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Google Professional-Data-Engineer Exam Syllabus Topics:

Section	Weight	Objectives
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 processing resources 1. Compute options (Dataflow, Dataproc, Dataplex, Cloud Functions, Cloud Run) 2. Cluster sizing and autoscaling 3. Cost optimization - Designing data pipelines 1. Integrating with new data sources 2. Streaming (e.g., windowing, late arriving data) 3. Processing logic 4. Batch processing 5. Data acquisition and import 6. AI data enrichment
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
Maintaining and automating data workloads (~15% of the exam)	15%	- Designing for reliability and fidelity 1. Performing data quality and validation checks 2. Planning for monitoring and alerting 3. Recovering from failures - Monitoring data pipelines and data processes 1. Logging, monitoring, and troubleshooting 2. Managing quotas and resource usage - Automating data processes 1. Scheduling jobs 2. Workflow orchestration 3. Continuous integration and continuous deployment (CI/CD)
Storing the data (~20% of the exam)	20%	- Planning for using a data warehouse 1. Defining architecture to support data access patterns 2. Designing the data model 3. Deciding the degree of data normalization 4. Mapping business requirements - Using a data lake 1. Processing data 2. Monitoring the data lake 3. Managing the lake (data discovery, access, cost controls) - Designing for a data platform 1. Building a federated governance model for distributed data systems 2. Building a data platform using Dataplex, Dataplex Catalog, BigQuery, Cloud Storage - Selecting storage systems 1. Lifecycle management of data 2. Analyzing data access patterns 3. Planning for storage costs and performance
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. 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 (Q113-Q118):

NEW QUESTION # 113
Which Java SDK class can you use to run your Dataflow programs locally?

A. LocalPipelineRunner
B. LocalRunner
C. DirectPipelineRunner
D. MachineRunner

Answer: C

Explanation:
DirectPipelineRunner allows you to execute operations in the pipeline directly, without any optimization. Useful for small local execution and tests

NEW QUESTION # 114
You need to store and analyze social media postings in Google BigQuery at a rate of 10,000 messages per minute in near real-time. Initially, design the application to use streaming inserts for individual postings. Your application also performs data aggregations right after the streaming inserts. You discover that the queries after streaming inserts do not exhibit strong consistency, and reports from the queries might miss in-flight data. How can you adjust your application design?

A. Re-write the application to load accumulated data every 2 minutes.
B. Load the original message to Google Cloud SQL, and export the table every hour to BigQuery via streaming inserts.
C. Convert the streaming insert code to batch load for individual messages.
D. Estimate the average latency for data availability after streaming inserts, and always run queries after waiting twice as long.

Answer: D

Explanation:
Explanation
The data is first comes to buffer and then written to Storage. If we are running queries in buffer we will face above mentioned issues. If we wait for the bigquery to write the data to storage then we won't face the issue.
So We need to wait till it's written tio storage

NEW QUESTION # 115
You are implementing several batch jobs that must be executed on a schedule. These jobs have many interdependent steps that must be executed in a specific order. Portions of the jobs involve executing shell scripts, running Hadoop jobs, and running queries in BigQuery. The jobs are expected to run for many minutes up to several hours. If the steps fail, they must be retried a fixed number of times. Which service should you use to manage the execution of these jobs?

A. Cloud Functions
B. Cloud Dataflow
C. Cloud Composer
D. Cloud Scheduler

Answer: C

NEW QUESTION # 116
You're training a model to predict housing prices based on an available dataset with real estate properties.
Your plan is to train a fully connected neural net, and you've discovered that the dataset contains latitude and longitude of the property. Real estate professionals have told you that the location of the property is highly influential on price, so you'd like to engineer a feature that incorporates this physical dependency.
What should you do?

A. Create a feature cross of latitude and longitude, bucketize it at the minute level and use L2 regularization during optimization.
B. Create a feature cross of latitude and longitude, bucketize at the minute level and use L1 regularization during optimization.
C. Create a numeric column from a feature cross of latitude and longitude.
D. Provide latitude and longitude as input vectors to your neural net.

Answer: B

Explanation:
Use L1 regularization when you need to assign greater importance to more influential features. It shrinks less important feature to 0.
L2 regularization performs better when all input features influence the output & all with the weights are of equal size.

NEW QUESTION # 117
You want to archive data in Cloud Storage. Because some data is very sensitive, you want to use the "Trust No One" (TNO) approach to encrypt your data to prevent the cloud provider staff from decrypting your dat a. What should you do?

A. Use gcloud kms keys create to create a symmetric key. Then use gcloud kms encrypt to encrypt each archival file with the key. Use gsutil cp to upload each encrypted file to the Cloud Storage bucket. Manually destroy the key previously used for encryption, and rotate the key once and rotate the key once.
B. Use gcloud kms keys create to create a symmetric key. Then use gcloud kms encrypt to encrypt each archival file with the key and unique additional authenticated data (AAD). Use gsutil cp to upload each encrypted file to the Cloud Storage bucket, and keep the AAD outside of Google Cloud.
C. Specify customer-supplied encryption key (CSEK) in the .boto configuration file. Use gsutil cp to upload each archival file to the Cloud Storage bucket. Save the CSEK in Cloud Memorystore as permanent storage of the secret.
D. Specify customer-supplied encryption key (CSEK) in the .boto configuration file. Use gsutil cp to upload each archival file to the Cloud Storage bucket. Save the CSEK in a different project that only the security team can access.

Answer: A

NEW QUESTION # 118
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