[2024] Professional-Data-Engineer Actual Exam Dumps, Professional-Data-Engineer Practice Test [Q98-Q112]

4/5 - (1 vote)

[2024] Professional-Data-Engineer Actual Exam Dumps, Professional-Data-Engineer Practice Test

TestBraindump Professional-Data-Engineer dumps & Google Cloud Certified sure practice dumps

Google Professional-Data-Engineer exam is designed to test an individual’s ability to design, build, and maintain data processing systems on Google Cloud Platform. Professional-Data-Engineer exam is intended for data engineers, developers, and other IT professionals who are responsible for designing and implementing data solutions on Google Cloud Platform. Professional-Data-Engineer exam covers a broad range of topics, including data processing, data warehousing, data analysis, and machine learning.

Google Professional-Data-Engineer certification is highly respected in the industry, and it can open up new career opportunities for individuals who hold it. Google Cloud Platform is one of the leading cloud computing platforms, and companies across different industries are increasingly adopting it. Professionals who are certified in Google Cloud Platform technologies are in high demand, and they can earn competitive salaries. Therefore, passing the Professional-Data-Engineer exam is a worthwhile investment for individuals who want to advance their careers in the field of data engineering.

NO.98 You are developing a software application using Google’s Dataflow SDK, and want to use conditional, for loops and other complex programming structures to create a branching pipeline. Which component will be used for the data processing operation?

PCollection

Transform

Pipeline

Sink API

NO.99 You are designing a basket abandonment system for an ecommerce company. The system will send a message to a user based on these rules:
No interaction by the user on the site for 1 hour

Has added more than $30 worth of products to the basket

Has not completed a transaction

You use Google Cloud Dataflow to process the data and decide if a message should be sent. How should you design the pipeline?

Use a fixed-time window with a duration of 60 minutes.

Use a sliding time window with a duration of 60 minutes.

Use a session window with a gap time duration of 60 minutes.

Use a global window with a time based trigger with a delay of 60 minutes.

NO.100 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>.

NO.101 Your infrastructure includes a set of YouTube channels. You have been tasked with creating a process for sending the YouTube channel data to Google Cloud for analysis. You want to design a solution that allows your world-wide marketing teams to perform ANSI SQL and other types of analysis on up-to-date YouTube channels log dat
a. How should you set up the log data transfer into Google Cloud?

Use Storage Transfer Service to transfer the offsite backup files to a Cloud Storage Multi-Regional storage bucket as a final destination.

Use Storage Transfer Service to transfer the offsite backup files to a Cloud Storage Regional bucket as a final destination.

Use BigQuery Data Transfer Service to transfer the offsite backup files to a Cloud Storage Multi-Regional storage bucket as a final destination.

Use BigQuery Data Transfer Service to transfer the offsite backup files to a Cloud Storage Regional

NO.102 You are building a model to make clothing recommendations. You know a user’s fashion pis 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.

NO.103 You stream order data by using a Dataflow pipeline, and write the aggregated result to Memorystore. You provisioned a Memorystore for Redis instance with Basic Tier. 4 GB capacity, which is used by 40 clients for read-only access. You are expecting the number of read-only clients to increase significantly to a few hundred and you need to be able to support the demand. You want to ensure that read and write access availability is not impacted, and any changes you make can be deployed quickly. What should you do?

Create multiple new Memorystore for Redis instances with Basic Tier (4 GB capacity) Modify the Dataflow pipeline and new clients to use all instances

Create a new Memorystore for Redis instance with Standard Tier Set capacity to 4 GB and read replica to No read replicas (high availability only). Delete the old instance.

Create a new Memorystore for Memcached instance Set a minimum of three nodes, and memory per node to 4 GB. Modify the Dataflow pipeline and all clients to use the Memcached instance Delete the old instance.

Create a new Memorystore for Redis instance with Standard Tier Set capacity to 5 GB and create multiple read replicas Delete the old instance.

The Basic Tier of Memorystore for Redis provides a standalone Redis instance that is not replicated and does not support read replicas. This means that it cannot scale horizontally to handle more read requests, and it does not provide high availability or automatic failover. If the number of read-only clients increases significantly, the Basic Tier instance may not be able to handle the demand and may impact the read and write access availability. Therefore, option A is not a good solution, as it would require creating multiple Basic Tier instances and modifying the Dataflow pipeline and the clients to distribute the load among them. This would increase the complexity and the management overhead of the solution.
The Standard Tier of Memorystore for Redis provides a highly available Redis instance that supports replication and read replicas. Replication ensures that the data is backed up in another zone and can fail over automatically in case of a primary node failure. Read replicas allow scaling the read throughput by adding up to five replicas to an instance and using them for read-only queries. The Standard Tier also supports in-transit encryption and maintenance windows. Therefore, option D is the best solution, as it would create a new Standard Tier instance with a higher capacity (5 GB) and multiple read replicas to handle the increased demand. The old instance can be deleted after migrating the data to the new instance.
Option B is not a good solution, as it would create a new Standard Tier instance with the same capacity (4 GB) and no read replicas. This would not improve the read throughput or the availability of the solution. Option C is not a good solution, as it would create a new Memorystore for Memcached instance, which is a different service that uses a different protocol and data model than Redis. This would require changing the code of the Dataflow pipeline and the clients to use the Memcached protocol and data structures, which would take more time and effort than migrating to a new Redis instance. References: Redis tier capabilities | Memorystore for Redis | Google Cloud, Pricing | Memorystore for Redis | Google Cloud, What is Memorystore? | Google Cloud Blog, Working with GCP Memorystore – Simple Talk – Redgate Software

NO.104 When a Cloud Bigtable node fails, ____ is lost.

all data

no data

the last transaction

the time dimension

NO.105 All Google Cloud Bigtable client requests go through a front-end server ______ they are sent to a Cloud Bigtable node.

before

after

only if

once

NO.106 What are two of the benefits of using denormalized data structures in BigQuery?

Reduces the amount of data processed, reduces the amount of storage required

Increases query speed, makes queries simpler

Reduces the amount of storage required, increases query speed

Reduces the amount of data processed, increases query speed

NO.107 Your company uses a proprietary system to send inventory data every 6 hours to a data ingestion service in the cloud. Transmitted data includes a payload of several fields and the timestamp of the transmission. If there are any concerns about a transmission, the system re-transmits the data. How should you deduplicate the data most efficiency?

Assign global unique identifiers (GUID) to each data entry.

Compute the hash value of each data entry, and compare it with all historical data.

Store each data entry as the primary key in a separate database and apply an index.

Maintain a database table to store the hash value and other metadata for each data entry.

NO.108 If you want to create a machine learning model that predicts the price of a particular stock based on its recent price history, what type of estimator should you use?

Unsupervised learning

Regressor

Classifier

Clustering estimator

NO.109 You need to migrate a Redis database from an on-premises data center to a Memorystore for Redis instance. You want to follow Google-recommended practices and perform the migration for minimal cost. time, and effort. What should you do?

Make a secondary instance of the Redis database on a Compute Engine instance, and then perform a live cutover.

Write a shell script to migrate the Redis data, and create a new Memorystore for Redis instance.

Create a Dataflow job to road the Redis database from the on-premises data center. and write the data to a Memorystore for Redis instance

Make an RDB backup of the Redis database, use the gsutil utility to copy the RDB file into a Cloud Storage bucket, and then import the RDB tile into the Memorystore for Redis instance.

NO.110 You have uploaded 5 years of log data to Cloud Storage A user reported that some data points in the log data are outside of their expected ranges, which indicates errors You need to address this issue and be able to run the process again in the future while keeping the original data for compliance reasons. What should you do?

Import the data from Cloud Storage into BigQuery Create a new BigQuery table, and skip the rows with errors.

Create a Compute Engine instance and create a new copy of the data in Cloud Storage Skip the rows with errors

Create a Cloud Dataflow workflow that reads the data from Cloud Storage, checks for values outside the expected range, sets the value to an appropriate default, and writes the updated records to a new dataset in Cloud Storage

NO.111 You are developing an application on Google Cloud that will automatically generate subject labels for users’ blog posts. You are under competitive pressure to add this feature quickly, and you have no additional developer resources. No one on your team has experience with machine learning. What should you do?

Call the Cloud Natural Language API from your application. Process the generated Entity Analysis as labels.

Call the Cloud Natural Language API from your application. Process the generated Sentiment Analysis as labels.

Build and train a text classification model using TensorFlow. Deploy the model using Cloud Machine Learning Engine. Call the model from your application and process the results as labels.

Build and train a text classification model using TensorFlow. Deploy the model using a Kubernetes Engine cluster. Call the model from your application and process the results as labels.

NO.112 An organization maintains a Google BigQuery dataset that contains tables with user-level datA. They want to expose aggregates of this data to other Google Cloud projects, while still controlling access to the user-level data. Additionally, they need to minimize their overall storage cost and ensure the analysis cost for other projects is assigned to those projects. What should they do?

Create and share an authorized view that provides the aggregate results.

Create and share a new dataset and view that provides the aggregate results.

Create and share a new dataset and table that contains the aggregate results.

Create dataViewer Identity and Access Management (IAM) roles on the dataset to enable sharing.

Professional-Data-Engineer Actual Questions and Braindumps: https://www.testbraindump.com/Professional-Data-Engineer-exam-prep.html

[Jun 13, 2022] Cloud-Digital-Leader Exam Dumps PDF Guaranteed Success with Accurate & Updated Questions [Q22-Q42]

Pass Guaranteed Quiz 2022 Realistic Verified Free Professional-Cloud-Network-Engineer Exam Dumps [Q17-Q40]

[Q118-Q136] Jan-2024 Realistic Professional-Data-Engineer Accurate & Verified Answers As Experienced in the Actual Test!

Leave a Reply Cancel reply

Related Posts

Leave a Reply Cancel reply