Google Cloud Platform Fundamentals: Core Infrastructure
Big Data and Machine Learning
Q1) Name two use cases for Google Cloud Dataproc (Select 2 answers).
- Data mining and analysis in datasets of known size
- Migrate on-premises Hadoop jobs to the cloud
- Manage data that arrives in realtime
- Manage datasets of unpredictable size
Q2) Name two use cases for Google Cloud Dataflow (Select 2 answers).
- Extract, Transform, and Load (ETL)
- Manual resource management
- Reserved compute instances
Q3) Name three use cases for the Google Cloud Machine Learning Platform (Select 3 answers).
- Fraud detection
- Data preparation
- Sentiment analysis
- Content personalization
- Query architecture
Q4) Which statements are true about BigQuery? Choose all that are true (2 statements).
- BigQuery is a good choice for data analytics warehousing.
- BigQuery requires that you provision database instances ahead of use.
- Once in BigQuery, data is not accessible from other GCP services.
- BigQuery is a good choice for online transaction processing.
- BigQuery lets you run fast SQL queries against large databases.
Q5) Name three use cases for Cloud Pub/Sub (Select 3 answers).
- Analyzing streaming data
- Storage of binary web content
- Decoupling systems
- Internet of Things applications
- Executing ad-hoc SQL queries
Q6) What is TensorFlow?
- A managed service for building machine learning models
- An open-source software library that’s useful for building machine learning applications
- A hardware device designed to accelerate machine learning workloads
- A managed service for building data pipelines
Q7) What does the Cloud Natural Language API do?
- It analyzes text to reveal its structure and meaning.
- It translates arbitrary strings into any supported language.
- It extracts text in various languages from images.
- It performs sentiment analysis on audio and video content.
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