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The candidates for this certification are the data engineers or those aiming to become one. These individuals should have the capacity to allow data-driven decision-making through the collection, transformation, and publishing of data. They have the expertise in designing, building, and operationalizing secure data processing systems and monitoring the same. This is with the specific emphasis on compliance and security, fidelity and reliability, portability and flexibility, as well as efficiency and scalability.
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Operationalize ML Models
- Deploy Machine Learning Pipelines: This objective requires your competence in ingesting relevant data, continuous evaluation, and retraining of ML models (Kuberflow, BigQuery Machine Learning, Cloud Machine Learning Engine, and Spark Machine Learning);
- Measure, Troubleshoot & Monitor Machine Learning Models: The focus of this subtopic includes the effect of dependencies on machine learning models. It will also measure the examinees’ understanding of machine learning terminologies, such as features, regression, labels, classification, models, recommendation, evaluation metrics, and unsupervised & supervised learning. Moreover, it will also assess their knowledge of common sources of error such as assumptions regarding data.
- Leverage Pre-Built Machine Learning Models as a Service: It covers one’s knowledge and skills in customizing machine learning APIs, including Auto ML text and Auto ML Vision. It also covers the conversational experiences, such as Dialogflow as well as machine learning APIs, including Speech API and Vision API;
- Select the Relevant Training & Service Infrastructure: The consideration for this topic includes distributed versus single machine, hardware accelerators (such as TPU and GPU), and edge compute usage;
Training Courses Recommended for the Exam Preparation
Training courses are meant to help candidates to learn about the Google exam syllabus and prepare well. It has hands-on labs and expert support that will allow you to get in-depth knowledge of each domain covered in the test. So, these are some of the best training courses offered by Google for the Professional Data Engineer certification exam.
Reference: https://cloud.google.com/certification/data-engineer
Google Professional-Data-Engineer Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Maintaining and automating data workloads | 18% | - Resource optimization
|
| Topic 2: Ensuring solution quality and reliability | 17% | - Testing and validating data systems
|
| Topic 3: Building and operationalizing data processing systems | 25% | - Building data pipelines
|
| Topic 4: Operationalizing machine learning models | 20% | - Preparing data for ML
|
| Topic 5: Designing data processing systems | 20% | - Designing for business requirements
|
Google Certified Professional Data Engineer Sample Questions:
You work on a regression problem in a natural language processing domain, and you have 100M labeled exmaples in your dataset. You have randomly shuffled your data and split your dataset into train and test samples (in a 90/10 ratio). After you trained the neural network and evaluated your model on a test set, you discover that the root-mean-squared error (RMSE) of your model is twice as high on the train set as on the test set. How should you improve the performance of your model?
- A. Try to collect more data and increase the size of your dataset.
- B. Increase the complexity of your model by, e.g., introducing an additional layer or increase sizing the size of vocabularies or n-grams used.
- C. Try out regularization techniques (e.g., dropout of batch normalization) to avoid overfitting.
- D. Increase the share of the test sample in the train-test split.
Correct Answer: B 🗳️
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You create an important report for your large team in Google Data Studio 360. The report uses Google BigQuery as its data source. You notice that visualizations are not showing data that is less than 1 hour old. What should you do?
- A. Disable caching in BigQuery by editing table details.
- B. Clear your browser history for the past hour then reload the tab showing the virtualizations.
- C. Disable caching by editing the report settings.
- D. Refresh your browser tab showing the visualizations.
Correct Answer: C 🗳️
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You are on the data governance team and are implementing security requirements to deploy resources. You need to ensure that resources are limited to only the europe-west3 region. You want to follow Google-recommended practices. What should you do?
- A. Deploy resources with Terraform and implement a variable validation rule to ensure that the region is set to the europe-west3 region for all resources.
- B. Create a Cloud Function to monitor all resources created and automatically destroy the ones created outside the europe-west3 region.
- C. Set the constraints/gcp.resourceLocations organization policy constraint to in:europe-west3- locations.
- D. Set the constraints/gcp.resourceLocations organization policy constraint to in:eu-locations.
Correct Answer: C 🗳️
You want to migrate an Apache Spark 3 batch job from on-premises to Google Cloud. You need to minimally change the job so that the job reads from Cloud Storage and writes the result to BigQuery. Your job is optimized for Spark, where each executor has 8 vCPU and 16 GB memory, and you want to be able to choose similar settings. You want to minimize installation and management effort to run your job. What should you do?
- A. Execute the job from a new Compute Engine VM.
- B. Execute as a Dataproc Serverless job.
- C. Execute the job as part of a deployment in a new Google Kubernetes Engine cluster.
- D. Execute the job in a new Dataproc cluster.
Correct Answer: B 🗳️
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You are operating a streaming Cloud Dataflow pipeline. Your engineers have a new version of the pipeline with a different windowing algorithm and triggering strategy. You want to update the running pipeline with the new version. You want to ensure that no data is lost during the update.
What should you do?
- A. Update the Cloud Dataflow pipeline inflight by passing the --update option with the --jobName set to a new unique job name
- B. Update the Cloud Dataflow pipeline inflight by passing the --update option with the --jobName set to the existing job name
- C. Stop the Cloud Dataflow pipeline with the Drain option. Create a new Cloud Dataflow job with the updated code
- D. Stop the Cloud Dataflow pipeline with the Cancel option. Create a new Cloud Dataflow job with the updated code
Correct Answer: C 🗳️
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