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Google GCP-DE Exam Syllabus Topics:

Section	Weight	Objectives
Ingesting and processing data	25%	- Transforming data 1. Applying data processing logic 2. Managing schema evolution 3. Optimizing transformations for performance - Building data pipelines 1. Implementing ingestion mechanisms 2. Handling data quality and consistency 3. Developing batch and streaming workflows
Preparing data for analysis and machine learning	13%	- Enabling data analysis 1. Implementing data sharing and governance 2. Optimizing query performance 3. Preparing datasets for querying and reporting - Preparing data for ML 1. Feature engineering and data preparation 2. Supporting model training and serving
Designing data processing systems	24%	- Planning data solutions 1. Selecting appropriate storage solutions 2. Designing for reliability, scalability, and efficiency 3. Planning for data security and compliance - Designing data pipelines 1. Defining architecture for batch and streaming processing 2. Designing for data transformation and enrichment 3. Planning for data migration and integration
Maintaining and automating data workloads	18%	- Operationalizing workloads 1. Orchestrating and scheduling jobs 2. Ensuring reliability and recoverability 3. Monitoring and troubleshooting pipelines - Automation and optimization 1. Automating deployment and management 2. Optimizing resource usage and costs
Storing and managing data	20%	- Implementing storage solutions 1. Using data storage services appropriately 2. Managing data lifecycle and retention 3. Designing data warehouses and data lakes - Optimizing storage performance and cost 1. Implementing cost optimization strategies 2. Configuring storage for access patterns

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Google Data Engineer Sample Questions (Q26-Q31):

NEW QUESTION # 26
You are building a new application that you need to collect data from in a scalable way. Data arrives continuously from the application throughout the day, and you expect to generate approximately 150 GB of JSON data per day by the end of the year. Your requirements are: Decoupling producer from consumer Space and cost-efficient storage of the raw ingested data, which is to be stored indefinitely Near real-time SQL query Maintain at least 2 years of historical data, which will be queried with SQ Which pipeline should you use to meet these requirements?

A. Set up periodic exports of the database to write to Cloud Storage and load into BigQuery.
B. Create an application that publishes events to Cloud Pub/Sub, and create a Cloud Dataflow pipeline that transforms the JSON event payloads to Avro, writing the data to Cloud Storage and BigQuery.
C. Create an application that publishes events to Cloud Pub/Sub, and create Spark jobs on Cloud Dataproc to convert the JSON data to Avro format, stored on HDFS on Persistent Disk.
D. Create an application that provides an AP
E. Create an application that writes to a Cloud SQL database to store the dat
F. Write a tool to poll the API and write data to Cloud Storage as gzipped JSON files.

Answer: D

NEW QUESTION # 27
Which of these is not a supported method of putting data into a partitioned table?

A. Use ORDER BY to put a table's rows into chronological order and then change the table's type to "Partitioned".
B. Create a partitioned table and stream new records to it every day.
C. Run a query to get the records for a specific day from an existing table and for the destination table, specify a partitioned table ending with the day in the format "$YYYYMMDD".
D. If you have existing data in a separate file for each day, then create a partitioned table and upload each file into the appropriate partition.

Answer: A

Explanation:
You cannot change an existing table into a partitioned table. You must create a partitioned table from scratch. Then you can either stream data into it every day and the data will automatically be put in the right partition, or you can load data into a specific partition by using "$YYYYMMDD" at the end of the table name. Reference: https://cloud.google.com/bigquery/docs/partitioned-tables

NEW QUESTION # 28
You have a data stored in BigQuery. The data in the BigQuery dataset must be highly available. You need to define a storage, backup, and recovery strategy of this data that minimizes cost. How should you configure the BigQuery table?

A. In the event of an emergency, use the backup copy of the table.
B. Create a scheduled query to make copies of the data to tables suffixed with the time of the backup
C. Set the BigQuery dataset to be multi-regional
D. Set the BigQuery dataset to be regiona
E. In the event of an emergency, use a point-in-time snapshot to recover the data.
F. Set the BigQuery dataset to be regiona
G. Set the BigQuery dataset to be multi-regional
H. In the event of an emergency, use the backup copy of the table.
I. Create a scheduled query to make copies of the data to tables suffixed with the time of the backu
J. In the event of an emergency, use a point-in-time snapshot to recover the data.

Answer: E

NEW QUESTION # 29
Suppose you have a dataset of images that are each labeled as to whether or not they contain a human face. To create a neural network that recognizes human faces in images using this labeled dataset, what approach would likely be the most effective?

A. Use deep learning by creating a neural network with multiple hidden layers to automatically detect features of faces.
B. Use K-means Clustering to detect faces in the pixels.
C. Use feature engineering to add features for eyes, noses, and mouths to the input data.
D. Build a neural network with an input layer of pixels, a hidden layer, and an output layer with two categories.

Answer: A

Explanation:
Traditional machine learning relies on shallow nets, composed of one input and one output layer, and at most one hidden layer in between. More than three layers (including input and output) qualifies as "deep" learning. So deep is a strictly defined, technical term that means more than one hidden layer.
In deep-learning networks, each layer of nodes trains on a distinct set of features based on the previous layer's output. The further you advance into the neural net, the more complex the features your nodes can recognize, since they aggregate and recombine features from the previous layer.
A neural network with only one hidden layer would be unable to automatically recognize high-level features of faces, such as eyes, because it wouldn't be able to "build" these features using previous hidden layers that detect low-level features, such as lines.
Feature engineering is difficult to perform on raw image data.
K- means Clustering is an unsupervised learning method used to categorize unlabeled data. Reference: https://deeplearning4j.org/neuralnet-overview

NEW QUESTION # 30
Which of the following statements is NOT true regarding Bigtable access roles?

A. To give a user access to only one table in a project, grant the user the Bigtable Editor role for that table.
B. Using IAM roles, you cannot give a user access to only one table in a project, rather than all tables in a project.
C. To give a user access to only one table in a project, you must configure access through your application.
D. You can configure access control only at the project level.

Answer: A

Explanation:
For Cloud Bigtable, you can configure access control at the project level. For example, you can grant the ability to:
Read from, but not write to, any table within the project.
Read from and write to any table within the project, but not manage instances. Read from and write to any table within the project, and manage instances. Reference: https://cloud.google.com/bigtable/docs/access-control

NEW QUESTION # 31
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