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Understanding functional and technical aspects of Google Professional Data Engineer Exam Building and operationalizing data processing systems

The following will be discussed here:

Awareness of current state and how to migrate a design to a future state
Transformation
Provisioning resources
Building and operationalizing storage systems
Monitoring pipelines
Integrating with new data sources
Effective use of managed services (Cloud Bigtable, Cloud Spanner, Cloud SQL, BigQuery, Cloud Storage, Cloud Datastore, Cloud Memorystore)
Data cleansing
Testing and quality control

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Google Certified Professional Data Engineer Exam Sample Questions (Q113-Q118):

NEW QUESTION # 113
You are using Google BigQuery as your data warehouse. Your users report that the following simple query is running very slowly, no matter when they run the query:
SELECT country, state, city FROM [myproject:mydataset.mytable] GROUP BY country You check the query plan for the query and see the following output in the Read section of Stage:1:

What is the most likely cause of the delay for this query?

A. Either the state or the city columns in the [myproject:mydataset.mytable] table have too many NULL values
B. Users are running too many concurrent queries in the system
C. Most rows in the [myproject:mydataset.mytable] table have the same value in the country column, causing data skew
D. The [myproject:mydataset.mytable] table has too many partitions

Answer: B

NEW QUESTION # 114
You want to schedule a number of sequential load and transformation jobs Data files will be added to a Cloud Storage bucket by an upstream process There is no fixed schedule for when the new data arrives Next, a Dataproc job is triggered to perform some transformations and write the data to BigQuery. You then need to run additional transformation jobs in BigQuery The transformation jobs are different for every table These jobs might take hours to complete You need to determine the most efficient and maintainable workflow to process hundreds of tables and provide the freshest data to your end users. What should you do?

A. 1 Create an Apache Airflow directed acyclic graph (DAG) in Cloud Composer with sequential tasks by using the Cloud Storage, Dataproc. and BigQuery operators2 Create a separate DAG for each table that needs to go through the pipeline3 Schedule the DAGs to run hourly
B. 1 Create an Apache Airflow directed acyclic graph (DAG) in Cloud Composer with sequential tasks by using the Dataproc and BigQuery operators2 Use a single shared DAG for all tables that need to go through the pipeline.3 Use a Cloud Storage object trigger to launch a Cloud Function that triggers the DAG
C. 1Create an Apache Airflow directed acyclic graph (DAG) in Cloud Composer with sequential tasks by using the Cloud Storage. Dataproc. and BigQuery operators2 Use a single shared DAG for all tables that need to go through the pipeline3 Schedule the DAG to run hourly
D. 1 Create an Apache Airflow directed acyclic graph (DAG) in Cloud Composer with sequential tasks by using the Dataproc and BigQuery operators.2 Create a separate DAG for each table that needs to go through the pipeline3 Use a Cloud Storage object trigger to launch a Cloud Function that triggers the DAG

Answer: D

Explanation:
This option is the most efficient and maintainable workflow for your use case, as it allows you to process each table independently and trigger the DAGs only when new data arrives in the Cloud Storage bucket. By using the Dataproc and BigQuery operators, you can easily orchestrate the load and transformation jobs for each table, and leverage the scalability and performanceof these services12. By creating a separate DAG for each table, you can customize the transformation logic and parameters for each table, and avoid the complexity and overhead of a single shared DAG3. By using a Cloud Storage object trigger, you can launch a Cloud Function that triggers the DAG for thecorresponding table, ensuring that the data is processed as soon as possible and reducing the idle time and cost of running the DAGs on a fixed schedule4 .
Option A is not efficient, as it runs the DAG hourly regardless of the data arrival, and it uses a single shared DAG for all tables, which makes it harder to maintain and debug. Option C is also not efficient, as it runs the DAGs hourly and does not leverage the Cloud Storage object trigger. Option D is not maintainable, as it uses a single shared DAG for all tables, and it does not use the Cloud Storage operator, which can simplify the data ingestion from the bucket. References:
1: Dataproc Operator | Cloud Composer | Google Cloud
2: BigQuery Operator | Cloud Composer | Google Cloud
3: Choose Workflows or Cloud Composer for service orchestration | Workflows | Google Cloud
4: Cloud Storage Object Trigger | Cloud Functions Documentation | Google Cloud
[5]: Triggering DAGs | Cloud Composer | Google Cloud
[6]: Cloud Storage Operator | Cloud Composer | Google Cloud

NEW QUESTION # 115
In order to securely transfer web traffic data from your computer's web browser to the Cloud Dataproc cluster you should use a(n) _____.

A. Special browser
B. FTP connection
C. VPN connection
D. SSH tunnel

Answer: D

Explanation:
To connect to the web interfaces, it is recommended to use an SSH tunnel to create a secure connection to the master node.

NEW QUESTION # 116
You are training a spam classifier. You notice that you are overfitting the training data. Which three actions can you take to resolve this problem? (Choose three.)

A. Use a larger set of features
B. Increase the regularization parameters
C. Decrease the regularization parameters
D. Use a smaller set of features
E. Reduce the number of training examples
F. Get more training examples

Answer: B,D,F

NEW QUESTION # 117
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.
MJTelco is building a custom interface to share data. They have these requirements:
* They need to do aggregations over their petabyte-scale datasets.
* They need to scan specific time range rows with a very fast response time (milliseconds).
Which combination of Google Cloud Platform products should you recommend?

A. Cloud Bigtable and Cloud SQL
B. BigQuery and Cloud Storage
C. Cloud Datastore and Cloud Bigtable
D. BigQuery and Cloud Bigtable

Answer: D

NEW QUESTION # 118
......

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Professional-Data-Engineer Reliable Exam Simulations & Test Professional-Data-Engineer Engine

Understanding functional and technical aspects of Google Professional Data Engineer Exam Building and operationalizing data processing systems

Test Professional-Data-Engineer Engine & Professional-Data-Engineer Study Center

Google Certified Professional Data Engineer Exam Sample Questions (Q113-Q118):

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