Google Certified Professional Data Engineer Exam brain dumps, Professional-Data-Engineer dumps pdf

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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: Ensuring solution quality	28%	- Reliability and performance 1. Monitoring pipelines and workloads 2. Fault tolerance and recovery strategies - Security and governance 1. Data governance and compliance 2. IAM and access control in GCP
Topic 3: 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. Event-driven vs batch architectures 2. Latency, throughput, and consistency trade-offs
Topic 4: Building and operationalizing data processing systems	24%	- Data processing and transformation 1. ETL/ELT pipeline design 2. Using Dataproc, Dataflow, and BigQuery SQL - Data ingestion and integration 1. Batch ingestion pipelines (BigQuery, Cloud Storage) 2. Streaming ingestion (Pub/Sub, Dataflow)

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Google Certified Professional Data Engineer Exam Sample Questions (Q16-Q21):

NEW QUESTION # 16
You work for a farming company. You have one BigQuery table named sensors, which is about
500 MB and contains the list of your 5000 sensors, with columns for id, name, and location. This table is updated every hour. Each sensor generates one metric every 30 seconds along with a timestamp, which you want to store in BigQuery. You want to run an analytical query on the data once a week for monitoring purposes. You also want to minimize costs. What data model should you use?

A. 1. Create a metrics table partitioned by timestamp.
2. Create a sensorId column in the metrics table, that points to the id column in the sensors table.
3. Use an INSERT statement every 30 seconds to append new metrics to the metrics table.
4. Join the two tables, if needed, when running the analytical query.
B. 1. Create a metrics column in the sensors table.
2. Set RECORD type and REPEATED mode for the metrics column.
3. Use an INSERT statement every 30 seconds to add new metrics.
C. 1. Create a metrics table partitioned by timestamp.
2. Create a sensorId column in the metrics table, which points to the id column in the sensors table.
3. Use an UPDATE statement every 30 seconds to append new metrics to the metrics table.
4. Join the two tables, if needed, when running the analytical query.
D. 1. Create a metrics column in the sensors table.
2. Set RECORD type and REPEATED mode for the metrics column.
3. Use an UPDATE statement every 30 seconds to add new metrics.

Answer: A

NEW QUESTION # 17
Business owners at your company have given you a database of bank transactions. Each row contains the
user ID, transaction type, transaction location, and transaction amount. They ask you to investigate what
type of machine learning can be applied to the data. Which three machine learning applications can you
use? (Choose three.)

A. Supervised learning to predict the location of a transaction.
B. Clustering to divide the transactions into N categories based on feature similarity.
C. Unsupervised learning to determine which transactions are most likely to be fraudulent.
D. Reinforcement learning to predict the location of a transaction.
E. Supervised learning to determine which transactions are most likely to be fraudulent.
F. Unsupervised learning to predict the location of a transaction.

Answer: B,C,D

NEW QUESTION # 18
You architect a system to analyze seismic data. Your extract, transform, and load (ETL) process runs as a series of MapReduce jobs on an Apache Hadoop cluster. The ETL process takes days to process a data set because some steps are computationally expensive. Then you discover that a sensor calibration step has been omitted. How should you change your ETL process to carry out sensor calibration systematically in the future?

A. Modify the transformMapReduce jobs to apply sensor calibration before they do anything else.
B. Introduce a new MapReduce job to apply sensor calibration to raw data, and ensure all other MapReduce jobs are chained after this.
C. Add sensor calibration data to the output of the ETL process, and document that all users need to apply sensor calibration themselves.
D. Develop an algorithm through simulation to predict variance of data output from the last MapReduce job based on calibration factors, and apply the correction to all data.

Answer: A

NEW QUESTION # 19
You recently deployed several data processing jobs into your Cloud Composer 2 environment.
You notice that some tasks are failing in Apache Airflow. On the monitoring dashboard, you see an increase in the total workers memory usage, and there were worker pod evictions. You need to resolve these errors. What should you do? (Choose two.)

A. Increase the memory available to the Airflow workers.
B. Increase the memory available to the Airflow triggerer.
C. Increase the Cloud Composer 2 environment size from medium to large.
D. Increase the maximum number of workers and reduce worker concurrency.
E. Increase the directed acyclic graph (DAG) file parsing interval.

Answer: A,D

NEW QUESTION # 20
Your company is selecting a system to centralize data ingestion and delivery. You are considering messaging and data integration systems to address the requirements. The key requirements are:
- The ability to seek to a particular offset in a topic, possibly back
to the start of all data ever captured
- Support for publish/subscribe semantics on hundreds of topics
- Retain per-key ordering
Which system should you choose?

A. Dataflow
B. Apache Kafka
C. Cloud Storage
D. Firebase Cloud Messaging

Answer: B

Explanation:
These are the functionalities which are currently lagging/not-available with Pub/Sub.

NEW QUESTION # 21
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