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Google Professional-Data-Engineer Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Designing data processing systems	20%	- Designing for business requirements 1. Selecting appropriate storage solutions 2. Designing for reliability and fault tolerance 3. Designing for scalability and elasticity - Designing for regulatory and security requirements 1. Ensuring data privacy and compliance 2. Implementing access control and data protection
Topic 2: Maintaining and automating data workloads	18%	- Automation and repeatability 1. Automating deployment and updates 2. Implementing CI/CD for data systems - Resource optimization 1. Cost management and resource allocation 2. Choosing appropriate compute and storage options
Topic 3: Operationalizing machine learning models	20%	- Preparing data for ML 1. Handling structured and unstructured data 2. Feature engineering and data preparation - Deploying and maintaining ML models 1. Optimizing model performance and cost 2. Model serving and monitoring
Topic 4: 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
Topic 5: Ensuring solution quality and reliability	17%	- Testing and validating data systems 1. Data quality validation 2. Performance and scalability testing - Troubleshooting and optimization 1. Diagnosing performance issues 2. Optimizing queries and workloads

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Google Certified Professional Data Engineer Exam Sample Questions (Q372-Q377):

NEW QUESTION # 372
Which is the preferred method to use to avoid hotspotting in time series data in Bigtable?

A. Randomization
B. Field promotion
C. Salting
D. Hashing

Answer: B

Explanation:
Explanation
By default, prefer field promotion. Field promotion avoids hotspotting in almost all cases, and it tends to make it easier to design a row key that facilitates queries.
Reference:
https://cloud.google.com/bigtable/docs/schema-design-time-series#ensure_that_your_row_key_avoids_hotspotti

NEW QUESTION # 373
The YARN ResourceManager and the HDFS NameNode interfaces are available on a Cloud Dataproc cluster
____.

A. worker node
B. conditional node
C. master node
D. application node

Answer: C

Explanation:
Explanation
The YARN ResourceManager and the HDFS NameNode interfaces are available on a Cloud Dataproc cluster master node. The cluster master-host-name is the name of your Cloud Dataproc cluster followed by an -m suffix-for example, if your cluster is named "my-cluster", the master-host-name would be "my-cluster-m".
Reference: https://cloud.google.com/dataproc/docs/concepts/cluster-web-interfaces#interfaces

NEW QUESTION # 374
You have a BigQuery table that contains customer data, including sensitive information such as names and addresses. You need to share the customer data with your data analytics and consumer support teams securely.
The data analytics team needs to access the data of all the customers, but must not be able to access the sensitive data. The consumer support team needs access to all data columns, but must not be able to access customers that no longer have active contracts. You enforced these requirements by using an authorized dataset and policy tags After implementing these steps, the data analytics team reports that they still have access to the sensitive columns. You need to ensure that the data analytics team does not have access to restricted data What should you do?
Choose 2 answers

A. Remove the bigquery. dataViewer role from the data analytics team on the authorized datasets.
B. Ensure that the data analytics team members do not have the Data Catalog Fine-Grained Reader role for the policy tags.
C. Enforce access control in the policy tag taxonomy.
D. Replace the authorized dataset with an authorized view Use row-level security and apply filter_ expression to limit data access.
E. Create two separate authorized datasets; one for the data analytics team and another for the consumer support team.

Answer: B,C

Explanation:
To ensure that the data analytics team does not have access to sensitive columns, you should:
* B. Ensure that the data analytics team members do not have the Data Catalog Fine-Grained Reader role for the policy tags. This role allows users to read metadata for data assets that have policy tags applied, which could include sensitive information.
* C. Enforce access control in the policy tag taxonomy. By setting access control at the policy tag level, you can restrict access to specific columns within a dataset, ensuring that only authorized users can view sensitive data.

NEW QUESTION # 375
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 that makes a queue in a NoSQL database
C. Use Cloud Composer to subscribe to a Pub/Sub tope and can the Python API.
D. Write an application hosted on a Compute Engine instance that makes a push subscription to the Pub/Sub topic

Answer: B

NEW QUESTION # 376
You have spent a few days loading data from comma-separated values (CSV) files into the Google BigQuery table CLICK_STREAM. The column DT stores the epoch time of click events. For convenience, you chose a simple schema where every field is treated as the STRING type. Now, you want to compute web session durations of users who visit your site, and you want to change its data type to the TIMESTAMP. You want to minimize the migration effort without making future queries computationally expensive. What should you do?

A. Construct a query to return every row of the table CLICK_STREAM, while using the built-in function to cast strings from the column DT into TIMESTAMP values. Run the query into a destination table NEW_CLICK_STREAM, in which the column TS is the TIMESTAMP type. the table NEW_CLICK_STREAM instead of the table CLICK_STREAM from now on. In the future, new data is loaded into the table NEW_CLICK_STREAM.
B. Add two columns to the table CLICK STREAM: TS of the TIMESTAMP type and IS_NEW of the BOOLEAN type. Reload all data in append mode. For each appended row, set the value of IS_NEW to true. For future queries, the column TS instead of the column DT, with the WHERE clause ensuring that the value of IS_NEW must be true.
C. Delete the table CLICK_STREAM, and then re-create it such that the column DT is of the TIMESTAMP type. Reload the data.
D. Add a column TS of the TIMESTAMP type to the table CLICK_STREAM, and populate the numeric values from the column TS for each row. the column TS instead of the column DT from now on.
E. Create a view CLICK_STREAM_V, where strings from the column DT are cast into TIMESTAMP values. the view CLICK_STREAM_V instead of the table CLICK_STREAM from now on.

Answer: A

Explanation:
It's better to create a new table and delete old one when we are changing the datatype is permanent. View is not suitable because every time the query will run and additional charges will be applied.

NEW QUESTION # 377
......

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