Professional-Data-Engineer Exam Price, Professional-Data-Engineer Valid Test Forum

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Google Professional-Data-Engineer Exam consists of multiple-choice and multiple-select questions, as well as scenario-based questions that require the candidate to demonstrate their problem-solving skills. Professional-Data-Engineer exam is three hours long, and candidates must score at least 70% to pass. Professional-Data-Engineer exam fee is $200, and it is available in English, Japanese, and Spanish.

To prepare for the exam, candidates can take advantage of a range of resources, including Google Cloud’s official documentation, online courses, and practice exams. They can also join study groups and attend events and webinars to connect with other professionals and learn from their experiences.

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Operationalize ML Models

Select the Relevant Training & Service Infrastructure: The consideration for this topic includes distributed versus single machine, hardware accelerators (such as TPU and GPU), and edge compute usage;
Deploy Machine Learning Pipelines: This objective requires your competence in ingesting relevant data, continuous evaluation, and retraining of ML models (Kuberflow, BigQuery Machine Learning, Cloud Machine Learning Engine, and Spark Machine Learning);
Measure, Troubleshoot & Monitor Machine Learning Models: The focus of this subtopic includes the effect of dependencies on machine learning models. It will also measure the examinees’ understanding of machine learning terminologies, such as features, regression, labels, classification, models, recommendation, evaluation metrics, and unsupervised & supervised learning. Moreover, it will also assess their knowledge of common sources of error such as assumptions regarding data.
Leverage Pre-Built Machine Learning Models as a Service: It covers one’s knowledge and skills in customizing machine learning APIs, including Auto ML text and Auto ML Vision. It also covers the conversational experiences, such as Dialogflow as well as machine learning APIs, including Speech API and Vision API;

Google Certified Professional Data Engineer Exam Sample Questions (Q95-Q100):

NEW QUESTION # 95
You receive data files in CSV format monthly from a third party. You need to cleanse this data, but every third month the schema of the files changes. Your requirements for implementing these transformations include:
* Executing the transformations on a schedule
* Enabling non-developer analysts to modify transformations
* Providing a graphical tool for designing transformations
What should you do?

A. Use Cloud Dataprep to build and maintain the transformation recipes, and execute them on a scheduled basis
B. Load each month's CSV data into BigQuery, and write a SQL query to transform the data to a standard schema. Merge the transformed tables together with a SQL query
C. Use Apache Spark on Cloud Dataproc to infer the schema of the CSV file before creating a Dataframe.
Then implement the transformations in Spark SQL before writing the data out to Cloud Storage and loading into BigQuery
D. Help the analysts write a Cloud Dataflow pipeline in Python to perform the transformation. The Python code should be stored in a revision control system and modified as the incoming data's schema changes

Answer: C

NEW QUESTION # 96
You operate an IoT pipeline built around Apache Kafka that normally receives around 5000 messages per second. You want to use Google Cloud Platform to create an alert as soon as the moving average over 1 hour drops below 4000 messages per second. What should you do?

A. Consume the stream of data in Cloud Dataflow using Kafka IO. Set a fixed time window of 1 hour. Compute the average when the window closes, and send an alert if the average is less than 4000 messages.
B. Use Kafka Connect to link your Kafka message queue to Cloud Pub/Sub. Use a Cloud Dataflow template to write your messages from Cloud Pub/Sub to BigQuery. Use Cloud Scheduler to run a script every five minutes that counts the number of rows created in BigQuery in the last hour. If that number falls below
4000, send an alert.
C. Consume the stream of data in Cloud Dataflow using Kafka IO. Set a sliding time window of 1 hour every 5 minutes. Compute the average when the window closes, and send an alert if the average is less than 4000 messages.
D. Use Kafka Connect to link your Kafka message queue to Cloud Pub/Sub. Use a Cloud Dataflow template to write your messages from Cloud Pub/Sub to Cloud Bigtable. Use Cloud Scheduler to run a script every hour that counts the number of rows created in Cloud Bigtable in the last hour. If that number falls below
4000, send an alert.

Answer: D

NEW QUESTION # 97
You're training a model to predict housing prices based on an available dataset with real estate properties. Your plan is to train a fully connected neural net, and you've discovered that the dataset contains latitude and longtitude of the property. Real estate professionals have told you that the location of the property is highly influential on price, so you'd like to engineer a feature that incorporates this physical dependency.
What should you do?

A. Create a feature cross of latitude and longtitude, bucketize it at the minute level and use L2 regularization during optimization.
B. Provide latitude and longtitude as input vectors to your neural net.
C. Create a numeric column from a feature cross of latitude and longtitude.
D. Create a feature cross of latitude and longtitude, bucketize at the minute level and use L1 regularization during optimization.

Answer: D

Explanation:
Reference https://cloud.google.com/bigquery/docs/gis-dataa

NEW QUESTION # 98
Which of the following is NOT one of the three main types of triggers that Dataflow supports?

A. Trigger based on element count
B. Trigger that is a combination of other triggers
C. Trigger based on time
D. Trigger based on element size in bytes

Answer: D

Explanation:
There are three major kinds of triggers that Dataflow supports: 1. Time-based triggers 2. Data-driven triggers.
You can set a trigger to emit results from a window when that window has received a certain number of data elements. 3. Composite triggers. These triggers combine multiple time-based or data-driven triggers in some logical way Reference: https://cloud.google.com/dataflow/model/triggers

NEW QUESTION # 99
What are two methods that can be used to denormalize tables in BigQuery?

A. 1) Use nested repeated fields; 2) Use a partitioned table
B. 1) Use a partitioned table; 2) Join tables into one table
C. 1) Split table into multiple tables; 2) Use a partitioned table
D. 1) Join tables into one table; 2) Use nested repeated fields

Answer: D

Explanation:
The conventional method of denormalizing data involves simply writing a fact, along with all its dimensions, into a flat table structure. For example, if you are dealing with sales transactions, you would write each individual fact to a record, along with the accompanying dimensions such as order and customer information.
The other method for denormalizing data takes advantage of BigQuery's native support for nested and repeated structures in JSON or Avro input data. Expressing records using nested and repeated structures can provide a more natural representation of the underlying data. In the case of the sales order, the outer part of a JSON structure would contain the order and customer information, and the inner part of the structure would contain the individual line items of the order, which would be represented as nested, repeated elements.
Reference: https://cloud.google.com/solutions/bigquery-data-warehouse#denormalizing_data

NEW QUESTION # 100
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Professional-Data-Engineer Exam Price, Professional-Data-Engineer Valid Test Forum

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Operationalize ML Models

Google Certified Professional Data Engineer Exam Sample Questions (Q95-Q100):

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