Aug-2024 Download Free Latest Exam Professional-Data-Engineer Certified Sample Questions [Q158-Q177]

NEW QUESTION 158
An online brokerage company requires a high volume trade processing architecture. You need to create a secure queuing system that triggers jobs. The jobs will run in Google Cloud and cat the company’s Python API to execute trades. You need to efficiently implement a solution. What should you do?

Use Cloud Composer to subscribe to a Pub/Sub tope and can the Python API.

Use a Pub/Sub push subscription to trigger a Cloud Function to pass the data to tie Python API.

Write an application that makes a queue in a NoSQL database

Write an application hosted on a Compute Engine instance that makes a push subscription to the Pub/Sub topic

NEW QUESTION 159
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 160
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.

NEW QUESTION 161
You operate an IoT pipeline built around Apache Kafka that normally receives around 5000 messages per second. You want to use Google Cloud Platform to create an alert as soon as the moving average over 1 hour drops below 4000 messages per second. What should you do?

Consume the stream of data in Cloud Dataflow using Kafka IO. Set a sliding time window of 1 hour every 5 minutes. Compute the average when the window closes, and send an alert if the average is less than 4000 messages.

Consume the stream of data in Cloud Dataflow using Kafka IO. Set a fixed time window of 1 hour. Compute the average when the window closes, and send an alert if the average is less than 4000 messages.

Use Kafka Connect to link your Kafka message queue to Cloud Pub/Sub. Use a Cloud Dataflow template to write your messages from Cloud Pub/Sub to Cloud Bigtable. Use Cloud Scheduler to run a script every hour that counts the number of rows created in Cloud Bigtable in the last hour. If that number falls below
4000, send an alert.

Use Kafka Connect to link your Kafka message queue to Cloud Pub/Sub. Use a Cloud Dataflow template to write your messages from Cloud Pub/Sub to BigQuery. Use Cloud Scheduler to run a script every five minutes that counts the number of rows created in BigQuery in the last hour. If that number falls below
4000, send an alert.

NEW QUESTION 162
You are choosing a NoSQL database to handle telemetry data submitted from millions of Internet-of-
Things (IoT) devices. The volume of data is growing at 100 TB per year, and each data entry has about
100 attributes. The data processing pipeline does not require atomicity, consistency, isolation, and
durability (ACID). However, high availability and low latency are required.
You need to analyze the data by querying against individual fields. Which three databases meet your
requirements? (Choose three.)

Redis

HBase

MySQL

MongoDB

Cassandra

HDFS with Hive

NEW QUESTION 163
What is the HBase Shell for Cloud Bigtable?

The HBase shell is a GUI based interface that performs administrative tasks, such as creating and deleting tables.

The HBase shell is a command-line tool that performs administrative tasks, such as creating and deleting tables.

The HBase shell is a hypervisor based shell that performs administrative tasks, such as creating and deleting new virtualized instances.

The HBase shell is a command-line tool that performs only user account management functions to grant access to Cloud Bigtable instances.

NEW QUESTION 164
In order to securely transfer web traffic data from your computer’s web browser to the Cloud Dataproc cluster you should use a(n) _____.

VPN connection

Special browser

SSH tunnel

FTP connection

NEW QUESTION 165
Which action can a Cloud Dataproc Viewer perform?

Submit a job.

Create a cluster.

Delete a cluster.

List the jobs.

NEW QUESTION 166
Which of the following is NOT true about Dataflow pipelines?

Dataflow pipelines are tied to Dataflow, and cannot be run on any other runner

Dataflow pipelines can consume data from other Google Cloud services

Dataflow pipelines can be programmed in Java

Dataflow pipelines use a unified programming model, so can work both with streaming and batch data sources

NEW QUESTION 167
Which of these are examples of a value in a sparse vector? (Select 2 answers.)

[0, 5, 0, 0, 0, 0]

[0, 0, 0, 1, 0, 0, 1]

[0, 1]

[1, 0, 0, 0, 0, 0, 0]

NEW QUESTION 168
Your company is using WHILECARD tables to query data across multiple tables with similar names. The SQL statement is currently failing with the following error:
# Syntax error : Expected end of statement but got “-” at [4:11] SELECT age FROM bigquery-public-data.noaa_gsod.gsod WHERE age != 99 AND_TABLE_SUFFIX = `1929′ ORDER BY age DESC Which table name will make the SQL statement work correctly?

`bigquery-public-data.noaa_gsod.gsod`

bigquery-public-data.noaa_gsod.gsod*

`bigquery-public-data.noaa_gsod.gsod’*

`bigquery-public-data.noaa_gsod.gsod*`

NEW QUESTION 169
Case Study 2 – MJTelco
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.
MJTelco’s 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

NEW QUESTION 170
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 171
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?

Grant the consultant the Viewer role on the project.

Grant the consultant the Cloud Dataflow Developer role on the project.

Create a service account and allow the consultant to log on with it.

Create an anonymized sample of the data for the consultant to work with in a different project.

NEW QUESTION 172
You have spent a few days loading data from comma-separated values (CSV) files into the Google BigQuery table CLICK_STREAM. The column DTstores the epoch time of click events. For convenience, you chose a simple schema where every field is treated as the STRINGtype. Now, you want to compute web session durations of users who visit your site, and you want to change its data type to the TIMESTAMP. You want to minimize the migration effort without making future queries computationally expensive. What should you do?

Delete the table CLICK_STREAM, and then re-create it such that the column DTis of the TIMESTAMP type. Reload the data.

Add a column TSof the TIMESTAMPtype to the table CLICK_STREAM, and populate the numeric values from the column TSfor each row. Reference the column TSinstead of the column DTfrom now on.

Create a view CLICK_STREAM_V, where strings from the column DTare cast into TIMESTAMPvalues.
Reference the view CLICK_STREAM_Vinstead of the table CLICK_STREAMfrom now on.

Add two columns to the table CLICK STREAM: TSof the TIMESTAMPtype and IS_NEWof the BOOLEANtype. Reload all data in append mode. For each appended row, set the value of IS_NEWto true. For future queries, reference the column TSinstead of the column DT, with the WHEREclause ensuring that the value of IS_NEWmust be true.

Construct a query to return every row of the table CLICK_STREAM, while using the built-in function to cast strings from the column DTinto TIMESTAMPvalues. Run the query into a destination table NEW_CLICK_STREAM, in which the column TSis the TIMESTAMPtype. Reference the table NEW_CLICK_STREAMinstead of the table CLICK_STREAMfrom now on. In the future, new data is loaded into the table NEW_CLICK_STREAM.

NEW QUESTION 173
Which of the following statements is NOT true regarding Bigtable access roles?

Using IAM roles, you cannot give a user access to only one table in a project, rather than all tables in a project.

To give a user access to only one table in a project, grant the user the Bigtable Editor role for that table.

You can configure access control only at the project level.

To give a user access to only one table in a project, you must configure access through your application.

NEW QUESTION 174
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?

BigQuery

Cloud SQL

Cloud BigTable

Cloud Datastore

NEW QUESTION 175
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 176
You need to look at BigQuery data from a specific table multiple times a day. The underlying table you are querying is several petabytes in size, but you want to filter your data and provide simple aggregations to downstream users. You want to run queries faster and get up-to-date insights quicker. What should you do?

Run a scheduled query to pull the necessary data at specific intervals daily.

Create a materialized view based off of the query being run.

Use a cached query to accelerate time to results.

Limit the query columns being pulled in the final result.

NEW QUESTION 177
Which of these statements about exporting data from BigQuery is false?

To export more than 1 GB of data, you need to put a wildcard in the destination filename.

The only supported export destination is Google Cloud Storage.

Data can only be exported in JSON or Avro format.

The only compression option available is GZIP.