Google Professional-Data-Engineer Exam Actual Questions

Name: Google Cloud Data Engineer Professional
Brand: QA4Exam
SKU: Professional-Data-Engineer
Price: 30 USD
Availability: InStock
Rating: 4.9 (680 reviews)

The questions for Professional-Data-Engineer were last updated on Oct 4, 2024.

Viewing page 1 out of 74 pages.
Viewing questions 1-5 out of 371 questions

Unlock Access to All 371 Questions & Answers

Question No. 1

You issue a new batch job to Dataflow. The job starts successfully, processes a few elements, and then suddenly fails and shuts down. You navigate to the Dataflow monitoring interface where you find errors related to a particular DoFn in your pipeline. What is the most likely cause of the errors?

AExceptions in worker code

BJob validation

CGraph or pipeline construction

DInsufficient permissions

Show Answer

Correct Answer: A

https://cloud.google.com/dataflow/docs/guides/troubleshooting-your-pipeline#detect_an_exception_in_worker_code While your job is running, you might encounter errors or exceptions in your worker code. These errors generally mean that the DoFns in your pipeline code have generated unhandled exceptions, which result in failed tasks in your Dataflow job. Exceptions in user code (for example, your DoFn instances) are reported in the Dataflow monitoring interface.

Question No. 2

You are developing a new deep teaming model that predicts a customer's likelihood to buy on your ecommerce site. Alter running an evaluation of the model against both the original training data and new test data, you find that your model is overfitting the dat

a. You want to improve the accuracy of the model when predicting new data. What should you do?

AIncrease the size of the training dataset, and increase the number of input features.

BIncrease the size of the training dataset, and decrease the number of input features.

CReduce the size of the training dataset, and increase the number of input features.

DReduce the size of the training dataset, and decrease the number of input features.

Show Answer

Correct Answer: B

https://machinelearningmastery.com/impact-of-dataset-size-on-deep-learning-model-skill-and-performance-estimates/

Question No. 3

You are implementing workflow pipeline scheduling using open source-based tools and Google Kubernetes Engine (GKE). You want to use a Google managed service to simplify and automate the task. You also want to accommodate Shared VPC networking considerations. What should you do?

AUse Dataflow for your workflow pipelines. Use Cloud Run triggers for scheduling.

BUse Dataflow for your workflow pipelines. Use shell scripts to schedule workflows.

CUse Cloud Composer in a Shared VPC configuration. Place the Cloud Composer resources in the host project.

DUse Cloud Composer in a Shared VPC configuration. Place the Cloud Composer resources in the service project.

Show Answer

Correct Answer: D

Shared VPC requires that you designate a host project to which networks and subnetworks belong and a service project, which is attached to the host project. When Cloud Composer participates in a Shared VPC, the Cloud Composer environment is in the service project. Reference: https://cloud.google.com/composer/docs/how-to/managing/configuring-shared-vpc

Question No. 4

You are implementing a chatbot to help an online retailer streamline their customer service. The chatbot must be able to respond to both text and voice inquiries. You are looking for a low-code or no-code option, and you want to be able to easily train the chatbot to provide answers to keywords. What should you do?

AUse the Speech-to-Text API to build a Python application in App Engine.

BUse the Speech-to-Text API to build a Python application in a Compute Engine instance.

CUse Dialogflow for simple queries and the Speech-to-Text API for complex queries.

DUse Dialogflow to implement the chatbot. defining the intents based on the most common queries collected.

Show Answer

Correct Answer: D

Dialogflow is a conversational AI platform that allows for easy implementation of chatbots without needing to code. It has built-in integration for both text and voice input via APIs like Cloud Speech-to-Text. Defining intents and entity types allows you to map common queries and keywords to responses. This would provide a low/no-code way to quickly build and iteratively improve the chatbot capabilities.

https://cloud.google.com/dialogflow/docs Dialogflow is a natural language understanding platform that makes it easy to design and integrate a conversational user interface into your mobile app, web application, device, bot, interactive voice response system, and so on. Using Dialogflow, you can provide new and engaging ways for users to interact with your product. Dialogflow can analyze multiple types of input from your customers, including text or audio inputs (like from a phone or voice recording). It can also respond to your customers in a couple of ways, either through text or with synthetic speech.

Question No. 5

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?

AUse Data Fusion to transform the data before loading it into BigQuery.

BLoad 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.

CCreate a table with the desired schema, toad the CSV files into the table, and perform the transformations in place using SQL.

DUse Data Fusion to convert the CSV files lo a self-describing data formal, such as AVRO. before loading the data to BigOuery.

Show Answer

Correct Answer: A

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.

Unlock All Questions for Google Professional-Data-Engineer Exam

Full Exam Access, Actual Exam Questions, Validated Answers, Anytime Anywhere, No Download Limits, No Practice Limits

Get All 371 Questions & Answers