pdf download Google-PDE Professional Data Engineer on Google Cloud Platform exam braindumps

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Test Number : Google-PDE
Test Name : Professional Data Engineer on Google Cloud Platform
Vendor Name : Google
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Google-PDE test Format | Google-PDE Course Contents | Google-PDE Course Outline | Google-PDE test Syllabus | Google-PDE test Objectives


A Professional Data Engineer enables data-driven decision making by collecting, transforming, and publishing data. A Data Engineer should be able to design, build, operationalize, secure, and monitor data processing systems with a particular emphasis on security and compliance; scalability and efficiency; reliability and fidelity; and flexibility and portability. A Data Engineer should also be able to leverage, deploy, and continuously train pre-existing machine learning models.

The Professional Data Engineer test assesses your ability to:
– Design data processing systems
– Build and operationalize data processing systems
– Operationalize machine learning models
– Ensure solution quality

1. Designing data processing systems
1.1 Selecting the appropriate storage technologies. Considerations include:
– Mapping storage systems to business requirements
– Data modeling
– Tradeoffs involving latency, throughput, transactions
– Distributed systems
– Schema design

1.2 Designing data pipelines. Considerations include:
– Data publishing and visualization (e.g., BigQuery)
– Batch and streaming data (e.g., Cloud Dataflow, Cloud Dataproc, Apache Beam, Apache Spark and Hadoop ecosystem, Cloud Pub/Sub, Apache Kafka)
– Online (interactive) vs. batch predictions
– Job automation and orchestration (e.g., Cloud Composer)

1.3 Designing a data processing solution. Considerations include:
– Choice of infrastructure
– System availability and fault tolerance
– Use of distributed systems
– Capacity planning
– Hybrid cloud and edge computing
– Architecture options (e.g., message brokers, message queues, middleware, service-oriented architecture, serverless functions)
– At least once, in-order, and exactly once, etc., event processing

1.4 Migrating data warehousing and data processing. Considerations include:
– Awareness of current state and how to migrate a design to a future state
– Migrating from on-premises to cloud (Data Transfer Service, Transfer Appliance, Cloud Networking)
– Validating a migration

2. Building and operationalizing data processing systems
2.1 Building and operationalizing storage systems. Considerations include:
– Effective use of managed services (Cloud Bigtable, Cloud Spanner, Cloud SQL, BigQuery, Cloud Storage, Cloud Datastore, Cloud Memorystore)
– Storage costs and performance
– Lifecycle management of data

2.2 Building and operationalizing pipelines. Considerations include:
– Data cleansing
– Batch and streaming
– Transformation
– Data acquisition and import
– Integrating with new data sources

2.3 Building and operationalizing processing infrastructure. Considerations include:
– Provisioning resources
– Monitoring pipelines
– Adjusting pipelines
– Testing and quality control

3. Operationalizing machine learning models
3.1 Leveraging pre-built ML models as a service. Considerations include:
– ML APIs (e.g., Vision API, Speech API)
– Customizing ML APIs (e.g., AutoML Vision, Auto ML text)
– Conversational experiences (e.g., Dialogflow)

3.2 Deploying an ML pipeline. Considerations include:
– Ingesting appropriate data
– Retraining of machine learning models (Cloud Machine Learning Engine, BigQuery ML, Kubeflow, Spark ML)
– Continuous evaluation

3.3 Choosing the appropriate training and serving infrastructure. Considerations include:
– Distributed vs. single machine
– Use of edge compute
– Hardware accelerators (e.g., GPU, TPU)

3.4 Measuring, monitoring, and troubleshooting machine learning models. Considerations include:
– Machine learning terminology (e.g., features, labels, models, regression, classification, recommendation, supervised and unsupervised learning, evaluation metrics)
– Impact of dependencies of machine learning models
– Common sources of error (e.g., assumptions about data)

4. Ensuring solution quality
4.1 Designing for security and compliance. Considerations include:
– Identity and access management (e.g., Cloud IAM)
– Data security (encryption, key management)
– Ensuring privacy (e.g., Data Loss Prevention API)
– Legal compliance (e.g., Health Insurance Portability and Accountability Act (HIPAA), Children’s Online Privacy Protection Act (COPPA), FedRAMP, General Data Protection Regulation (GDPR))

4.2 Ensuring scalability and efficiency. Considerations include:
– Building and running test suites
– Pipeline monitoring (e.g., Stackdriver)
– Assessing, troubleshooting, and improving data representations and data processing infrastructure
– Resizing and autoscaling resources

4.3 Ensuring reliability and fidelity. Considerations include:
– Performing data preparation and quality control (e.g., Cloud Dataprep)
– Verification and monitoring
– Planning, executing, and stress testing data recovery (fault tolerance, rerunning failed jobs, performing retrospective re-analysis)
– Choosing between ACID, idempotent, eventually consistent requirements

4.4 Ensuring flexibility and portability. Considerations include:
– Mapping to current and future business requirements
– Designing for data and application portability (e.g., multi-cloud, data residency requirements)
– Data staging, cataloging, and discovery


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Chrome JavaScript timer throttling: Google’s checks demonstrate it saves up to 2 hours’ battery lifestyles | Google-PDE test Cram and Cheatsheet

Google looks set for Chrome 86 to lower how much battery life the browser consumes. it’s already experimenting with blockading unruly ads that drain battery lifestyles and jam up networks and now it’s targeting vigor-hungry JavaScript timers.

To check the JavaScript timing throttling function, clients can down load the existing Chrome from the Canary developer channel and sort into the tackle bar ‘chrome://flags’ and then either search ‘JavaScript’ or classification in ‘chrome://flags/#intensive-wake-up-throttling’. This test environment will throttle JavaScript timers within the historical past.

“When enabled, wake-americafrom DOM Timers are limited to one per minute in a web page that has been hidden for 5 minutes,” Google’s Chromium builders observe.

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The Chromium group have additionally published the consequences of experiments it ran in a doc titled ‘Throttling JavaScript Timers to cut back Battery usage in historical past Tabs’, which explains that web page developers are the usage of these timers for analytics in a means that doesn’t benefit the person and drains the battery on macOS, home windows, and Linux instruments.

“We used Devtools to check out the work performed through widely wide-spread websites within the heritage. They found that a lot of work become performed from JavaScript timers. furthermore, they found that the work done from these JavaScript timers became often now not useful to the consumer when the page changed into backgrounded (eg checking if scroll position modified, reporting logs, analyzing interactions with advertisements).”

So the group guessed that reducing the number of wake-u.s.from JavaScript timers on a backgrounded page could prolong battery lifestyles with out a have an effect on on the consumer, equivalent to inflicting a web page to freeze.

They stated that wake-united statesin background pages are at the moment restricted to at least one wake-up per 2d and about 1% of CPU use. Google’s experiments check limiting these wake-u.s.to one per minute.

The Chromium group ran one scan on Chrome with 36 heritage tabs and the about:clean tab within the foreground, and a second scan with 36 background tabs and YouTube within the foreground taking part in a video in fullscreen. The background tabs protected frequent sites like Twitter, logged in Gmail, Amazon, and most effective purchase.

the usage of a tremendously-specced MacBook seasoned 15-Inch 2018, the community then validated how long it took to drain its battery on Chrome with out a throttling, Chrome with throttling, and Safari 13.1.

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Even with throttling turned on in Chrome, the MacBook battery become drained quicker than Safari in that configuration. however, the throttling puts Chrome a lot nearer to Safari’s have an impact on on battery existence than in the past.

“on the median, throttling JavaScript timers aggressively extends the battery existence via virtually two hours (28%) for a person with many background tabs, when the foreground tab is set:blank,” the Chromium team writes.

within the YouTube tests, throttling JavaScript timers aggressively extended the battery lifestyles by using almost 36 minutes or about 13%. Safari turned into now not proven during this situation.

chrome-js-timer-throttling.pngGoogle proven how long it took to empty its battery on Chrome with out a throttling, Chrome with throttling, and Safari 13.1.

photo: Google

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