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Building & Operationalizing Data Processing Systems

Within this subject area, the test takers should show that they know how to build and operationalize storage systems. Specifically, they need to be conversant with effective use of managed services (such as Cloud Bigtable, Cloud SQL, Cloud Spanner, BigQuery, Cloud Storage, Cloud Memorystore, Cloud Datastore), storage costs & performance, and lifecycle management of data. The students should also be capable of building as well as operationalizing pipelines, including such technical tasks as data cleansing, transformation, batch & streaming, data acquisition & import, and integrating with new data sources. Apart from that, the candidates need to have sufficient competency to build and operationalize the processing infrastructure. This includes a good comprehension of provisioning resources, adjusting pipelines, monitoring pipelines, as well as testing & quality control.

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Latest Professional-Data-Engineer Exam Questions Vce - Professional-Data-Engineer Certified Questions

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Preparing for the exam requires a combination of hands-on experience with Google Cloud Platform, as well as studying relevant documentation and training materials. Google offers a variety of resources for exam preparation, including online courses, hands-on labs, and practice exams.

Understanding functional and technical aspects of Google Professional Data Engineer Exam Operationalizing machine learning models

The following will be discussed here:

Ingesting appropriate data
Impact of dependencies of machine learning models
Conversational experiences (e.g., Dialogflow)
Distributed vs. single machine
Deploying an ML pipeline
Measuring, monitoring, and troubleshooting machine learning models
Operationalizing machine learning models
Leveraging pre-built ML models as a service
Hardware accelerators (e.g., GPU, TPU)
Machine learning terminology (e.g., features, labels, models, regression, classification, recommendation, supervised and unsupervised learning, evaluation metrics)
Continuous evaluation
Customizing ML APIs (e.g., AutoML Vision, Auto ML text)
Common sources of error (e.g., assumptions about data)

Google Certified Professional Data Engineer Exam Sample Questions (Q185-Q190):

NEW QUESTION # 185
Data Analysts in your company have the Cloud IAM Owner role assigned to them in their projects to allow them to work with multiple GCP products in their projects. Your organization requires that all BigQuery data access logs be retained for 6 months. You need to ensure that only audit personnel in your company can access the data access logs for all projects. What should you do?

A. Export the data access logs via a project-level export sink to a Cloud Storage bucket in the Data Analysts' projects. Restrict access to the Cloud Storage bucket.
B. Export the data access logs via a project-level export sink to a Cloud Storage bucket in a newly created projects for audit logs. Restrict access to the project with the exported logs.
C. Enable data access logs in each Data Analyst's project. Restrict access to Stackdriver Logging via Cloud IAM roles.
D. Export the data access logs via an aggregated export sink to a Cloud Storage bucket in a newly created project for audit logs. Restrict access to the project that contains the exported logs.

Answer: D

NEW QUESTION # 186
You are building a model to make clothing recommendations. You know a user's fashion preference is 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?

A. Continuously retrain the model on just the new data.
B. Continuously retrain the model on a combination of existing data and the new data.
C. Train on the new data while using the existing data as your test set.
D. Train on the existing data while using the new data as your test set.

Answer: D

Explanation:
https://cloud.google.com/automl-tables/docs/prepare

NEW QUESTION # 187
You are migrating your data warehouse to BigQuery. You have migrated all of your data into tables in a dataset. Multiple users from your organization will be using the data. They should only see certain tables based on their team membership. How should you set user permissions?

A. Create authorized views for each team in the same dataset in which the data resides, and assign the users
/groups data viewer access to the authorized views
B. Create authorized views for each team in datasets created for each team. Assign the authorized views data viewer access to the dataset in which the data resides. Assign the users/groups data viewer access to the datasets in which the authorized views reside
C. Create SQL views for each team in the same dataset in which the data resides, and assign the users
/groups data viewer access to the SQL views
D. Assign the users/groups data viewer access at the table level for each table

Answer: D

NEW QUESTION # 188
The data analyst team at your company uses BigQuery for ad-hoc queries and scheduled SQL pipelines in a Google Cloud project with a slot reservation of 2000 slots. However, with the recent introduction of hundreds of new non time-sensitive SQL pipelines, the team is encountering frequent quota errors. You examine the logs and notice that approximately 1500 queries are being triggered concurrently during peak time. You need to resolve the concurrency issue. What should you do?

A. Update SOL pipelines to run as a batch query, and run ad-hoc queries as interactive query jobs.
B. Increase the slot capacity of the project with baseline as 0 and maximum reservation size as 3000.
C. Update SQL pipelines and ad-hoc queries to run as interactive query jobs.
D. Increase the slot capacity of the project with baseline as 2000 and maximum reservation size as 3000.

Answer: A

Explanation:
To resolve the concurrency issue in BigQuery caused by the introduction of hundreds of non-time-sensitive SQL pipelines, the best approach is to differentiate the types of queries based on their urgency and resource requirements. Here's why option C is the best choice:
* SQL Pipelines as Batch Queries:
* Batch queriesin BigQuery are designed for non-time-sensitive operations. They run in a lower priority queue and do not consume slots immediately, which helps to reduce the overall slot consumption during peak times.
* By converting non-time-sensitive SQL pipelines to batch queries, you can significantly alleviate the pressure on slot reservations.
* Ad-Hoc Queries as Interactive Queries:
* Interactive queriesare prioritized to run immediately and are suitable for ad-hoc analysis where users expect quick results.
* Running ad-hoc queries as interactive jobs ensures that analysts can get their results without delay, improving productivity and user satisfaction.
* Concurrency Management:
* This approach helps balance the workload by leveraging BigQuery's ability to handle different types of queries efficiently, reducing the likelihood of encountering quota errors due to slot exhaustion.
Steps to Implement:
* Identify Non-Time-Sensitive Pipelines:
* Review and identify SQL pipelines that are not time-critical and can be executed as batch jobs.
* Update Pipelines to Batch Queries:
* Modify these pipelines to run as batch queries. This can be done by setting the priority of the query job to BATCH.
* Ensure Ad-Hoc Queries are Interactive:
* Ensure that all ad-hoc queries are submitted as interactive jobs, allowing them to run with higher priority and immediate slot allocation.
Reference Links:
* BigQuery Batch Queries
* BigQuery Slot Allocation and Management

NEW QUESTION # 189
You need to compose visualizations for operations teams with the following requirements:
Which approach meets the requirements?

A. Load the data into Google BigQuery tables, write a Google Data Studio 360 report that connects to your data, calculates a metric, and then uses a filter expression to show only suboptimal rows in a table.
B. Load the data into Google BigQuery tables, write Google Apps Script that queries the data, calculates the metric, and shows only suboptimal rows in a table in Google Sheets.
C. Load the data into Google Sheets, use formulas to calculate a metric, and use filters/sorting to show only suboptimal links in a table.
D. Load the data into Google Cloud Datastore tables, write a Google App Engine Application that queries all rows, applies a function to derive the metric, and then renders results in a table using the Google charts and visualization API.

Answer: D

NEW QUESTION # 190
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