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Google Professional-Data-Engineer Exam Syllabus Topics:

Section	Weight	Objectives
Designing data processing systems	20%	- Designing for business requirements 1. Selecting appropriate storage solutions 2. Designing for scalability and elasticity 3. Designing for reliability and fault tolerance - Designing for regulatory and security requirements 1. Ensuring data privacy and compliance 2. Implementing access control and data protection
Operationalizing machine learning models	20%	- Preparing data for ML 1. Feature engineering and data preparation 2. Handling structured and unstructured data - Deploying and maintaining ML models 1. Optimizing model performance and cost 2. Model serving and monitoring
Building and operationalizing data processing systems	25%	- Deploying and managing systems 1. Managing infrastructure and resources 2. Monitoring and logging data processes - Building data pipelines 1. Transforming and cleaning data 2. Orchestrating data workflows 3. Ingesting data from various sources
Maintaining and automating data workloads	18%	- Automation and repeatability 1. Automating deployment and updates 2. Implementing CI/CD for data systems - Resource optimization 1. Choosing appropriate compute and storage options 2. Cost management and resource allocation
Ensuring solution quality and reliability	17%	- Troubleshooting and optimization 1. Optimizing queries and workloads 2. Diagnosing performance issues - Testing and validating data systems 1. Data quality validation 2. Performance and scalability testing

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Google Certified Professional Data Engineer Exam Sample Questions (Q117-Q122):

NEW QUESTION # 117
You are monitoring your organization's data lake hosted on BigQuery. The ingestion pipelines read data from Pub/Sub and write the data into tables on BigQuery. After a new version of the ingestion pipelines is deployed, the daily stored data increased by 50%. The volumes of data in Pub/Sub remained the same and only some tables had their daily partition data size doubled. You need to investigate and fix the cause of the data increase. What should you do?

A. 1. Check for code errors in the deployed pipelines.
2. Check for multiple writing to pipeline BigQuery sink.
3. Check for errors in Cloud Logging during the day of the release of the new pipelines.
4. If no errors, restore the BigQuery tables to their content before the last release by using time travel.
B. 1. Roll back the last deployment.
2. Restore the BigQuery tables to their content before the last release by using time travel.
3. Restart the Dataflow jobs and replay the messages by seeking the subscription to the timestamp of the release.
C. 1. Check for duplicate rows in the BigQuery tables that have the daily partition data size doubled.
2. Check the BigQuery Audit logs to find job IDs.
3. Use Cloud Monitoring to determine when the identified Dataflow jobs started and the pipeline code version.
4. When more than one pipeline ingests data into a table, stop all versions except the latest one.
D. 1. Check for duplicate rows in the BigQuery tables that have the daily partition data size doubled.
2. Schedule daily SQL jobs to deduplicate the affected tables.
3. Share the deduplication script with the other operational teams to reuse if this occurs to other tables.

Answer: C

NEW QUESTION # 118
You are training a spam classifier. You notice that you are overfitting the training data. Which three actions can you take to resolve this problem? (Choose three.)

A. Use a larger set of features
B. Decrease the regularization parameters
C. Reduce the number of training examples
D. Get more training examples
E. Increase the regularization parameters
F. Use a smaller set of features

Answer: A,B,D

NEW QUESTION # 119
You are managing a Cloud Dataproc cluster. You need to make a job run faster while minimizing costs, without losing work in progress on your clusters. What should you do?

A. Increase the cluster size with more non-preemptible workers.
B. Increase the cluster size with preemptible worker nodes, and configure them to use graceful decommissioning.
C. Increase the cluster size with preemptible worker nodes, and configure them to forcefully decommission.
D. Increase the cluster size with preemptible worker nodes, and use Cloud Stackdriver to trigger a script to preserve work.

Answer: B

Explanation:
Explanation
Reference https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/flex

NEW QUESTION # 120
You are using BigQuery with a multi-region dataset that includes a table with the daily sales volumes. This table is updated multiple times per day. You need to protect your sales table in case of regional failures with a recovery point objective (RPO) of less than 24 hours, while keeping costs to a minimum. What should you do?

A. Schedule a daily BigQuery snapshot of the table.
B. Modify ETL job to load the data into both the current and another backup region.
C. Schedule a daily export of the table to a Cloud Storage dual or multi-region bucket.
D. Schedule a daily copy of the dataset to a backup region.

Answer: C

NEW QUESTION # 121
You need to create a new transaction table in Cloud Spanner that stores product sales data. You are deciding what to use as a primary key. From a performance perspective, which strategy should you choose?

A. The current epoch time
B. A concatenation of the product name and the current epoch time
C. The original order identification number from the sales system, which is a monotonically increasing integer
D. A random universally unique identifier number (version 4 UUID)

Answer: D

Explanation:
https://cloud.google.com/spanner/docs/schema-and-data-model#choosing_a_primary_key

NEW QUESTION # 122
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