Free GCP-DE Sample, GCP-DE Valid Braindumps

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Google GCP-DE Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Ensuring solution quality20%-25%- Security and compliance
  • 1. Manage IAM and access control
  • 2. Protect sensitive data
  • 3. Implement encryption and compliance controls
- Data quality management
  • 1. Validate and clean data
  • 2. Maintain metadata and lineage
  • 3. Implement data governance
Topic 2: Designing data processing systems22%-27%- Designing data pipelines
  • 1. Design orchestration workflows
  • 2. Optimize pipeline performance and cost
  • 3. Build ETL and ELT pipelines
- Designing for data ingestion
  • 1. Design batch and streaming ingestion solutions
  • 2. Select appropriate storage systems
  • 3. Design scalable and reliable ingestion architecture
Topic 3: Building and operationalizing data processing systems28%-33%- Building data processing systems
  • 1. Use BigQuery, Dataflow, Pub/Sub, Dataproc and related services
  • 2. Implement batch processing systems
  • 3. Implement streaming data systems
- Operationalizing systems
  • 1. Manage logging and alerting
  • 2. Monitor data pipelines and workloads
  • 3. Implement fault tolerance and recovery
Topic 4: Managing and optimizing solutions20%-25%- Reliability and scalability
  • 1. Implement disaster recovery strategies
  • 2. Design highly available systems
  • 3. Scale data workloads efficiently
- Managing resources and costs
  • 1. Tune query and pipeline performance
  • 2. Control operational costs
  • 3. Optimize storage and compute usage

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Google Data Engineer Sample Questions (Q73-Q78):

NEW QUESTION # 73
You have enabled the free integration between Firebase Analytics and Google BigQuery. Firebase now automatically creates a new table daily in BigQuery in the format app_events_YYYYMMDD. You want to query all of the tables for the past 30 days in legacy SQL. What should you do?

Answer: C

Explanation:
Reference:
https://cloud.google.com/blog/products/gcp/using-bigquery-and-firebase-analytics-to-understandyour-mobile-ap


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)?

Answer: C,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 # 75
All Google Cloud Bigtable client requests go through a front-end server they are sent to a Cloud Bigtable node.

Answer: B

Explanation:
In a Cloud Bigtable architecture all client requests go through a front-end server before they are sent to a Cloud Bigtable node.
The nodes are organized into a Cloud Bigtable cluster, which belongs to a Cloud Bigtable instance, which is a container for the cluster. Each node in the cluster handles a subset of the requests to the cluster.
When additional nodes are added to a cluster, you can increase the number of simultaneous requests that the cluster can handle, as well as the maximum throughput for the entire cluster.
Reference: https://cloud.google.com/bigtable/docs/overview


NEW QUESTION # 76
You launched a new gaming app almost three years ago. You have been uploading log files from the previous day to a separate Google BigQuery table with the table name format LOGS_yyyymmdd. You have been using table wildcard functions to generate daily and monthly reports for all time ranges. Recently, you discovered that some queries that cover long date ranges are exceeding the limit of 1,000 tables and failing. How can you resolve this issue?

Answer: A


NEW QUESTION # 77
MJTelco is building a custom interface to share dat
a. They have these requirements:
They need to do aggregations over their petabyte-scale datasets.
They need to scan specific time range rows with a very fast response time (milliseconds). Which combination of Google Cloud Platform products should you recommend?

Answer: B


NEW QUESTION # 78
......

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