GCP-DE Valid Exam Labs | GCP-DE Study Plan

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Google GCP-DE Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Designing data processing systems	22%-27%	- Designing data pipelines 1. Design orchestration workflows 2. Build ETL and ELT pipelines 3. Optimize pipeline performance and cost - Designing for data ingestion 1. Select appropriate storage systems 2. Design batch and streaming ingestion solutions 3. Design scalable and reliable ingestion architecture
Topic 2: Ensuring solution quality	20%-25%	- Security and compliance 1. Implement encryption and compliance controls 2. Manage IAM and access control 3. Protect sensitive data - Data quality management 1. Implement data governance 2. Maintain metadata and lineage 3. Validate and clean data
Topic 3: Building and operationalizing data processing systems	28%-33%	- Building data processing systems 1. Implement streaming data systems 2. Use BigQuery, Dataflow, Pub/Sub, Dataproc and related services 3. Implement batch processing systems - Operationalizing systems 1. Implement fault tolerance and recovery 2. Manage logging and alerting 3. Monitor data pipelines and workloads
Topic 4: Managing and optimizing solutions	20%-25%	- Managing resources and costs 1. Optimize storage and compute usage 2. Tune query and pipeline performance 3. Control operational costs - Reliability and scalability 1. Scale data workloads efficiently 2. Design highly available systems 3. Implement disaster recovery strategies

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Quiz Efficient Google - GCP-DE - Data Engineer Valid Exam Labs

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Google Data Engineer Sample Questions (Q70-Q75):

NEW QUESTION # 70
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 Partition transform in Cloud Dataflow to separate valid data from corrupt data.
B. Add a SideInput that returns a Boolean if the element is corrupt.
C. Add a ParDo transform in Cloud Dataflow to discard corrupt elements.
D. Add a GroupByKey transform in Cloud Dataflow to group all of the valid data together and discard the rest.

Answer: C

NEW QUESTION # 71
You are building a new data pipeline to share data between two different types of applications: jobs generators and job runners. Your solution must scale to accommodate increases in usage and must accommodate the addition of new applications without negatively affecting the performance of existing ones. What should you do?

A. Create a table on Cloud SQL, and insert and delete rows with the job information
B. Create an API using App Engine to receive and send messages to the applications
C. Use a Cloud Pub/Sub topic to publish jobs, and use subscriptions to execute them
D. Create a table on Cloud Spanner, and insert and delete rows with the job information

Answer: C

NEW QUESTION # 72
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 Stackdriver Monitoring dashboard based on the BigQuery metric slots/allocated_for_project
B. 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
C. 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
D. Create a Stackdriver Monitoring dashboard based on the BigQuery metric query/scanned_bytes

Answer: B

NEW QUESTION # 73
What Dataflow concept determines when a Window's contents should be output based on certain criteria being met?

A. Windows
B. OutputCriteria
C. Triggers
D. Sessions

Answer: C

Explanation:
Triggers control when the elements for a specific key and window are output. As elements arrive, they are put into one or more windows by a Window transform and its associated WindowFn, and then passed to the associated Trigger to determine if the Windows contents should be output.
Reference:
https://cloud.google.com/dataflow/java-sdk/JavaDoc/com/google/cloud/dataflow/sdk/transforms/windowing/Tri

NEW QUESTION # 74
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 timestamp followed by a stock symbol
B. A sequential numeric ID
C. A stock symbol followed by a timestamp
D. A non-sequential numeric ID

Answer: A,B

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 # 75
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