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Google Cloud Big Data & Machine Learning Fundamentals course
This course is a gateway to introduce you to Google Cloud's big data and different machine learning functions. However, to successfully pass this training, you have to attain one year of experience in SQL, extract transform, data modeling, machine learning, programming in Python, and load activities. So, the objectives of the course are the following:
- Hire BigQuery and Cloud SQL for interactive data analysis
- Create ML models using BigQuery ML, APIs, and AutoML.
- Utilize Cloud SQL & Dataproc to migrate existing MySQL, Pig, Spark, or Hive workloads to Google Cloud
- Recognize the purpose of the key Big data and Machine Learning products in Google Cloud
Career Opportunities
The certified individuals can explore a variety of job opportunities. Some of the positions that they can take up include a Software Engineer, a Cloud Architect, a Data Engineer, a Sales Engineer, a Data Scientist, a Cloud Developer, and a Kubernetes Architect, among others. The salary outlook for these job roles is an average of $128,500 per annum.
How to book Google Professional Data Engineer Exams
The registration for the Google Professional Data Engineer Exam follows the steps given below.
- Step4: Take the date of the exam, choose exam center and make further payment using payment method like credit/debit etc.
- Step1: Visit the Google Cloud Webassessor Website
- Step2: Sign in or sign up to your Google Cloud Webassessor account
- Step3: Search for the exam name Google Professional Data Engineer
Reference: https://cloud.google.com/certification/data-engineer
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Nowadays, employment situation is becoming more and more rigorous, it's necessary for people to acquire more skills and knowledge when they are looking for a job. Enterprises and institutions often raise high acquirements for massive candidates, and aim to get the best quality talents. Thus a high-quality Google Cloud Certified Professional-Data-Engineer certification will be an outstanding advantage, especially for the employees, which may double your salary, get you a promotion.
Google Professional-Data-Engineer Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Operationalizing machine learning models | 20% | - Deploying and maintaining ML models
|
| Ensuring solution quality and reliability | 17% | - Testing and validating data systems
|
| Building and operationalizing data processing systems | 25% | - Deploying and managing systems
|
| Maintaining and automating data workloads | 18% | - Automation and repeatability
|
| Designing data processing systems | 20% | - Designing for business requirements
|
Google Certified Professional Data Engineer Sample Questions:
You have some data, which is shown in the graphic below. The two dimensions are X and Y, and the shade of each dot represents what class it is. You want to classify this data accurately using a linear algorithm. To do this you need to add a synthetic feature. What should the value of that feature be?
- A. X2+Y2
- B. Y2
- C. cos(X)
- D. X2
Correct Answer: A 🗳️
You use a dataset in BigQuery for analysis. You want to provide third-party companies with access to the same dataset. You need to keep the costs of data sharing low and ensure that the data is current. Which solution should you choose?
- A. Use Cloud Scheduler to export the data on a regular basis to Cloud Storage, and provide third- party companies with access to the bucket.
- B. Create a Cloud Dataflow job that reads the data in frequent time intervals, and writes it to the relevant BigQuery dataset or Cloud Storage bucket for third-party companies to use.
- C. Create a separate dataset in BigQuery that contains the relevant data to share, and provide third- party companies with access to the new dataset.
- D. Create an authorized view on the BigQuery table to control data access, and provide third-party companies with access to that view.
Correct Answer: D 🗳️
Explanation: Only visible for RealValidExam members. You can sign-up / login (it's free).
You are designing storage for very large text files for a data pipeline on Google Cloud. You want to support ANSI SQL queries. You also want to support compression and parallel load from the input locations using Google recommended practices. What should you do?
- A. Transform text files to compressed Avro using Cloud Dataflow. Use Cloud Storage and BigQuery permanent linked tables for query.
- B. Transform text files to compressed Avro using Cloud Dataflow. Use BigQuery for storage and query.
- C. Compress text files to gzip using the Grid Computing Tools. Use BigQuery for storage and query.
- D. Compress text files to gzip using the Grid Computing Tools. Use Cloud Storage, and then import into Cloud Bigtable for query.
Correct Answer: A 🗳️
Explanation: Only visible for RealValidExam members. You can sign-up / login (it's free).
Your company currently runs a large on-premises cluster using Spark, Hive, and HDFS in a colocation facility. The cluster is designed to accommodate peak usage on the system; however, many jobs are batch in nature, and usage of the cluster fluctuates quite dramatically. Your company is eager to move to the cloud to reduce the overhead associated with on-premises infrastructure and maintenance and to benefit from the cost savings. They are also hoping to modernize their existing infrastructure to use more serverless offerings in order to take advantage of the cloud. Because of the timing of their contract renewal with the colocation facility, they have only 2 months for their initial migration. How would you recommend they approach their upcoming migration strategy so they can maximize their cost savings in the cloud while still executing the migration in time?
- A. Modernize the Spark workload for Dataflow and the Hive workload for BigQuery.
- B. Migrate the Spark workload to Dataproc plus HDFS, and modernize the Hive workload for BigQuery.
- C. Migrate the workloads to Dataproc plus HDFS; modernize later.
- D. Migrate the workloads to Dataproc plus Cloud Storage; modernize later.
Correct Answer: D 🗳️
Explanation: Only visible for RealValidExam members. You can sign-up / login (it's free).
You want to use a BigQuery table as a data sink. In which writing mode(s) can you use BigQuery as a sink?
- A. Only streaming
- B. BigQuery cannot be used as a sink
- C. Only batch
- D. Both batch and streaming
Correct Answer: D 🗳️
Explanation: Only visible for RealValidExam members. You can sign-up / login (it's free).
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