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Google Professional-Data-Engineer Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Maintaining and automating data workloads (~15% of the exam)	15%	- Automating data processes 1. Scheduling jobs 2. Workflow orchestration 3. Continuous integration and continuous deployment (CI/CD) - Monitoring data pipelines and data processes 1. Logging, monitoring, and troubleshooting 2. Managing quotas and resource usage - Designing for reliability and fidelity 1. Performing data quality and validation checks 2. Recovering from failures 3. Planning for monitoring and alerting
Topic 2: Storing the data (~20% of the exam)	20%	- Designing for a data platform 1. Building a data platform using Dataplex, Dataplex Catalog, BigQuery, Cloud Storage 2. Building a federated governance model for distributed data systems - Using a data lake 1. Monitoring the data lake 2. Processing data 3. Managing the lake (data discovery, access, cost controls) - Planning for using a data warehouse 1. Mapping business requirements 2. Defining architecture to support data access patterns 3. Designing the data model 4. Deciding the degree of data normalization - Selecting storage systems 1. Planning for storage costs and performance 2. Lifecycle management of data 3. Analyzing data access patterns
Topic 3: Designing data processing systems (~30% of the exam)	30%	- Designing data processing resources 1. Cost optimization 2. Cluster sizing and autoscaling 3. Compute options (Dataflow, Dataproc, Dataplex, Cloud Functions, Cloud Run) - Selecting appropriate storage technologies 1. Mapping storage options to business requirements 2. Choosing between BigQuery, Bigtable, Spanner, Cloud SQL, Cloud Storage, Firestore, Memorystore, AlloyDB - Designing data pipelines 1. Streaming (e.g., windowing, late arriving data) 2. Integrating with new data sources 3. Batch processing 4. AI data enrichment 5. Data acquisition and import 6. Processing logic
Topic 4: Preparing and using data for analysis (~15% of the exam)	15%	- Sharing data securely 1. Publishing datasets 2. Data sharing and collaboration - Preparing data for visualization 1. Connecting to Looker and other BI tools 2. Preparing data for reporting and dashboards
Topic 5: Ingesting and processing the data (~20% of the exam)	20%	- Building and maintaining data structures and databases 1. Defining data lifecycle 2. Planning for analytical and operational use cases - Deploying and operationalizing the pipelines 1. Job automation and orchestration (Cloud Composer, Workflows) 2. CI/CD for data pipelines - Performing security considerations 1. Data encryption 2. Identity and Access Management (IAM) 3. Auditing, privacy, and compliance

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Google Certified Professional Data Engineer Exam Sample Questions (Q83-Q88):

NEW QUESTION # 83
Which software libraries are supported by Cloud Machine Learning Engine?

A. TensorFlow and Torch
B. TensorFlow
C. Theano and Torch
D. Theano and TensorFlow

Answer: B

Explanation:
Cloud ML Engine mainly does two things:
Enables you to train machine learning models at scale by running TensorFlow training applications in the cloud.
Hosts those trained models for you in the cloud so that you can use them to get predictions about new data.

NEW QUESTION # 84
You want to analyze hundreds of thousands of social media posts daily at the lowest cost and with the fewest steps.
You have the following requirements:
* You will batch-load the posts once per day and run them through the Cloud Natural Language API.
* You will extract topics and sentiment from the posts.
* You must store the raw posts for archiving and reprocessing.
* You will create dashboards to be shared with people both inside and outside your organization.
You need to store both the data extracted from the API to perform analysis as well as the raw social media posts for historical archiving. What should you do?

A. Store the social media posts and the data extracted from the API in Cloud SQL.
B. Feed to social media posts into the API directly from the source, and write the extracted data from the API into BigQuery.
C. Store the raw social media posts in Cloud Storage, and write the data extracted from the API into BigQuery.
D. Store the social media posts and the data extracted from the API in BigQuery.

Answer: B

NEW QUESTION # 85
You are developing an application that uses a recommendation engine on Google Cloud. Your solution should display new videos to customers based on past views. Your solution needs to generate labels for the entities in videos that the customer has viewed. Your design must be able to provide very fast filtering suggestions based on data from other customer preferences on several TB of data. What should you do?

A. Build and train a classification model with Spark MLlib to generate labels. Build and train a second classification model with Spark MLlib to filter results to match customer preferences. Deploy the models using Cloud Dataproc. Call the models from your application.
B. Build an application that calls the Cloud Video Intelligence API to generate labels. Store data in Cloud SQL, and join and filter the predicted labels to match the user's viewing history to generate preferences.
C. Build an application that calls the Cloud Video Intelligence API to generate labels. Store data in Cloud Bigtable, and filter the predicted labels to match the user's viewing history to generate preferences.
D. Build and train a complex classification model with Spark MLlib to generate labels and filter the results.
Deploy the models using Cloud Dataproc. Call the model from your application.

Answer: C

Explanation:
The recommendation requires filtering based on several TB of data, therefore BigTable is the recommended option vs Cloud SQL which is limited to 10TB.

NEW QUESTION # 86
You are working on a sensitive project involving private user data. You have set up a project on Google Cloud Platform to house your work internally. An external consultant is going to assist with coding a complex transformation in a Google Cloud Dataflow pipeline for your project. How should you maintain users' privacy?

A. Create an anonymized sample of the data for the consultant to work with in a different project.
B. Grant the consultant the Cloud Dataflow Developer role on the project.
C. Grant the consultant the Viewer role on the project.
D. Create a service account and allow the consultant to log on with it.

Answer: A

NEW QUESTION # 87
How can you get a neural network to learn about relationships between categories in a categorical feature?

A. Create a multi-hot column
B. Create an embedding column
C. Create a one-hot column
D. Create a hash bucket

Answer: B

Explanation:
There are two problems with one-hot encoding. First, it has high dimensionality, meaning that instead of having just one value, like a continuous feature, it has many values, or dimensions. This makes computation more time-consuming, especially if a feature has a very large number of categories. The second problem is that it doesn't encode any relationships between the categories. They are completely independent from each other, so the network has no way of knowing which ones are similar to each other.
Both of these problems can be solved by representing a categorical feature with an embedding column.
The idea is that each category has a smaller vector with, let's say, 5 values in it. But unlike a one-hot vector, the values are not usually 0. The values are weights, similar to the weights that are used for basic features in a neural network. The difference is that each category has a set of weights (5 of them in this case).
You can think of each value in the embedding vector as a feature of the category. So, if two categories are very similar to each other, then their embedding vectors should be very similar too. Reference: https:// cloudacademy.com/google/introduction-to-google-cloud-machine-learning-engine-course/a-wide-and- deep-model.html

NEW QUESTION # 88
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