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Google GCP-DE Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: 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
Topic 2: Maintaining and automating data workloads	18%	- Automation and optimization 1. Automating deployment and management 2. Optimizing resource usage and costs - Operationalizing workloads 1. Monitoring and troubleshooting pipelines 2. Ensuring reliability and recoverability 3. Orchestrating and scheduling jobs
Topic 3: 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
Topic 4: Designing data processing systems	24%	- Planning data solutions 1. Selecting appropriate storage solutions 2. Planning for data security and compliance 3. Designing for reliability, scalability, and efficiency - Designing data pipelines 1. Planning for data migration and integration 2. Defining architecture for batch and streaming processing 3. Designing for data transformation and enrichment
Topic 5: Preparing data for analysis and machine learning	13%	- Enabling data analysis 1. Preparing datasets for querying and reporting 2. Implementing data sharing and governance 3. Optimizing query performance - Preparing data for ML 1. Feature engineering and data preparation 2. Supporting model training and serving

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Google Data Engineer Sample Questions (Q56-Q61):

NEW QUESTION # 56
Your company maintains a hybrid deployment with GCP, where analytics are performed on your anonymized customer dat a. The data are imported to Cloud Storage from your data center through parallel uploads to a data transfer server running on GCP. Management informs you that the daily transfers take too long and have asked you to fix the problem. You want to maximize transfer speeds. Which action should you take?

A. Increase your network bandwidth from Compute Engine to Cloud Storage.
B. Increase the CPU size on your server.
C. Increase your network bandwidth from your datacenter to GCP.
D. Increase the size of the Google Persistent Disk on your server.

Answer: C

NEW QUESTION # 57
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. Build a neural network with an input layer of pixels, a hidden layer, and an output layer with two categories.
B. Use deep learning by creating a neural network with multiple hidden layers to automatically detect features of faces.
C. Use feature engineering to add features for eyes, noses, and mouths to the input data.
D. Use K-means Clustering to detect faces in the pixels.

Answer: B

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 # 58
You work for a large fast food restaurant chain with over 400,000 employees. You store employee information in Google BigQuery in a Users table consisting of a FirstName field and a LastName field. A member of IT is building an application and asks you to modify the schema and data in BigQuery so the application can query a FullName field consisting of the value of the FirstName field concatenated with a space, followed by the value of the LastName field for each employee. How can you make that data available while minimizing cost?

A. Use BigQuery to export the data for the table to a CSV fil
B. Create a Google Cloud Dataproc job to process the CSV file and output a new CSV file containing the proper values for FirstName, LastName and FullNam G. Run a BigQuery load job to load the new CSV file into BigQuery.
C. Run an UPDATE statement that updates the FullName column for each user with the concatenation of the FirstName and LastName values.
D. Create a view in BigQuery that concatenates the FirstName and LastName field values to produce the FullName.
E. Create a Google Cloud Dataflow job that queries BigQuery for the entire Users table, concatenates the FirstName value and LastName value for each user, and loads the proper values for FirstName, LastName, and FullName into a new table in BigQuery.
F. Add a new column called FullName to the Users tabl

Answer: C

NEW QUESTION # 59
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. 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.
B. Create an application that writes to a Cloud SQL database to store the dat
C. 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.
D. Set up periodic exports of the database to write to Cloud Storage and load into BigQuery.
E. Write a tool to poll the API and write data to Cloud Storage as gzipped JSON files.
F. Create an application that provides an AP

Answer: F

NEW QUESTION # 60
You are creating a new pipeline in Google Cloud to stream IoT data from Cloud Pub/Sub through Cloud Dataflow to BigQuery. While previewing the data, you notice that roughly 2% of the data appears to be corrupt. You need to modify the Cloud Dataflow pipeline to filter out this corrupt data. What should you do?

A. Add a SideInput that returns a Boolean if the element is corrupt.
B. Add a Partition transform in Cloud Dataflow to separate valid data from corrupt data.
C. Add a GroupByKey transform in Cloud Dataflow to group all of the valid data together and discard the rest.
D. Add a ParDo transform in Cloud Dataflow to discard corrupt elements.

Answer: D

NEW QUESTION # 61
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