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Professional-Data-Engineer PDF DEMO:

QUESTION NO: 1
You designed a database for patient records as a pilot project to cover a few hundred patients in three clinics. Your design used a single database table to represent all patients and their visits, and you used self-joins to generate reports. The server resource utilization was at 50%. Since then, the scope of the project has expanded. The database must now store 100 times more patient records.
You can no longer run the reports, because they either take too long or they encounter errors with insufficient compute resources. How should you adjust the database design?
A. Shard the tables into smaller ones based on date ranges, and only generate reports with prespecified date ranges.
B. Partition the table into smaller tables, with one for each clinic. Run queries against the smaller table pairs, and use unions for consolidated reports.
C. Add capacity (memory and disk space) to the database server by the order of 200.
D. Normalize the master patient-record table into the patient table and the visits table, and create other necessary tables to avoid self-join.
Answer: A

QUESTION NO: 2
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.
You create a new report for your large team in Google Data Studio 360. The report uses Google
BigQuery as its data source. It is company policy to ensure employees can view only the data associated with their region, so you create and populate a table for each region. You need to enforce the regional access policy to the data.
Which two actions should you take? (Choose two.)
A. Ensure all the tables are included in global dataset.
B. Adjust the settings for each table to allow a related region-based security group view access.
C. Adjust the settings for each dataset to allow a related region-based security group view access.
D. Adjust the settings for each view to allow a related region-based security group view access.
E. Ensure each table is included in a dataset for a region.
Answer: D,E

QUESTION NO: 3
You want to use a BigQuery table as a data sink. In which writing mode(s) can you use
BigQuery as a sink?
A. Only batch
B. Only streaming
C. Both batch and streaming
D. BigQuery cannot be used as a sink
Answer: C
Explanation:
When you apply a BigQueryIO.Write transform in batch mode to write to a single table, Dataflow invokes a BigQuery load job. When you apply a BigQueryIO.Write transform in streaming mode or in batch mode using a function to specify the destination table, Dataflow uses BigQuery's streaming inserts Reference: https://cloud.google.com/dataflow/model/bigquery-io

QUESTION NO: 4
An external customer provides you with a daily dump of data from their database. The data flows into Google Cloud Storage GCS as comma-separated values (CSV) files. You want to analyze this data in Google BigQuery, but the data could have rows that are formatted incorrectly or corrupted.
How should you build this pipeline?
A. Run a Google Cloud Dataflow batch pipeline to import the data into BigQuery, and push errors to another dead-letter table for analysis.
B. Enable BigQuery monitoring in Google Stackdriver and create an alert.
C. Use federated data sources, and check data in the SQL query.
D. Import the data into BigQuery using the gcloud CLI and set max_bad_records to 0.
Answer: A

QUESTION NO: 5
You work for a car manufacturer and have set up a data pipeline using Google Cloud Pub/Sub to capture anomalous sensor events. You are using a push subscription in Cloud Pub/Sub that calls a custom HTTPS endpoint that you have created to take action of these anomalous events as they occur. Your custom HTTPS endpoint keeps getting an inordinate amount of duplicate messages. What is the most likely cause of these duplicate messages?
A. Your custom endpoint is not acknowledging messages within the acknowledgement deadline.
B. The message body for the sensor event is too large.
C. The Cloud Pub/Sub topic has too many messages published to it.
D. Your custom endpoint has an out-of-date SSL certificate.
Answer: D

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Updated: Aug 19, 2019

Professional-Data-Engineer試験問題集 & Google Professional-Data-Engineer Exam復習時間

PDF問題と解答

試験コード:Professional-Data-Engineer
試験名称:Professional Data Engineer exam
最近更新時間:2019-08-19
問題と解答:全 185
Google Professional-Data-Engineer 資格認証攻略

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模擬試験

試験コード:Professional-Data-Engineer
試験名称:Professional Data Engineer exam
最近更新時間:2019-08-19
問題と解答:全 185
Google Professional-Data-Engineer テスト参考書

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オンライン版

試験コード:Professional-Data-Engineer
試験名称:Professional Data Engineer exam
最近更新時間:2019-08-19
問題と解答:全 185
Google Professional-Data-Engineer 資格関連題

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Professional-Data-Engineer 模擬試験問題集