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Google GCP-DE Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: 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. Supporting model training and serving 2. Feature engineering and data preparation
Topic 2: Maintaining and automating data workloads	18%	- Operationalizing workloads 1. Monitoring and troubleshooting pipelines 2. Ensuring reliability and recoverability 3. Orchestrating and scheduling jobs - Automation and optimization 1. Optimizing resource usage and costs 2. Automating deployment and management
Topic 3: Storing and managing data	20%	- Implementing storage solutions 1. Managing data lifecycle and retention 2. Using data storage services appropriately 3. Designing data warehouses and data lakes - Optimizing storage performance and cost 1. Configuring storage for access patterns 2. Implementing cost optimization strategies
Topic 4: Ingesting and processing data	25%	- Transforming data 1. Managing schema evolution 2. Optimizing transformations for performance 3. Applying data processing logic - Building data pipelines 1. Handling data quality and consistency 2. Developing batch and streaming workflows 3. Implementing ingestion mechanisms
Topic 5: 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. Planning for data migration and integration 3. Designing for data transformation and enrichment

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Google Data Engineer Sample Questions (Q40-Q45):

NEW QUESTION # 40
You need to create a data pipeline that copies time-series transaction data so that it can be queried from within BigQuery by your data science team for analysis. Every hour, thousands of transactions are updated with a new status. The size of the intitial dataset is 1.5 PB, and it will grow by 3 TB per day. The data is heavily structured, and your data science team will build machine learning models based on this dat a. You want to maximize performance and usability for your data science team. Which two strategies should you adopt? Choose 2 answers.

A. Develop a data pipeline where status updates are appended to BigQuery instead of updated.
B. Use BigQuery'ssupport for external data sources to query.
C. Copy a daily snapshot of transaction data to Cloud Storage and store it as an Avro fil
D. Denormalize the data as must as possible.
E. Use BigQuery UPDATE to further reduce the size of the dataset.
F. Preserve the structure of the data as much as possible.

Answer: A,C

NEW QUESTION # 41
Flowlogistic's management has determined that the current Apache Kafka servers cannot handle the data volume for their real-time inventory tracking system. You need to build a new system on Google Cloud Platform (GCP) that will feed the proprietary tracking software. The system must be able to ingest data from a variety of global sources, process and query in real-time, and store the data reliably. Which combination of GCP products should you choose?

A. Cloud Pub/Sub, Cloud Dataflow, and Cloud Storage
B. Cloud Pub/Sub, Cloud Dataflow, and Local SSD
C. Cloud Load Balancing, Cloud Dataflow, and Cloud Storage
D. Cloud Pub/Sub, Cloud SQL, and Cloud Storage

Answer: D

NEW QUESTION # 42
Each analytics team in your organization is running BigQuery jobs in their own projects. You want to enable each team to monitor slot usage within their projects. What should you do?

A. Create a log export for each project, capture the BigQuery job execution logs, create a custom metric based on the totalSlotMs, and create a Stackdriver Monitoring dashboard based on the custom metric
B. Create a Stackdriver Monitoring dashboard based on the BigQuery metric query/scanned_bytes
C. Create an aggregated log export at the organization level, capture the BigQuery job execution logs, create a custom metric based on the totalSlotMs, and create a Stackdriver Monitoring dashboard based on the custom metric
D. Create a Stackdriver Monitoring dashboard based on the BigQuery metric slots/allocated_for_project

Answer: C

NEW QUESTION # 43
By default, which of the following windowing behavior does Dataflow apply to unbounded data sets?

A. Windows at every 10 minutes
B. Windows at every 100 MB of data
C. Single, Global Window
D. Windows at every 1 minute

Answer: C

Explanation:
Dataflow's default windowing behavior is to assign all elements of a PCollection to a single, global window, even for unbounded PCollections Reference: https://cloud.google.com/dataflow/model/pcollection

NEW QUESTION # 44
What is the general recommendation when designing your row keys for a Cloud Bigtable schema?

A. Include multiple time series values within the row key
B. Keep your row key as long as the field permits
C. Keep the row keep as an 8 bit integer
D. Keep your row key reasonably short

Answer: D

Explanation:
A general guide is to, keep your row keys reasonably short. Long row keys take up additional memory and storage and increase the time it takes to get responses from the Cloud Bigtable server.
Reference: https://cloud.google.com/bigtable/docs/schema-design#row-keys

NEW QUESTION # 45
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