Interactive GCP-DE Questions - High Pass-Rate Google Valid GCP-DE Exam Question: Data Engineer

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Google GCP-DE Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Ingesting and processing data25%- Transforming data
  • 1. Optimizing transformations for performance
  • 2. Applying data processing logic
  • 3. Managing schema evolution
- Building data pipelines
  • 1. Handling data quality and consistency
  • 2. Developing batch and streaming workflows
  • 3. Implementing ingestion mechanisms
Topic 2: Maintaining and automating data workloads18%- Operationalizing workloads
  • 1. Ensuring reliability and recoverability
  • 2. Monitoring and troubleshooting pipelines
  • 3. Orchestrating and scheduling jobs
- Automation and optimization
  • 1. Automating deployment and management
  • 2. Optimizing resource usage and costs
Topic 3: Preparing data for analysis and machine learning13%- Enabling data analysis
  • 1. Optimizing query performance
  • 2. Implementing data sharing and governance
  • 3. Preparing datasets for querying and reporting
- Preparing data for ML
  • 1. Feature engineering and data preparation
  • 2. Supporting model training and serving
Topic 4: Designing data processing systems24%- Planning data solutions
  • 1. Planning for data security and compliance
  • 2. Designing for reliability, scalability, and efficiency
  • 3. Selecting appropriate storage solutions
- Designing data pipelines
  • 1. Designing for data transformation and enrichment
  • 2. Planning for data migration and integration
  • 3. Defining architecture for batch and streaming processing
Topic 5: Storing and managing data20%- Implementing storage solutions
  • 1. Using data storage services appropriately
  • 2. Managing data lifecycle and retention
  • 3. Designing data warehouses and data lakes
- Optimizing storage performance and cost
  • 1. Implementing cost optimization strategies
  • 2. Configuring storage for access patterns

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Google Data Engineer Sample Questions (Q76-Q81):

NEW QUESTION # 76
Which of these is not a supported method of putting data into a partitioned table?

Answer: A

Explanation:
You cannot change an existing table into a partitioned table. You must create a partitioned table from scratch. Then you can either stream data into it every day and the data will automatically be put in the right partition, or you can load data into a specific partition by using "$YYYYMMDD" at the end of the table name. Reference: https://cloud.google.com/bigquery/docs/partitioned-tables


NEW QUESTION # 77
You work for an economic consulting firm that helps companies identify economic trends as they happen. As part of your analysis, you use Google BigQuery to correlate customer data with the average prices of the 100 most common goods sold, including bread, gasoline, milk, and others. The average prices of these goods are updated every 30 minutes. You want to make sure this data stays up to date so you can combine it with other data in BigQuery as cheaply as possible. What should you do?

Answer: D


NEW QUESTION # 78
Which of these sources can you not load data into BigQuery from?

Answer: C

Explanation:
You can load data into BigQuery from a file upload, Google Cloud Storage, Google Drive, or Google Cloud Bigtable. It is not possible to load data into BigQuery directly from Google Cloud SQL. One way to get data from Cloud SQL to BigQuery would be to export data from Cloud SQL to Cloud Storage and then load it from there.
Reference: https://cloud.google.com/bigquery/loading-data


NEW QUESTION # 79
You are building a new application that you need to collect data from in a scalable way. Data arrives continuously from the application throughout the day, and you expect to generate approximately 150 GB of JSON data per day by the end of the year. Your requirements are: Decoupling producer from consumer Space and cost-efficient storage of the raw ingested data, which is to be stored indefinitely Near real-time SQL query Maintain at least 2 years of historical data, which will be queried with SQ Which pipeline should you use to meet these requirements?

Answer: A


NEW QUESTION # 80
Your company is loading comma-separated values (CSV) files into Google BigQuery. The data is fully imported successfully; however, the imported data is not matching byte-to-byte to the source file. What is the most likely cause of this problem?

Answer: D


NEW QUESTION # 81
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