Free PDF Quiz Professional-Data-Engineer - The Best Latest Google Certified Professional Data Engineer Exam Real Test

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Google Professional-Data-Engineer Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Operationalizing machine learning models	20%	- Deploying and maintaining ML models 1. Optimizing model performance and cost 2. Model serving and monitoring - Preparing data for ML 1. Handling structured and unstructured data 2. Feature engineering and data preparation
Topic 2: 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. Ingesting data from various sources 3. Orchestrating data workflows
Topic 3: Ensuring solution quality and reliability	17%	- Testing and validating data systems 1. Performance and scalability testing 2. Data quality validation - Troubleshooting and optimization 1. Diagnosing performance issues 2. Optimizing queries and workloads
Topic 4: Maintaining and automating data workloads	18%	- Automation and repeatability 1. Implementing CI/CD for data systems 2. Automating deployment and updates - Resource optimization 1. Choosing appropriate compute and storage options 2. Cost management and resource allocation
Topic 5: 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

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Google Certified Professional Data Engineer Exam Sample Questions (Q405-Q410):

NEW QUESTION # 405
You are building a new application that you need to collect data from in a scalable way. Data arrives continuously from the application throughout the day, and you expect to generate approximately 150 GB of JSON data per day by the end of the year. Your requirements are:
* Decoupling producer from consumer
* Space and cost-efficient storage of the raw ingested data, which is to be stored indefinitely
* Near real-time SQL query
* Maintain at least 2 years of historical data, which will be queried with SQL Which pipeline should you use to meet these requirements?

A. Create an application that provides an API. Write a tool to poll the API and write data to Cloud Storage as gzipped JSON files.
B. Create an application that writes to a Cloud SQL database to store the data. Set up periodic exports of the database to write to Cloud Storage and load into BigQuery.
C. Create an application that publishes events to Cloud Pub/Sub, and create a Cloud Dataflow pipeline that transforms the JSON event payloads to Avro, writing the data to Cloud Storage and BigQuery.
D. Create an application that publishes events to Cloud Pub/Sub, and create Spark jobs on Cloud Dataproc to convert the JSON data to Avro format, stored on HDFS on Persistent Disk.

Answer: C

NEW QUESTION # 406
You are deploying a batch pipeline in Dataflow. This pipeline reads data from Cloud Storage, transforms the data, and then writes the data into BigQuery. The security team has enabled an organizational constraint in Google Cloud, requiring all Compute Engine instances to use only internal IP addresses and no external IP addresses. What should you do?

A. Ensure that the firewall rules allow access to Cloud Storage and BigQuery. Use Dataflow with only internal IPs.
B. Ensure that Private Google Access is enabled in the subnetwork. Use Dataflow with only internal IP addresses.
C. Ensure that your workers have network tags to access Cloud Storage and BigQuery. Use Dataflow with only internal IP addresses.
D. Create a VPC Service Controls perimeter that contains the VPC network and add Dataflow, Cloud Storage, and BigQuery as allowed services in the perimeter. Use Dataflow with only internal IP addresses.

Answer: B

NEW QUESTION # 407
You are using Google BigQuery as your data warehouse. Your users report that the following simple query is running very slowly, no matter when they run the query:
SELECT country, state, city FROM [myproject:mydataset.mytable] GROUP BY country You check the query plan for the query and see the following output in the Read section of Stage:1:

What is the most likely cause of the delay for this query?

A. Users are running too many concurrent queries in the system
B. Either the state or the city columns in the [myproject:mydataset.mytable] table have too many NULL values
C. Most rows in the [myproject:mydataset.mytable] table have the same value in the country column, causing data skew
D. The [myproject:mydataset.mytable] table has too many partitions

Answer: A

NEW QUESTION # 408
You are designing storage for very large text files for a data pipeline on Google Cloud. You want to support ANSI SQL queries. You also want to support compression and parallel load from the input locations using Google recommended practices. What should you do?

A. Transform text files to compressed Avro using Cloud Dataflow. Use Cloud Storage and BigQuery permanent linked tables for query.
B. Compress text files to gzip using the Grid Computing Tools. Use Cloud Storage, and then import into Cloud Bigtable for query.
C. Compress text files to gzip using the Grid Computing Tools. Use BigQuery for storage and query.
D. Transform text files to compressed Avro using Cloud Dataflow. Use BigQuery for storage and query.

Answer: B

Explanation:
Explanation/Reference:

NEW QUESTION # 409
You have some data, which is shown in the graphic below. The two dimensions are X and Y, and the
shade of each dot represents what class it is. You want to classify this data accurately using a linear
algorithm. To do this you need to add a synthetic feature. What should the value of that feature be?

A. cos(X)
B. Y
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