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Google Professional-Data-Engineer Exam Syllabus Topics:

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

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Google Certified Professional Data Engineer Exam Sample Questions (Q106-Q111):

NEW QUESTION # 106
Your financial services company has a critical daily reconciliation process that involves several distinct steps:
fetching data from an external SFTP server, decrypting the files, loading them into Cloud Storage, and finally running a series of BigQuery SQL transformations. Each step has strict dependencies, and the entire process should notify you if not completed by 7:00 AM. Manual intervention for failures is costly and delays compliance reporting. You need a highly observable and robust solution that supports easy re-runs of individual steps if errors occur. What should you do?

A. Create a Cloud Composer DAG that includes a single BashOperator to execute a top-level shell script, which in turn calls individual scripts for each pipeline step. Upload the scripts to a Cloud Composer environment's DAGs folder, and configure it to run daily.
B. Define a Cloud Composer DAG to orchestrate the SFTP fetch and decryption steps, and then use Cloud Scheduler to trigger a separate Dataflow job that handles the Cloud Storage load and BigQuery transformations and schedule to run daily.
C. Develop a Cloud Composer DAG that includes a single PythonOperator to execute a Python script that runs each step sequentially, incorporating error handling and retries. Upload the scripts to a Cloud Composer environment's DAGs folder, and configure it to run daily.
D. Implement a Cloud Composer DAG, with each step defined as a separate task using appropriate Airflow operators, and schedule the DAG for daily execution.

Answer: D

Explanation:
Cloud Composer (based on Apache Airflow) is designed for workflow orchestration where complex dependencies exist. To achieve high observability and robustness (specifically the ability to re-run individual steps), the workflow must be decomposed into granular tasks.
* Granularity and Re-runs: By defining each step (SFTP fetch, Decrypt, GCS Load, BQ Transform) as a separate task in a DAG, Airflow tracks the state of each individually. If the "BigQuery SQL" step fails, you can "Clear" only that specific task in the UI to re-run it without re-fetching or re-decrypting the data, saving time and costs.
* Observability: Each task has its own logs and status in the Airflow UI. Options B and C (Single Operator/Script) are "black boxes"-if they fail, Airflow only knows the entire script failed, making it difficult to pinpoint where and impossible to re-run just the failed portion.
* SLA and Notifications: Airflow has built-in sla_miss_callbacks and email_on_failure features. You can set an sla parameter of 7:00 AM to automatically trigger alerts if the process is lagging.
* Correcting other options: * A: Splitting the logic between Composer and a separate Scheduler
/Dataflow job breaks the end-to-end lineage and makes it harder to manage dependencies and global SLAs.
* B & C: As mentioned, using a single operator for multiple logic steps defeats the purpose of an orchestrator's monitoring and retry capabilities.
Reference: Google Cloud Documentation on Cloud Composer / Airflow:
"An Airflow DAG is a collection of all the tasks you want to run, organized in a way that reflects their relationships and dependencies... Breaking down your workflow into multiple tasks allows for: Individual retries (re-running only failed parts), Parallel execution, and Clearer monitoring in the Airflow web interface." (Source: Key Airflow Concepts)
"You can use the SLA (Service Level Agreement) feature in Airflow to track whether a task or DAG takes longer than expected to finish... If a task exceeds its SLA, Airflow can send an email alert or trigger a callback function." (Source: Airflow Documentation - SLAs)

NEW QUESTION # 107
All Google Cloud Bigtable client requests go through a front-end server ______ they are sent to a Cloud Bigtable node.

A. only if
B. after
C. once
D. before

Answer: D

Explanation:
In a Cloud Bigtable architecture all client requests go through a front-end server before they are sent to a Cloud Bigtable node.
The nodes are organized into a Cloud Bigtable cluster, which belongs to a Cloud Bigtable instance, which is a container for the cluster. Each node in the cluster handles a subset of the requests to the cluster.
When additional nodes are added to a cluster, you can increase the number of simultaneous requests that the cluster can handle, as well as the maximum throughput for the entire cluster.
Reference:
https://cloud.google.com/bigtable/docs/overview

NEW QUESTION # 108
You work for a bank. You have a labelled dataset that contains information on already granted loan application and whether these applications have been defaulted. You have been asked to train a model to predict default rates for credit applicants. What should you do?

A. Match loan applicants with their social profiles to enable feature engineering.
B. Train a linear regression to predict a credit default risk score.
C. Increase the size of the dataset by collecting additional data.
D. Remove the bias from the data and collect applications that have been declined loans.

Answer: B

NEW QUESTION # 109
Your neural network model is taking days to train. You want to increase the training speed. What can you do?

A. Increase the number of layers in your neural network.
B. Subsample your test dataset.
C. Subsample your training dataset.
D. Increase the number of input features to your model.

Answer: A

Explanation:
Reference: https://towardsdatascience.com/how-to-increase-the-accuracy-of-a-neural-network-9f5d1c6f407d

NEW QUESTION # 110
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. Build a neural network with an input layer of pixels, a hidden layer, and an output layer with two categories.
B. Use deep learning by creating a neural network with multiple hidden layers to automatically detect features of faces.
C. Use K-means Clustering to detect faces in the pixels.
D. Use feature engineering to add features for eyes, noses, and mouths to the input data.

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 # 111
......

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