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Google GCP-DE Exam Syllabus Topics:

Section	Weight	Objectives
Operationalizing data and ML pipelines	30%	- Monitoring and troubleshooting 1. Performance optimization and debugging 2. Logging and observability - Pipeline automation and orchestration 1. Scheduling and monitoring pipelines 2. Cloud Composer workflows
Building and operationalizing data processing systems	30%	- Data pipeline implementation 1. Dataflow pipeline development 2. Dataproc and Spark-based processing - Data ingestion and transformation 1. Pub/Sub streaming ingestion 2. ETL/ELT workflows
Maintaining and optimizing data and ML solutions	20%	- Security and governance 1. Data encryption and compliance 2. IAM and access control - Machine learning integration 1. Vertex AI integration for pipelines 2. BigQuery ML usage
Designing data processing systems	20%	- Data pipeline architecture design 1. Scalable data ingestion design 2. Batch vs streaming data processing selection - Storage and data modeling 1. Data lake architecture on Google Cloud Storage 2. Data warehouse design using BigQuery

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Google Data Engineer Sample Questions (Q55-Q60):

NEW QUESTION # 55
You are integrating one of your internal IT applications and Google BigQuery, so users can query BigQuery from the application's interface. You do not want individual users to authenticate to BigQuery and you do not want to give them access to the dataset. You need to securely access BigQuery from your IT application.
What should you do?

A. Create a service account and grant dataset access to that accoun
B. Create a dummy user and grant dataset access to that use
C. Create groups for your users and give those groups access to the dataset
D. Store the username and password for that user in a file on the files system, and use those credentials to access the BigQuery dataset
E. Use the service account's private key to access the dataset
F. Integrate with a single sign-on (SSO) platform, and pass each user's credentials along with the query request

Answer: A

NEW QUESTION # 56
Which of the following is NOT true about Dataflow pipelines?

A. Dataflow pipelines can consume data from other Google Cloud services
B. Dataflow pipelines use a unified programming model, so can work both with streaming and batch data sources
C. Dataflow pipelines are tied to Dataflow, and cannot be run on any other runner
D. Dataflow pipelines can be programmed in Java

Answer: C

Explanation:
Dataflow pipelines can also run on alternate runtimes like Spark and Flink, as they are built using the Apache Beam SDKs Reference: https://cloud.google.com/dataflow/

NEW QUESTION # 57
MJTelco needs you to create a schema in Google Bigtable that will allow for the historical analysis of the last 2 years of records. Each record that comes in is sent every 15 minutes, and contains a unique identifier of the device and a data record. The most common query is for all the data for a given device for a given day. Which schema should you use?

A. Rowkey: dateColumn data: device_id, data_point
B. Rowkey: data_pointColumn data: device_id, date
C. Rowkey: date#device_idColumn data: data_point
D. Rowkey: device_idColumn data: date, data_point
E. Rowkey: date#data_pointColumn data: device_id

Answer: B

NEW QUESTION # 58
You are building a model to predict whether or not it will rain on a given day. You have thousands of input features and want to see if you can improve training speed by removing some features while having a minimum effect on model accuracy. What can you do?

A. Remove the features that have null values for more than 50% of the training records.
B. Combine highly co-dependent features into one representative feature.
C. Instead of feeding in each feature individually, average their values in batches of 3.
D. Eliminate features that are highly correlated to the output labels.

Answer: B

NEW QUESTION # 59
You are developing an application on Google Cloud that will automatically generate subject labels for users' blog posts. You are under competitive pressure to add this feature quickly, and you have no additional developer resources. No one on your team has experience with machine learning. What should you do?

A. Call the model from your application and process the results as labels.
B. Call the model from your application and process the results as labels.
C. Call the Cloud Natural Language API from your applicatio
D. Deploy the model using Cloud Machine Learning Engin
E. Build and train a text classification model using TensorFlo
F. Call the Cloud Natural Language API from your applicatio
G. Process the generated Sentiment Analysis as labels.
H. Build and train a text classification model using TensorFlo
I. Process the generated Entity Analysis as labels.
J. Deploy the model using a KubernetesEngine cluste

Answer: I

NEW QUESTION # 60
......

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