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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Maintaining and automating data workloads | 18% | - Operationalizing workloads
|
| Topic 2: Designing data processing systems | 24% | - Planning data solutions
|
| Topic 3: Preparing data for analysis and machine learning | 13% | - Preparing data for ML
|
| Topic 4: Storing and managing data | 20% | - Implementing storage solutions
|
| Topic 5: Ingesting and processing data | 25% | - Building data pipelines
|
You are planning to migrate your current on-premises Apache Hadoop deployment to the cloud. You need to ensure that the deployment is as fault-tolerant and cost-effective as possible for long-running batch jobs. You want to use a managed service. What should you do?
Correct Answer: J 🗳️
You architect a system to analyze seismic dat
a. Your extract, transform, and load (ETL) process runs as a series of MapReduce jobs on an Apache Hadoop cluster. The ETL process takes days to process a data set because some steps are computationally expensive. Then you discover that a sensor calibration step has been omitted. How should you change your ETL process to carry out sensor calibration systematically in the future?
Correct Answer: B 🗳️
You launched a new gaming app almost three years ago. You have been uploading log files from the previous day to a separate Google BigQuery table with the table name format LOGS_yyyymmdd. You have been using table wildcard functions to generate daily and monthly reports for all time ranges. Recently, you discovered that some queries that cover long date ranges are exceeding the limit of 1,000 tables and failing. How can you resolve this issue?
Correct Answer: B 🗳️
You are a retailer that wants to integrate your online sales capabilities with different in-home assistants, such as Google Home. You need to interpret customer voice commands and issue an order to the backend systems. Which solutions should you choose?
Correct Answer: B 🗳️
As your organization expands its usage of GCP, many teams have started to create their own projects. Projects are further multiplied to accommodate different stages of deployments and target audiences. Each project requires unique access control configurations. The central IT team needs to have access to all projects. Furthermore, data from Cloud Storage buckets and BigQuery datasets must be shared for use in other projects in an ad hoc way. You want to simplify access control management by minimizing the number of policies. Which two steps should you take? Choose 2 answers.
Correct Answer: A,D 🗳️
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