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Google Professional-Data-Engineer Exam Syllabus Topics:

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

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Google Certified Professional Data Engineer Exam Sample Questions (Q231-Q236):

NEW QUESTION # 231
Your car factory is pushing machine measurements as messages into a Pub/Sub topic in your Google Cloud project. A Dataflow streaming job, that you wrote with the Apache Beam SDK, reads these messages, sends acknowledgment to Pub/Sub, applies some custom business logic in a DoFn instance, and writes the result to BigQuery. You want to ensure that if your business logic fails on a message, the message will be sent to a Pub/Sub topic that you want to monitor for alerting purposes. What should you do?

A. Use an exception handling block in your Dataflow's DoFn code to push the messages that failed to be transformed through a side output and to a new Pub/Sub topic. Use Cloud Monitoring to monitor the topic/num_unacked_messages_by_region metric on this new topic.
B. Enable retaining of acknowledged messages in your Pub/Sub pull subscription. Use Cloud Monitoring to monitor the subscription/num_retained_acked_messages metric on this subscription.
C. Create a snapshot of your Pub/Sub pull subscription. Use Cloud Monitoring to monitor the snapshot/ num_messages metric on this snapshot.
D. Enable dead lettering in your Pub/Sub pull subscription, and specify a new Pub/Sub topic as the dead letter topic. Use Cloud Monitoring to monitor the subscription/dead_letter_message_count metric on your pull subscription.

Answer: A

NEW QUESTION # 232
You are choosing a NoSQL database to handle telemetry data submitted from millions of Internet-of- Things (IoT) devices. The volume of data is growing at 100 TB per year, and each data entry has about
100 attributes. The data processing pipeline does not require atomicity, consistency, isolation, and durability (ACID). However, high availability and low latency are required.
You need to analyze the data by querying against individual fields. Which three databases meet your requirements? (Choose three.)

A. Cassandra
B. HDFS with Hive
C. Redis
D. MongoDB
E. MySQL
F. HBase

Answer: B,D,F

Explanation:
Explanation/Reference:

NEW QUESTION # 233
When running a pipeline that has a BigQuery source, on your local machine, you continue to get permission denied errors. What could be the reason for that?

A. Your gcloud does not have access to the BigQuery resources
B. BigQuery cannot be accessed from local machines
C. Pipelines cannot be run locally
D. You are missing gcloud on your machine

Answer: A

Explanation:
Explanation
When reading from a Dataflow source or writing to a Dataflow sink using DirectPipelineRunner, the Cloud Platform account that you configured with the gcloud executable will need access to the corresponding source/sink Reference:
https://cloud.google.com/dataflow/java-sdk/JavaDoc/com/google/cloud/dataflow/sdk/runners/DirectPipelineRun

NEW QUESTION # 234
You've migrated a Hadoop job from an on-prem cluster to dataproc and GCS. Your Spark job is a complicated analytical workload that consists of many shuffing operations and initial data are parquet files (on average 200-400 MB size each). You see some degradation in performance after the migration to Dataproc, so you'd like to optimize for it. You need to keep in mind that your organization is very cost-sensitive, so you'd like to continue using Dataproc on preemptibles (with 2 non-preemptible workers only) for this workload.
What should you do?

A. Switch from HDDs to SSDs, copy initial data from GCS to HDFS, run the Spark job and copy results back to GCS.
B. Switch from HDDs to SSDs, override the preemptible VMs configuration to increase the boot disk size.
C. Increase the size of your parquet files to ensure them to be 1 GB minimum.
D. Switch to TFRecords formats (appr. 200MB per file) instead of parquet files.

Answer: C

NEW QUESTION # 235
You have data pipelines running on BigQuery, Cloud Dataflow, and Cloud Dataproc. You need to perform health checks and monitor their behavior, and then notify the team managing the pipelines if they fail. You also need to be able to work across multiple projects. Your preference is to use managed products of features of the platform. What should you do?

A. Export the logs to BigQuery, and set up App Engine to read that information and send emails if you find a failure in the logs
B. Run a Virtual Machine in Compute Engine with Airflow, and export the information to Stackdriver
C. Develop an App Engine application to consume logs using GCP API calls, and send emails if you find a failure in the logs
D. Export the information to Cloud Stackdriver, and set up an Alerting policy

Answer: B

NEW QUESTION # 236
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