FREE PROFESSIONAL-DATA-ENGINEER VCE DUMPS & PROFESSIONAL-DATA-ENGINEER STUDY TEST

Free Professional-Data-Engineer Vce Dumps & Professional-Data-Engineer Study Test

Free Professional-Data-Engineer Vce Dumps & Professional-Data-Engineer Study Test

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Google Certified Professional Data Engineer Exam Sample Questions (Q285-Q290):

NEW QUESTION # 285
Which Java SDK class can you use to run your Dataflow programs locally?

  • A. LocalPipelineRunner
  • B. DirectPipelineRunner
  • C. MachineRunner
  • D. LocalRunner

Answer: B

Explanation:
DirectPipelineRunner allows you to execute operations in the pipeline directly, without any optimization.
Useful for small local execution and tests
Reference: https://cloud.google.com/dataflow/java-
sdk/JavaDoc/com/google/cloud/dataflow/sdk/runners/DirectPipelineRunner


NEW QUESTION # 286
You are loading CSV files from Cloud Storage to BigQuery. The files have known data quality issues, including mismatched data types, such as STRINGS and INT64s in the same column, and inconsistent formatting of values such as phone numbers or addresses. You need to create the data pipeline to maintain data quality and perform the required cleansing and transformation. What should you do?

  • A. Use Data Fusion to convert the CSV files lo a self-describing data formal, such as AVRO. before loading the data to BigOuery.
  • B. Create a table with the desired schema, toad the CSV files into the table, and perform the transformations in place using SQL.
  • C. Load the CSV files into a staging table with the desired schema, perform the transformations with SQL. and then write the results to the final destination table.
  • D. Use Data Fusion to transform the data before loading it into BigQuery.

Answer: D

Explanation:
Data Fusion's advantages:
Visual interface: Offers a user-friendly interface for designing data pipelines without extensive coding, making it accessible to a wider range of users.
Built-in transformations: Includes a wide range of pre-built transformations to handle common data quality issues, such as:
Data type conversions
Data cleansing (e.g., removing invalid characters, correcting formatting) Data validation (e.g., checking for missing values, enforcing constraints) Data enrichment (e.g., adding derived fields, joining with other datasets) Custom transformations: Allows for custom transformations using SQL or Java code for more complex cleaning tasks.
Scalability: Can handle large datasets efficiently, making it suitable for processing CSV files with potential data quality issues.
Integration with BigQuery: Integrates seamlessly with BigQuery, allowing for direct loading of transformed data.
Topic 2, MJTelco Case Study
Company Overview
MJTelco is a startup that plans to build networks in rapidly growing, underserved markets around the world. The company has patents for innovative optical communications hardware. Based on these patents, they can create many reliable, high-speed backbone links with inexpensive hardware.
Company Background
Founded by experienced telecom executives, MJTelco uses technologies originally developed to overcome communications challenges in space. Fundamental to their operation, they need to create a distributed data infrastructure that drives real-time analysis and incorporates machine learning to continuously optimize their topologies. Because their hardware is inexpensive, they plan to overdeploy the network allowing them to account for the impact of dynamic regional politics on location availability and cost.
Their management and operations teams are situated all around the globe creating many-to-many relationship between data consumers and provides in their system. After careful consideration, they decided public cloud is the perfect environment to support their needs.
Solution Concept
MJTelco is running a successful proof-of-concept (PoC) project in its labs. They have two primary needs:
Scale and harden their PoC to support significantly more data flows generated when they ramp to more than 50,000 installations.
Refine their machine-learning cycles to verify and improve the dynamic models they use to control topology definition.
MJTelco will also use three separate operating environments - development/test, staging, and production - to meet the needs of running experiments, deploying new features, and serving production customers.
Business Requirements
Scale up their production environment with minimal cost, instantiating resources when and where needed in an unpredictable, distributed telecom user community.
Ensure security of their proprietary data to protect their leading-edge machine learning and analysis.
Provide reliable and timely access to data for analysis from distributed research workers Maintain isolated environments that support rapid iteration of their machine-learning models without affecting their customers.
Technical Requirements
Ensure secure and efficient transport and storage of telemetry data
Rapidly scale instances to support between 10,000 and 100,000 data providers with multiple flows each.
Allow analysis and presentation against data tables tracking up to 2 years of data storing approximately 100m records/day Support rapid iteration of monitoring infrastructure focused on awareness of data pipeline problems both in telemetry flows and in production learning cycles.
CEO Statement
Our business model relies on our patents, analytics and dynamic machine learning. Our inexpensive hardware is organized to be highly reliable, which gives us cost advantages. We need to quickly stabilize our large distributed data pipelines to meet our reliability and capacity commitments.
CTO Statement
Our public cloud services must operate as advertised. We need resources that scale and keep our data secure. We also need environments in which our data scientists can carefully study and quickly adapt our models. Because we rely on automation to process our data, we also need our development and test environments to work as we iterate.
CFO Statement
The project is too large for us to maintain the hardware and software required for the data and analysis. Also, we cannot afford to staff an operations team to monitor so many data feeds, so we will rely on automation and infrastructure. Google Cloud's machine learning will allow our quantitative researchers to work on our high-value problems instead of problems with our data pipelines.


NEW QUESTION # 287
You want to process payment transactions in a point-of-sale application that will run on Google Cloud Platform. Your user base could grow exponentially, but you do not want to manage infrastructure scaling.
Which Google database service should you use?

  • A. Cloud Datastore
  • B. Cloud SQL
  • C. Cloud Bigtable
  • D. BigQuery

Answer: B


NEW QUESTION # 288
Your organization has been collecting and analyzing data in Google BigQuery for 6 months. The majority of the data analyzed is placed in a time-partitioned table named events_partitioned. To reduce the cost of queries, your organization created a view called events, which queries only the last 14 days of dat
a. The view is described in legacy SQL. Next month, existing applications will be connecting to BigQuery to read the events data via an ODBC connection. You need to ensure the applications can connect. Which two actions should you take? (Choose two.)

  • A. Create a new view over events_partitioned using standard SQL
  • B. Create a new partitioned table using a standard SQL query
  • C. Create a Google Cloud Identity and Access Management (Cloud IAM) role for the ODBC connection and shared "events"
  • D. Create a new view over events using standard SQL
  • E. Create a service account for the ODBC connection to use for authentication

Answer: C,D


NEW QUESTION # 289
Your company is performing data preprocessing for a learning algorithm in Google Cloud Dataflow.
Numerous data logs are being are being generated during this step, and the team wants to analyze them.
Due to the dynamic nature of the campaign, the data is growing exponentially every hour. The data scientists have written the following code to read the data for a new key features in the logs.
BigQueryIO.Read
.named("ReadLogData")
.from("clouddataflow-readonly:samples.log_data")
You want to improve the performance of this data read. What should you do?

  • A. Use .fromQuery operation to read specific fields from the table.
  • B. Use of both the Google BigQuery TableSchema and TableFieldSchema classes.
  • C. Specify the Tableobject in the code.
  • D. Call a transform that returns TableRow objects, where each element in the PCollexction represents a single row in the table.

Answer: D


NEW QUESTION # 290
......

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