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Google Certified Professional Data Engineer Exam 認定 Professional-Data-Engineer 試験問題 (Q170-Q175):

質問 # 170
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 try two different sets of features
C. To make sure your model is generalized for more than just the training data
D. So you can use one dataset for a wide model and one for a deep model

正解：C

解説：
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.

質問 # 171
How would you query specific partitions in a BigQuery table?

A. Use the EXTRACT(DAY) clause
B. Use the __PARTITIONTIME pseudo-column in the WHERE clause
C. Use DATE BETWEEN in the WHERE clause
D. Use the DAY column in the WHERE clause

正解：B

解説：
Partitioned tables include a pseudo column named _PARTITIONTIME that contains a date-based timestamp for data loaded into the table. To limit a query to particular partitions (such as Jan 1st and 2nd of 2017), use a clause similar to this:
WHERE _PARTITIONTIME BETWEEN TIMESTAMP('2017-01-01') AND TIMESTAMP('2017-01-02') Reference: https://cloud.google.com/bigquery/docs/partitioned-tables#the_partitiontime_pseudo_column

質問 # 172
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 WHERE date BETWEEN YYYY-MM-DD AND YYYY-MM-DD
B. Use the TABLE_DATE_RANGE function
C. Use SELECT IF.(date >= YYYY-MM-DD AND date <= YYYY-MM-DD
D. Use the WHERE_PARTITIONTIME pseudo column

正解：B

質問 # 173
You migrated your on-premises Apache Hadoop Distributed File System (HDFS) data lake to Cloud Storage. The data scientist team needs to process the data by using Apache Spark and SQL. Security policies need to be enforced at the column level. You need a cost-effective solution that can scale into a data mesh. What should you do?

A. 1 Apply an Identity and Access Management (IAM) policy at the file level in Cloud Storage
2. Define a BigQuery external table for SQL processing.
3. Use Dataproc Spark to process the Cloud Storage files.
B. 1. Define a BigLake table.
2. Create a taxonomy of policy tags in Data Catalog.
3. Add policy lags to columns.
4. Process with the Spark-BigQuery connector or BigQuery SOL.
C. 1. Deploy a long-living Dalaproc cluster with Apache Hive and Ranger enabled.
2. Configure Ranger for column level security.
3. Process with Dataproc Spark or Hive SQL.
D. 1. Load the data to BigQuery tables.
2. Create a taxonomy of policy tags in Data Catalog.
3. Add policy tags to columns.
4. Procoss with the Spark-BigQuery connector or BigQuery SQL.

正解：A

解説：
For automating the CI/CD pipeline of DAGs running in Cloud Composer, the following approach ensures that DAGs are tested and deployed in a streamlined and efficient manner.
Use Cloud Build for Development Instance Testing:
Use Cloud Build to automate the process of copying the DAG code to the Cloud Storage bucket of the development instance.
This triggers Cloud Composer to automatically pick up and test the new DAGs in the development environment.
Testing and Validation:
Ensure that the DAGs run successfully in the development environment.
Validate the functionality and correctness of the DAGs before promoting them to production.
Deploy to Production:
If the DAGs pass all tests in the development environment, use Cloud Build to copy the tested DAG code to the Cloud Storage bucket of the production instance.
This ensures that only validated and tested DAGs are deployed to production, maintaining the stability and reliability of the production environment.
Simplicity and Reliability:
This approach leverages Cloud Build's capabilities for automation and integrates seamlessly with Cloud Composer's reliance on Cloud Storage for DAG storage.
By using Cloud Storage for both development and production deployments, the process remains simple and robust.
Google Data Engineer Reference:
Cloud Composer Documentation
Using Cloud Build
Deploying DAGs to Cloud Composer
Automating DAG Deployment with Cloud Build
By implementing this CI/CD pipeline, you ensure that DAGs are thoroughly tested in the development environment before being automatically deployed to the production environment, maintaining high quality and reliability.
Topic 3, 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.

質問 # 174
Flowlogistic wants to use Google BigQuery as their primary analysis system, but they still have Apache Hadoop and Spark workloads that they cannot move to BigQuery. Flowlogistic does not know how to store the data that is common to both workloads. What should they do?

A. Store the common data in BigQuery as partitioned tables.
B. Store the common data in BigQuery and expose authorized views.
C. Store he common data in the HDFS storage for a Google Cloud Dataproc cluster.
D. Store the common data encoded as Avro in Google Cloud Storage.

正解：B

質問 # 175
......

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最新のGoogle Professional-Data-Engineer受験体験 &合格スムーズProfessional-Data-Engineer合格内容 |便利なProfessional-Data-Engineer受験対策解説集

Professional-Data-Engineer受験体験を読むと、Google Certified Professional Data Engineer Examの半分を合格したことを意味します

Google Certified Professional Data Engineer Exam 認定 Professional-Data-Engineer 試験問題 (Q170-Q175):

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