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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. Data sharing and collaboration 2. Publishing datasets
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. Lifecycle management of data 2. Planning for storage costs and performance 3. Analyzing data access patterns - 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 - Using a data lake 1. Managing the lake (data discovery, access, cost controls) 2. Monitoring the data lake 3. Processing data
Topic 3: Maintaining and automating data workloads (~15% of the exam)	15%	- 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. Continuous integration and continuous deployment (CI/CD) 2. Workflow orchestration 3. Scheduling jobs - Monitoring data pipelines and data processes 1. Managing quotas and resource usage 2. Logging, monitoring, and troubleshooting
Topic 4: Designing data processing systems (~30% of the exam)	30%	- 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. Data acquisition and import 2. Integrating with new data sources 3. Streaming (e.g., windowing, late arriving data) 4. Processing logic 5. AI data enrichment 6. Batch processing - Designing data processing resources 1. Compute options (Dataflow, Dataproc, Dataplex, Cloud Functions, Cloud Run) 2. Cost optimization 3. Cluster sizing and autoscaling
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 - Performing security considerations 1. Identity and Access Management (IAM) 2. Auditing, privacy, and compliance 3. Data encryption - Deploying and operationalizing the pipelines 1. Job automation and orchestration (Cloud Composer, Workflows) 2. CI/CD for data pipelines

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Google Certified Professional Data Engineer Exam Sample Questions (Q81-Q86):

NEW QUESTION # 81
An online brokerage company requires a high volume trade processing architecture. You need to create a secure queuing system that triggers jobs. The jobs will run in Google Cloud and cat the company's Python API to execute trades. You need to efficiently implement a solution. What should you do?

A. Use a Pub/Sub push subscription to trigger a Cloud Function to pass the data to tie Python API.
B. Write an application hosted on a Compute Engine instance that makes a push subscription to the Pub/ Sub topic
C. Use Cloud Composer to subscribe to a Pub/Sub tope and can the Python API.
D. Write an application that makes a queue in a NoSQL database

Answer: A

NEW QUESTION # 82
You work for a manufacturing company that sources up to 750 different components, each from a different supplier. You've collected a labeled dataset that has on average 1000 examples for each unique component.
Your team wants to implement an app to help warehouse workers recognize incoming components based on a photo of the component. You want to implement the first working version of this app (as Proof-Of-Concept) within a few working days. What should you do?

A. Use Cloud Vision AutoML with the existing dataset.
B. Train your own image recognition model leveraging transfer learning techniques.
C. Use Cloud Vision API by providing custom labels as recognition hints.
D. Use Cloud Vision AutoML, but reduce your dataset twice.

Answer: A

NEW QUESTION # 83
You are building new real-time data warehouse for your company and will use Google BigQuery streaming inserts. There is no guarantee that data will only be sent in once but you do have a unique ID for each row of data and an event timestamp. You want to ensure that duplicates are not included while interactively querying dat
a. Which query type should you use?

A. Use GROUP BY on the unique ID column and timestamp column and SUM on the values.
B. Use the ROW_NUMBER window function with PARTITION by unique ID along with WHERE row equals 1.
C. Use the LAG window function with PARTITION by unique ID along with WHERE LAG IS NOT NULL.
D. Include ORDER BY DESK on timestamp column and LIMIT to 1.

Answer: B

Explanation:
https://cloud.google.com/bigquery/docs/reference/standard-sql/analytic-function-concepts

NEW QUESTION # 84
You have enabled the free integration between Firebase Analytics and Google BigQuery. Firebase now automatically creates a new table daily in BigQuery in the format app_events_YYYYMMDD.You want to query all of the tables for the past 30 days in legacy SQL. What should you do?

A. Use SELECT IF.(date >= YYYY-MM-DD AND date <= YYYY-MM-DD
B. Use the WHERE_PARTITIONTIMEpseudo column
C. Use WHEREdate BETWEEN YYYY-MM-DD AND YYYY-MM-DD
D. Use the TABLE_DATE_RANGEfunction

Answer: D

Explanation:
Explanation/Reference:
Reference: https://cloud.google.com/blog/products/gcp/using-bigquery-and-firebase-analytics-to- understand-your-mobile-app?hl=am

NEW QUESTION # 85
You are selecting services to write and transform JSON messages from Cloud Pub/Sub to BigQuery for a data pipeline on Google Cloud. You want to minimize service costs. You also want to monitor and accommodate input data volume that will vary in size with minimal manual intervention. What should you do?

A. Use Cloud Dataflow to run your transformations. Monitor the total execution time for a sampling of jobs. Configure the job to use non-default Compute Engine machine types when needed.
B. Use Cloud Dataproc to run your transformations. Monitor CPU utilization for the cluster. Resize the number of worker nodes in your cluster via the command line.
C. Use Cloud Dataflow to run your transformations. Monitor the job system lag with Stackdriver. Use the
default autoscaling setting for worker instances.
D. Use Cloud Dataproc to run your transformations. Use the diagnose command to generate an operational output archive. Locate the bottleneck and adjust cluster resources.

Answer: D

NEW QUESTION # 86
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