engine Cron Service to run the Cloud Dataflow job.

Configure the Cloud Dataflow job as a streaming job so that it processes the log data immediately.

Full Access

Question # 24

You are designing a data mesh on Google Cloud with multiple distinct data engineering teams building data products. The typical data curation design pattern consists of landing files in Cloud Storage, transforming raw data in Cloud Storage and BigQuery datasets. and storing the final curated data product in BigQuery datasets You need to configure Dataplex to ensure that each team can access only the assets needed to build their data products. You also need to ensure that teams can easily share the curated data product. What should you do?

1 Create a single Dataplex virtual lake and create a single zone to contain landing, raw. and curated data.2 Provide each data engineering team access to the virtual lake.

1 Create a single Dataplex virtual lake and create a single zone to contain landing, raw. and curated data. 2 Build separate assets for each data product within the zone.3. Assign permissions to the data engineering teams at the zone level.

1 Create a Dataplex virtual lake for each data product, and create a single zone to contain landing, raw, and curated data.2. Provide the data engineering teams with full access to the virtual lake assigned to their data product.

1 Create a Dataplex virtual lake for each data product, and create multiple zones for landing, raw. and curated data. 2. Provide the data engineering teams with full access to the virtual lake assigned to their data product.

Full Access

Answer:

Explanation:

This option is the best way to configure Dataplex for a data mesh architecture, as it allows each data engineering team to have full ownership and control over their data products, while also enabling easy discovery and sharing of the curated data across the organization12.Â By creating a Dataplex virtual lake for each data product,you can isolate the data assets and resources for each domain, and avoid conflicts and dependencies between different teams3.Â By creating multiple zones for landing, raw, and curated data, you can enforce different security and governance policies for each stage of the data curation process, and ensure that only authorized users can access the data assets45. By providing the data engineering teams with full access to the virtual lake assigned to their data product, you can empower them to manage and monitor their data products, and leverage the Dataplex features such as tagging, quality, and lineage.

Option A is not suitable, as it creates a single point of failure and a bottleneck for the data mesh, and does not allow for fine-grained access control and governance for different data products2.Â Option B is also not suitable, as it does not isolate the data assets and resources for each data product, and assigns permissions at the zone level, which may not reflect the different roles and responsibilities of the data engineering teams34.Â Option C is better than option A and B, but it does not create multiple zones forlanding, raw, and curated data, which may compromise the security and quality of the data products5.Â References:

1: Building a data mesh on Google Cloud using BigQuery and Dataplex | Google Cloud Blog

2: Data Mesh - 7 Effective Practices to Get Started - Confluent

3: Best practices | Dataplex | Google Cloud

4: Secure your lake | Dataplex | Google Cloud

5: Zones | Dataplex | Google Cloud

[6]: Managing a Data Mesh with Dataplex â€“ ROI Training

Go to page: