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Google GCP-DE Exam Syllabus Topics:

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

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Google Data Engineer Sample Questions (Q29-Q34):

NEW QUESTION # 29
What is the general recommendation when designing your row keys for a Cloud Bigtable schema?

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

Answer: B

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 # 30
You used Cloud Dataprep to create a recipe on a sample of data in a BigQuery table. You want to reuse this recipe on a daily upload of data with the same schema, after the load job with variable execution time completes. What should you do?

A. Create an App Engine cron job to schedule the execution of the Cloud Dataprep job.
B. Export the recipe as a Cloud Dataprep template, and create a job in Cloud Scheduler.
C. Create a cron schedule in Cloud Dataprep.
D. Export the Cloud Dataprep job as a Cloud Dataflow template, and incorporate it into a Cloud Composer job.

Answer: B

NEW QUESTION # 31
For the best possible performance, what is the recommended zone for your Compute Engine instance and Cloud Bigtable instance?

A. Have the Cloud Bigtable instance to be in the same zone as all of the consumers of your data.
B. Have both the Compute Engine instance and the Cloud Bigtable instance to be in the same zone.
C. Have both the Compute Engine instance and the Cloud Bigtable instance to be in different zones.
D. Have the Compute Engine instance in the furthest zone from the Cloud Bigtable instance.

Answer: B

Explanation:
It is recommended to create your Compute Engine instance in the same zone as your Cloud Bigtable instance for the best possible performance, If it's not possible to create a instance in the same zone, you should create your instance in another zone within the same region. For example, if your Cloud Bigtable instance is located in us-central1-b, you could create your instance in us-central1-f. This change may result in several milliseconds of additional latency for each Cloud Bigtable request.
It is recommended to avoid creating your Compute Engine instance in a different region from your Cloud Bigtable instance, which can add hundreds of milliseconds of latency to each Cloud Bigtable request.
Reference: https://cloud.google.com/bigtable/docs/creating-compute-instance

NEW QUESTION # 32
You need to deploy additional dependencies to all of a Cloud Dataproc cluster at startup using an existing initialization action. Company security policies require that Cloud Dataproc nodes do not have access to the Internet so public initialization actions cannot fetch resources. What should you do?

A. Use Resource Manager to add the service account used by the Cloud Dataproc cluster to the Network User role
B. Deploy the Cloud SQL Proxy on the Cloud Dataproc master
C. Copy all dependencies to a Cloud Storage bucket within your VPC security perimeter
D. Use an SSH tunnel to give the Cloud Dataproc cluster access to the Internet

Answer: A

NEW QUESTION # 33
Which row keys are likely to cause a disproportionate number of reads and/or writes on a particular node in a Bigtable cluster (select 2 answers)?

A. A stock symbol followed by a timestamp
B. A sequential numeric ID
C. A non-sequential numeric ID
D. A timestamp followed by a stock symbol

Answer: B,D

Explanation:
using a timestamp as the first element of a row key can cause a variety of problems.
In brief, when a row key for a time series includes a timestamp, all of your writes will target a single node; fill that node; and then move onto the next node in the cluster, resulting in hotspotting.
Suppose your system assigns a numeric ID to each of your application's users. You might be tempted to use the user's numeric ID as the row key for your table. However, since new users are more likely to be active users, this approach is likely to push most of your traffic to a small number of nodes. [https://cloud.google.com/bigtable/docs/schema-design] Reference:
https://cloud.google.com/bigtable/docs/schema-design-time-series#ensure_that_your_row_key_avoids_hotspotti

NEW QUESTION # 34
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