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Google Professional-Data-Engineer Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Operationalizing machine learning models	26%	- Model deployment and monitoring 1. Online vs batch prediction 2. Model monitoring and drift detection - ML pipeline integration 1. Feature engineering and feature stores 2. Vertex AI pipeline deployment
Topic 2: Designing data processing systems	22%	- Data architecture and storage design 1. Designing scalable and cost-effective data models 2. Choosing appropriate data storage solutions (relational, NoSQL, data warehouse) - Batch and streaming data processing design 1. Latency, throughput, and consistency trade-offs 2. Event-driven vs batch architectures
Topic 3: Building and operationalizing data processing systems	24%	- Data ingestion and integration 1. Streaming ingestion (Pub/Sub, Dataflow) 2. Batch ingestion pipelines (BigQuery, Cloud Storage) - Data processing and transformation 1. Using Dataproc, Dataflow, and BigQuery SQL 2. ETL/ELT pipeline design
Topic 4: Ensuring solution quality	28%	- Reliability and performance 1. Monitoring pipelines and workloads 2. Fault tolerance and recovery strategies - Security and governance 1. IAM and access control in GCP 2. Data governance and compliance

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Google Certified Professional Data Engineer Exam Sample Questions (Q115-Q120):

NEW QUESTION # 115
You are designing a basket abandonment system for an ecommerce company. The system will send a message to a user based on these rules:
- No interaction by the user on the site for 1 hour
- Has added more than $30 worth of products to the basket
- Has not completed a transaction
You use Google Cloud Dataflow to process the data and decide if a message should be sent.
How should you design the pipeline?

A. Use a global window with a time based trigger with a delay of 60 minutes.
B. Use a fixed-time window with a duration of 60 minutes.
C. Use a session window with a gap time duration of 60 minutes.
D. Use a sliding time window with a duration of 60 minutes.

Answer: C

Explanation:
It will send a message per user after that user is inactive for 60 minutes. Session window works well for capturing a session per user basis.

NEW QUESTION # 116
Your company has hired a new data scientist who wants to perform complicated analyses across very large datasets stored in Google Cloud Storage and in a Cassandra cluster on Google Compute Engine. The scientist primarily wants to create labelled data sets for machine learning projects, along with some visualization tasks.
She reports that her laptop is not powerful enough to perform her tasks and it is slowing her down. You want to help her perform her tasks. What should you do?

A. Host a visualization tool on a VM on Google Compute Engine.
B. Run a local version of Jupiter on the laptop.
C. Deploy Google Cloud Datalab to a virtual machine (VM) on Google Compute Engine.
D. Grant the user access to Google Cloud Shell.

Answer: D

Explanation:
Explanation/Reference:

NEW QUESTION # 117
Which of these statements about BigQuery caching is true?

A. BigQuery caches query results for 48 hours.
B. There is no charge for a query that retrieves its results from cache.
C. By default, a query's results are not cached.
D. Query results are cached even if you specify a destination table.

Answer: B

Explanation:
When query results are retrieved from a cached results table, you are not charged for the query.
BigQuery caches query results for 24 hours, not 48 hours. Query results are not cached if you specify a destination table. A query's results are always cached except under certain conditions, such as if you specify a destination table.
Reference:
https://cloud.google.com/bigquery/querying-data#query-caching

NEW QUESTION # 118
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. Delete the table CLICK_STREAM, and then re-create it such that the column DT is of the TIMESTAMP type. Reload the data.
B. 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.
C. 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.
D. 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.
E. 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.

Answer: C

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 # 119
The _________ for Cloud Bigtable makes it possible to use Cloud Bigtable in a Cloud Dataflow pipeline.

A. BigQuery Data Transfer Service
B. Cloud Dataflow connector
C. BiqQuery API
D. DataFlow SDK

Answer: B

Explanation:
The Cloud Dataflow connector for Cloud Bigtable makes it possible to use Cloud Bigtable in a Cloud Dataflow pipeline. You can use the connector for both batch and streaming operations.
Reference:
https://cloud.google.com/bigtable/docs/dataflow-hbase

NEW QUESTION # 120
......

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