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

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| Section | Weight | Objectives |
|---|
| Topic 1: Ingesting and processing data | 25% | - 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 workloads | 18% | - 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 learning | 13% | - 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 systems | 24% | - 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 data | 20% | - 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?
- A. Use ORDER BY to put a table's rows into chronological order and then change the table's type to "Partitioned".
- B. Create a partitioned table and stream new records to it every day.
- C. Run a query to get the records for a specific day from an existing table and for the destination table, specify a partitioned table ending with the day in the format "$YYYYMMDD".
- D. If you have existing data in a separate file for each day, then create a partitioned table and upload each file into the appropriate partition.
Answer: A
Explanation:
You cannot change an existing table into a partitioned table. You must create a partitioned table from scratch. Then you can either stream data into it every day and the data will automatically be put in the right partition, or you can load data into a specific partition by using "$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?
- A. Store the data in a file in a regional Google Cloud Storage bucke
- B. Store and update the data in a regional Google Cloud Storage bucket and create a federated data source in BigQuery
- C. Use Cloud Dataflow to query BigQuery and combine the data programmatically with the data stored in Google Cloud Storage.
- D. Load the data every 30 minutes into a new partitioned table in BigQuery.
- E. Use Google Cloud Dataflow to query BigQuery and combine the data programmatically with the data stored in Cloud Datastore
- F. Store the data in Google Cloud Datastor
Answer: D
NEW QUESTION # 78
Which of these sources can you not load data into BigQuery from?
- A. Google Drive
- B. Google Cloud Storage
- C. Google Cloud SQL
- D. File upload
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?
- A. Create an application that provides an AP
- B. Create an application that publishes events to Cloud Pub/Sub, and create Spark jobs on Cloud Dataproc to convert the JSON data to Avro format, stored on HDFS on Persistent Disk.
- C. Write a tool to poll the API and write data to Cloud Storage as gzipped JSON files.
- D. Set up periodic exports of the database to write to Cloud Storage and load into BigQuery.
- E. Create an application that publishes events to Cloud Pub/Sub, and create a Cloud Dataflow pipeline that transforms the JSON event payloads to Avro, writing the data to Cloud Storage and BigQuery.
- F. Create an application that writes to a Cloud SQL database to store the dat
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?
- A. The CSV data loaded in BigQuery is not flagged as CSV.
- B. The CSV data loaded in BigQuery is not using BigQuery's default encoding.
- C. The CSV data has not gone through an ETL phase before loading into BigQuery.
- D. The CSV data has invalid rows that were skipped on import.
Answer: D
NEW QUESTION # 81
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