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IntroductionData engineers are responsible for finding trends in data sets and developing algorithms to help make raw data more useful to the enterprise. This IT role requires a significant set of technical skills, including a deep knowledge of SQL database design and multiple programming languages They collect, transform, and visualize data. The Data Engineer designs, builds, maintains, and troubleshoots data processing systems with a particular emphasis on the security, reliability, fault-tolerance,scalability, fidelity, and efficiency of such systems.
Understanding functional and technical aspects of Google Professional Data Engineer Exam Ensuring solution qualityThe following will be discussed here:
Ensuring flexibility and portabilityData security (encryption, key management)Mapping to current and future business requirementsResizing and autoscaling resourcesDesigning for data and application portability (e.g., multi-cloud, data residency requirements)Pipeline monitoring (e.g., Stackdriver)Ensuring privacy (e.g., Data Loss Prevention API)Performing data preparation and quality control (e.g., Cloud Dataprep)Planning, executing, and stress testing data recovery (fault tolerance, rerunning failed jobs, performing retrospective re-analysis)Identity and access management (e.g.,Cloud IAM)Ensuring scalability and efficiencyData staging, cataloging, and discoveryLegal compliance (e.g., Health Insurance Portability and Accountability Act (HIPAA), Children's Online Privacy Protection Act (COPPA), FedRAMP, General Data Protection Regulation (GDPR))Building and running test suitesEnsuring reliability and fidelityGoogle Professional-Data-Engineer Exam Syllabus Topics:TopicDetailsTopic 1Modeling Business Processes for Analysis and OptimizationTopic 2Visualizing Data and Advocating Policy Automation Decision Support Data SummarizationTopic 3Building and Maintaining Data Structures and Databases>> Professional-Data-Engineer Pass4sure Dumps Pdf <<
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Google Certified Professional Data Engineer Exam Sample Questions (Q202-Q207):NEW QUESTION # 202
Your analytics team wants to build a simple statistical model to determine which customers are most likely to work with your company again, based on a few different metrics. They want to run the model on Apache Spark, using data housed in Google Cloud Storage, and you have recommended using Google Cloud Dataproc to execute this job. Testing has shown that this workload can run in approximately 30 minutes on a 15-node cluster, outputting the results into Google BigQuery. The plan is to run this workload weekly.
How should you optimize the cluster for cost?
Answer: B
NEW QUESTION # 203
Your company has hired a new data scientist who wants to perform complicated analyses across very large datasets stored in Google Cloud Storage and in a Cassandra cluster on Google Compute Engine.
The scientist primarily wants to create labelled data sets for machine learning projects, along with some visualization tasks. She reports that her laptop is not powerful enough to perform her tasks and it is slowing her down. You want to help her perform her tasks. What should you do?
Answer: A
NEW QUESTION # 204
You are designing storage for 20 TB of text files as part of deploying a data pipeline on Google Cloud. Your input data is in CSV format. You want to minimize the cost of querying aggregate values for multiple users who will query the data in Cloud Storage with multiple engines. Which storage service and schema design should you use?
Answer: D
NEW QUESTION # 205
You are developing an application on Google Cloud that will automatically generate subject labels for users' blog posts. You are under competitive pressure to add this feature quickly, and you have no additional developer resources. No one on your team has experience with machine learning. What should you do?
labels.D. Build and train a text classification model using TensorFlow. Deploy the model using Cloud Machine
Learning Engine. Call the model from your application and process the results as labels.
Answer: B
NEW QUESTION # 206
Flowlogistic's CEO wants to gain rapid insight into their customer base so his sales team can be better informed in the field. This team is not very technical, so they've purchased a visualization tool to simplify the creation of BigQuery reports. However, they've been overwhelmed by all the data in the table, and are spending a lot of money on queries trying to find the data they need. You want to solve their problem in the most cost-effective way. What should you do?
Answer: B
NEW QUESTION # 207
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