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

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Google Certified Professional Data Engineer Exam Sample Questions (Q276-Q281):

NEW QUESTION # 276
You have Cloud Functions written in Node.js that pull messages from Cloud Pub/Sub and send the data to BigQuery. You observe that the message processing rate on the Pub/Sub topic is orders of magnitude higher than anticipated, but there is no error logged in Stackdriver Log Viewer. What are the two most likely causes of this problem? Choose 2 answers.

A. The subscriber code does not acknowledge the messages that it pulls.
B. The subscriber code cannot keep up with the messages.
C. Error handling in the subscriber code is not handling run-time errors properly.
D. Publisher throughput quota is too small.
E. Total outstanding messages exceed the 10-MB maximum.

Answer: B,C

NEW QUESTION # 277
You want to create a machine learning model using BigQuery ML and create an endpoint foe hosting the model using Vertex Al. This will enable the processing of continuous streaming data in near-real time from multiple vendors. The data may contain invalid values. What should you do?

A. Create a Pub'Sub topic and send all vendor data to it Connect a Cloud Function to the topic to process the data and store it in BigQuery.
B. Create a Pub/Sub topic and send all vendor data to it Use Dataflow to process and sanitize the Pub/Sub data and stream it to BigQuery.
C. Create a new BigOuery dataset and use streaming inserts to land the data from multiple vendors.
Configure your BigQuery ML model to use the "ingestion' dataset as the training data.
D. Use BigQuery streaming inserts to land the data from multiple vendors whore your BigQuery dataset ML model is deployed.

Answer: B

Explanation:
Dataflow provides a scalable and flexible way to process and clean the incoming data in real-time before loading it into BigQuery.

NEW QUESTION # 278
Which of these is NOT a way to customize the software on Dataproc cluster instances?

A. Log into the master node and make changes from there
B. Set initialization actions
C. Configure the cluster using Cloud Deployment Manager
D. Modify configuration files using cluster properties

Answer: C

Explanation:
You can access the master node of the cluster by clicking the SSH button next to it in the Cloud Console.
You can easily use the --properties option of the dataproc command in the Google Cloud SDK to modify many common configuration files when creating a cluster.
When creating a Cloud Dataproc cluster, you can specify initialization actions in executables and/or scripts that Cloud Dataproc will run on all nodes in your Cloud Dataproc cluster immediately after the cluster is set up.
[https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/init-actions] Reference: https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/cluster- properties

NEW QUESTION # 279
Suppose you have a dataset of images that are each labeled as to whether or not they contain a human face. To create a neural network that recognizes human faces in images using this labeled dataset, what approach would likely be the most effective?

A. Use feature engineering to add features for eyes, noses, and mouths to the input data.
B. Use deep learning by creating a neural network with multiple hidden layers to automatically detect features of faces.
C. Build a neural network with an input layer of pixels, a hidden layer, and an output layer with two categories.
D. Use K-means Clustering to detect faces in the pixels.

Answer: B

Explanation:
Traditional machine learning relies on shallow nets, composed of one input and one output layer, and at most one hidden layer in between. More than three layers (including input and output) qualifies as "deep" learning. So deep is a strictly defined, technical term that means more than one hidden layer.
In deep-learning networks, each layer of nodes trains on a distinct set of features based on the previous layer's output. The further you advance into the neural net, the more complex the features your nodes can recognize, since they aggregate and recombine features from the previous layer.
A neural network with only one hidden layer would be unable to automatically recognize high-level features of faces, such as eyes, because it wouldn't be able to "build" these features using previous hidden layers that detect low-level features, such as lines.
Feature engineering is difficult to perform on raw image data.
K-means Clustering is an unsupervised learning method used to categorize unlabeled data.
Reference: https://deeplearning4j.org/neuralnet-overview

NEW QUESTION # 280
Your company has hired a new data scientist who wants to perform complicated analyses across very large datasets stored in Google Cloud Storage and in a Cassandra cluster on Google Compute Engine.
The scientist primarily wants to create labelled data sets for machine learning projects, along with some visualization tasks. She reports that her laptop is not powerful enough to perform her tasks and it is slowing her down. You want to help her perform her tasks. What should you do?

A. Host a visualization tool on a VM on Google Compute Engine.
B. Grant the user access to Google Cloud Shell.
C. Run a local version of Jupiter on the laptop.
D. Deploy Google Cloud Datalab to a virtual machine (VM) on Google Compute Engine.

Answer: B

NEW QUESTION # 281
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