Intereactive Google Professional-Data-Engineer Testing Engine - Professional-Data-Engineer New Learning Materials

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Google Professional-Data-Engineer Exam Syllabus Topics:

Section	Weight	Objectives
Building and operationalizing data processing systems	25%	- Building data pipelines 1. Transforming and cleaning data 2. Ingesting data from various sources 3. Orchestrating data workflows - Deploying and managing systems 1. Managing infrastructure and resources 2. Monitoring and logging data processes
Designing data processing systems	20%	- Designing for business requirements 1. Designing for reliability and fault tolerance 2. Selecting appropriate storage solutions 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
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
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. Model serving and monitoring 2. Optimizing model performance and cost
Maintaining and automating data workloads	18%	- Resource optimization 1. Choosing appropriate compute and storage options 2. Cost management and resource allocation - Automation and repeatability 1. Automating deployment and updates 2. Implementing CI/CD for data systems

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Quiz Google - Professional-Data-Engineer Useful Intereactive Testing Engine

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Google Certified Professional Data Engineer Exam Sample Questions (Q179-Q184):

NEW QUESTION # 179
Your company is running their first dynamic campaign, serving different offers by analyzing real-time data
during the holiday season. The data scientists are collecting terabytes of data that rapidly grows every
hour during their 30-day campaign. They are using Google Cloud Dataflow to preprocess the data and
collect the feature (signals) data that is needed for the machine learning model in Google Cloud Bigtable.
The team is observing suboptimal performance with reads and writes of their initial load of 10 TB of data.
They want to improve this performance while minimizing cost. What should they do?

A. Redesign the schema to use a single row key to identify values that need to be updated frequently in
the cluster.
B. Redefine the schema by evenly distributing reads and writes across the row space of the table.
C. Redesign the schema to use row keys based on numeric IDs that increase sequentially per user
viewing the offers.
D. The performance issue should be resolved over time as the site of the BigDate cluster is increased.

Answer: B

NEW QUESTION # 180
You are collecting loT sensor data from millions of devices across the world and storing the data in BigQuery. Your access pattern is based on recent data tittered by location_id and device_version with the following query:

You want to optimize your queries for cost and performance. How should you structure your data?

A. Cluster table data by create_date, partition by location and device_version
B. Cluster table data by create_date location_id and device_version
C. Partition table data by create_date, location_id and device_version
D. Partition table data by create_date cluster table data by tocation_id and device_version

Answer: D

NEW QUESTION # 181
You are administering a BigQuery on-demand environment. Your business intelligence tool is submitting hundreds of queries each day that aggregate a large (50 TB) sales history fact table at the day and month levels. These queries have a slow response time and are exceeding cost expectations. You need to decrease response time, lower query costs, and minimize maintenance. What should you do?

A. Build authorized views on top of the sales table to aggregate data at the day and month level.
B. Create a scheduled query to build sales day and sales month aggregate tables on an hourly basis.
C. Enable Bl Engine and add your sales table as a preferred table.
D. Build materialized views on top of the sales table to aggregate data at the day and month level.

Answer: D

Explanation:
To improve response times and reduce costs for frequent queries aggregating a large sales history fact table, materialized views are a highly effective solution.

NEW QUESTION # 182
You are building an ELT solution in BigQuery by using Dataform. You need to perform uniqueness and null value checks on your final tables. What should you do to efficiently integrate these checks into your pipeline?

A. Build Dataform assertions into your code
B. Create Dataplex data quality tasks.
C. Build BigQuery user-defined functions (UDFs).
D. Write a Spark-based stored procedure.

Answer: A

Explanation:
Dataform assertions are data quality tests that find rows that violate one or more rules specified in the query. If the query returns any rows, the assertion fails. Dataform runs assertions every time it updates your SQL workflow and alerts you if any assertions fail. You can create assertions for all Dataform table types: tables, incremental tables, views, and materialized views. You can add built-in assertions to the config block of a table, such as nonNull and rowConditions, or create manual assertions with SQLX for advanced use cases.
Dataform automatically creates views in BigQuery that contain the results of compiled assertion queries, which you can inspect to debug failing assertions. Dataform assertions are an efficient way to integrate data quality checks into your ELT solution in BigQuery by using Dataform. References: Test tables with assertions
| Dataform | Google Cloud, Test data quality with assertions | Dataform, Data quality tests and documenting datasets | Dataform, Data quality testing with SQL assertions | Dataform

NEW QUESTION # 183
Your globally distributed auction application allows users to bid on items. Occasionally, users place identical bids at nearly identical times, and different application servers process those bids. Each bid event contains the item, amount, user, and timestamp. You want to collate those bid events into a single location in real time to determine which user bid first. What should you do?

A. Have each application server write the bid events to Cloud Pub/Sub as they occur. Push the events from Cloud Pub/Sub to a custom endpoint that writes the bid event information into Cloud SQL.
B. Create a file on a shared file and have the application servers write all bid events to that file. Process the file with Apache Hadoop to identify which user bid first.
C. Have each application server write the bid events to Google Cloud Pub/Sub as they occur. Use a pull
D. Set up a MySQL database for each application server to write bid events into. Periodically query each of those distributed MySQL databases and update a master MySQL database with bid event information.

Answer: D

Explanation:
subscription to pull the bid events using Google Cloud Dataflow. Give the bid for each item to the user in the bid event that is processed first.

NEW QUESTION # 184
......

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