FREE PDF GOOGLE - THE BEST RELIABLE STUDY PROFESSIONAL-DATA-ENGINEER QUESTIONS

Free PDF Google - The Best Reliable Study Professional-Data-Engineer Questions

Free PDF Google - The Best Reliable Study Professional-Data-Engineer Questions

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Google Professional-Data-Engineer: Google Certified Professional Data Engineer Exam is a highly-revered certification exam that is designed to test individuals' ability to design, build, and manage data processing systems. Professionals who pass Professional-Data-Engineer exam are recognized as experts in the field of data engineering and are highly sought after by leading tech companies worldwide. Professional-Data-Engineer exam is intended for individuals who have a deep understanding of data processing systems and possess the skills to design and manage them.

Google Professional-Data-Engineer Certification Exam is a highly sought-after certification in the field of data engineering. Google Certified Professional Data Engineer Exam certification is designed for professionals who possess the necessary skills and knowledge to design, build, and maintain data processing systems. Professional-Data-Engineer exam is intended to test the candidate's ability to work with large volumes of data, design scalable data processing systems, and implement solutions that are highly available, reliable, and secure.

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Google Professional-Data-Engineer Certification is a popular certification for professionals in the field of data engineering. Google Certified Professional Data Engineer Exam certification is offered by Google Cloud and is designed to test the knowledge of professionals in designing and building data processing systems, as well as their ability to analyze and use machine learning models. Google Certified Professional Data Engineer Exam certification is gaining popularity among professionals who want to validate their skills and knowledge in this field.

Google Certified Professional Data Engineer Exam Sample Questions (Q35-Q40):

NEW QUESTION # 35
Your new customer has requested daily reports that show their net consumption of Google Cloud compute resources and who used the resources. You need to quickly and efficiently generate these daily reports. What should you do?

  • A. Filter data in Cloud Logging by project, resource, and user; then export the data in CSV format.
  • B. Do daily exports of Cloud Logging data to BigQuery. Create views filtering by project, log type, resource, and user.
  • C. Export Cloud Logging data to Cloud Storage in CSV format. Cleanse the data using Dataprep, filtering by project, resource, and user.
  • D. Filter data in Cloud Logging by project, log type, resource, and user, then import the data into BigQuery.

Answer: A

Explanation:
https://cloud.google.com/logging/docs/view/logs-explorer-interface?cloudshell=true


NEW QUESTION # 36
Which role must be assigned to a service account used by the virtual machines in a Dataproc cluster so they can execute jobs?

  • A. Dataproc Worker
  • B. Dataproc Viewer
  • C. Dataproc Runner
  • D. Dataproc Editor

Answer: A

Explanation:
Service accounts used with Cloud Dataproc must have Dataproc/Dataproc Worker role (or have all the permissions granted by Dataproc Worker role).
Reference: https://cloud.google.com/dataproc/docs/concepts/service-accounts#important_notes


NEW QUESTION # 37
You have a table that contains millions of rows of sales data, partitioned by date Various applications and users query this data many times a minute. The query requires aggregating values by using avg. max. and sum, and does not require joining to other tables. The required aggregations are only computed over the past year of data, though you need to retain full historical data in the base tables You want to ensure that the query results always include the latest data from the tables, while also reducing computation cost, maintenance overhead, and duration. What should you do?

  • A. Create a new table that aggregates the base table data include a filter clause to specify the last year of partitions. Set up a scheduled query to recreate the new table every hour.
  • B. Create a view to aggregate the base table data Include a filter clause to specify the last year of partitions.
  • C. Create a materialized view to aggregate the base table data Configure a partition expiration on the base table to retain only the last one year of partitions.
  • D. Create a materialized view to aggregate the base table data include a filter clause to specify the last one year of partitions.

Answer: A

Explanation:
A materialized view is a database object that contains the results of a query, which can be updated periodically. It can improve the performance and efficiency of queries that involve aggregations, joins, or filters. By creating a materialized view to aggregate the base table data and include a filter clause to specify the last one year of partitions, you can ensure that the query results always include the latest data from the tables, while also reducing computation cost, maintenance overhead, and duration. The materialized view will automatically refresh when the base table data changes, and will only use the partitions that match the filter clause. Option A is incorrect because it will delete the historical data from the base table, which is not desired. Option C is incorrect because it will create a redundant table that needs to be updated manually by a scheduled query, which is more complex and costly than using a materialized view. Option D is incorrect because a view does not store any data, but only references the base table data, which means it will not reduce the computation cost or duration of the query. Reference:
Materialized views, ML models in data warehouse - Google Cloud
Data Engineering with Google Cloud Platform - Packt Subscription


NEW QUESTION # 38
You have data located in BigQuery that is used to generate reports for your company. You have noticed some weekly executive report fields do not correspond to format according to company standards for example, report errors include different telephone formats and different country code identifiers. This is a frequent issue, so you need to create a recurring job to normalize the dat a. You want a quick solution that requires no coding What should you do?

  • A. Use Cloud Data Fusion and Wrangler to normalize the data, and set up a recurring job.
  • B. Use BigQuery and GoogleSQL to normalize the data, and schedule recurring quenes in BigQuery.
  • C. Use Dataflow SQL to create a job that normalizes the data, and that after the first run of the job, schedule the pipeline to execute recurrently.
  • D. Create a Spark job and submit it to Dataproc Serverless.

Answer: A

Explanation:
Cloud Data Fusion is a fully managed, cloud-native data integration service that allows you to build and manage data pipelines with a graphical interface. Wrangler is a feature of Cloud Data Fusion that enables you to interactively explore, clean, and transform data using a spreadsheet-like UI. You can use Wrangler to normalize the data in BigQuery by applying various directives, such as parsing, formatting, replacing, and validating data. You can also preview the results and export the wrangled data to BigQuery or other destinations. You can then set up a recurring job in Cloud Data Fusion to run the Wrangler pipeline on a schedule, such as weekly or daily. This way, you can create a quick and code-free solution to normalize the data for your reports. Reference:
Cloud Data Fusion overview
Wrangler overview
Wrangle data from BigQuery
[Scheduling pipelines]


NEW QUESTION # 39
You are administering a BigQuery on-demand environment. Your business intelligence tool is submitting hundreds of queries each day that aggregate a large (50 TB) sales history fact table at the day and month levels. These queries have a slow response time and are exceeding cost expectations. You need to decrease response time, lower query costs, and minimize maintenance. What should you do?

  • A. Build authorized views on top of the sales table to aggregate data at the day and month level.
  • B. Build materialized views on top of the sales table to aggregate data at the day and month level.
  • C. Create a scheduled query to build sales day and sales month aggregate tables on an hourly basis.
  • D. Enable Bl Engine and add your sales table as a preferred table.

Answer: B

Explanation:
To improve response times and reduce costs for frequent queries aggregating a large sales history fact table, materialized views are a highly effective solution. Here's why option A is the best choice:
Materialized Views:
Materialized views store the results of a query physically and update them periodically, offering faster query responses for frequently accessed data.
They are designed to improve performance for repetitive and expensive aggregation queries by precomputing the results.
Efficiency and Cost Reduction:
By building materialized views at the day and month level, you significantly reduce the computation required for each query, leading to faster response times and lower query costs.
Materialized views also reduce the need for on-demand query execution, which can be costly when dealing with large datasets.
Minimized Maintenance:
Materialized views in BigQuery are managed automatically, with updates handled by the system, reducing the maintenance burden on your team.
Steps to Implement:
Identify Aggregation Queries:
Analyze the existing queries to identify common aggregation patterns at the day and month levels.
Create Materialized Views:
Create materialized views in BigQuery for the identified aggregation patterns. For example CREATE MATERIALIZED VIEW project.dataset.sales_daily_summary AS SELECT DATE(transaction_time) AS day, SUM(amount) AS total_sales FROM project.dataset.sales GROUP BY day; CREATE MATERIALIZED VIEW project.dataset.sales_monthly_summary AS SELECT EXTRACT(YEAR FROM transaction_time) AS year, EXTRACT(MONTH FROM transaction_time) AS month, SUM(amount) AS total_sales FROM project.dataset.sales GROUP BY year, month; Query Using Materialized Views:
Update existing queries to use the materialized views instead of directly querying the base table.
Reference:
BigQuery Materialized Views
Optimizing Query Performance


NEW QUESTION # 40
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