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| Section | Weight | Objectives |
|---|
| Topic 1: 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 2: Ingesting and processing data | 25% | - Building data pipelines
- 1. Developing batch and streaming workflows
- 2. Implementing ingestion mechanisms
- 3. Handling data quality and consistency
- Transforming data
- 1. Optimizing transformations for performance
- 2. Applying data processing logic
- 3. Managing schema evolution
|
| Topic 3: Preparing data for analysis and machine learning | 13% | - Preparing data for ML
- 1. Supporting model training and serving
- 2. Feature engineering and data preparation
- Enabling data analysis
- 1. Preparing datasets for querying and reporting
- 2. Optimizing query performance
- 3. Implementing data sharing and governance
|
| Topic 4: Designing data processing systems | 24% | - Designing data pipelines
- 1. Defining architecture for batch and streaming processing
- 2. Planning for data migration and integration
- 3. Designing for data transformation and enrichment
- Planning data solutions
- 1. Designing for reliability, scalability, and efficiency
- 2. Planning for data security and compliance
- 3. Selecting appropriate storage solutions
|
| Topic 5: Storing and managing data | 20% | - Implementing storage solutions
- 1. Managing data lifecycle and retention
- 2. Using data storage services appropriately
- 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 (Q24-Q29):
NEW QUESTION # 24
Cloud Dataproc is a managed Apache Hadoop and Apache service.
- A. Blaze
- B. Ignite
- C. Fire
- D. Spark
Answer: D
Explanation:
Cloud Dataproc is a managed Apache Spark and Apache Hadoop service that lets you use open source data tools for batch processing, querying, streaming, and machine learning.
Reference: https://cloud.google.com/dataproc/docs/
NEW QUESTION # 25
Suppose you have a table that includes a nested column called "city" inside a column called "person", but when you try to submit the following query in BigQuery, it gives you an error.
SELECT person FROM `project1.example.table1` WHERE city = "London"
How would you correct the error?
- A. Change "person" to "person.city".
- B. Add ", UNNEST(city)" before the WHERE clause.
- C. Change "person" to "city.person".
- D. Add ", UNNEST(person)" before the WHERE clause.
Answer: D
Explanation:
To access the person.city column, you need to "UNNEST(person)" and JOIN it to table1 using a comma. Reference:
https://cloud.google.com/bigquery/docs/reference/standard-sql/migrating-from-legacy-sql#nested_repeated_resu
NEW QUESTION # 26
By default, which of the following windowing behavior does Dataflow apply to unbounded data sets?
- A. Windows at every 1 minute
- B. Windows at every 100 MB of data
- C. Single, Global Window
- D. Windows at every 10 minutes
Answer: C
Explanation:
Dataflow's default windowing behavior is to assign all elements of a PCollection to a single, global window, even for unbounded PCollections Reference: https://cloud.google.com/dataflow/model/pcollection
NEW QUESTION # 27
Your company produces 20,000 files every hour. Each data file is formatted as a comma separated values (CSV) file that is less than 4 KB. All files must be ingested on Google Cloud Platform before they can be processed. Your company site has a 200 ms latency to Google Cloud, and your Internet connection bandwidth is limited as 50 Mbps. You currently deploy a secure FTP (SFTP) server on a virtual machine in Google Compute Engine as the data ingestion point. A local SFTP client runs on a dedicated machine to transmit the CSV files as is. The goal is to make reports with data from the previous day available to the executives by 10:00 a.m. each day. This design is barely able to keep up with the current volume, even though the bandwidth utilization is rather low.
You are told that due to seasonality, your company expects the number of files to double for the next three months. Which two actions should you take? (choose two.)
- A. Transmit the TAR files instead, and disassemble the CSV files in the cloud upon receiving them.
- B. Introduce data compression for each file to increase the rate file of file transfer.
- C. Create an S3-compatible storage endpoint in your network, and use Google Cloud Storage Transfer Service to transfer on-premices data to the designated storage bucket.
- D. Redesign the data ingestion process to use gsutil tool to send the CSV files to a storage bucket in parallel.
- E. Assemble 1,000 files into a tape archive (TAR) fil
- F. Contact your internet service provider (ISP) to increase your maximum bandwidth to at least 100 Mbps.
Answer: A,D
NEW QUESTION # 28
What are all of the BigQuery operations that Google charges for?
- A. Storage, queries, and loading data from a file
- B. Storage, queries, and exporting data
- C. Storage, queries, and streaming inserts
- D. Queries and streaming inserts
Answer: C
Explanation:
Google charges for storage, queries, and streaming inserts. Loading data from a file and exporting data are free operations.
Reference: https://cloud.google.com/bigquery/pricing
NEW QUESTION # 29
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