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Google GCP-DE Exam Syllabus Topics:

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

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Google Data Engineer Sample Questions (Q53-Q58):

NEW QUESTION # 53
You are a retailer that wants to integrate your online sales capabilities with different in-home assistants, such as Google Home. You need to interpret customer voice commands and issue an order to the backend systems. Which solutions should you choose?

A. Cloud AutoML Natural Language
B. Cloud Natural Language API
C. Dialogflow Enterprise Edition
D. Cloud Speech-to-Text API

Answer: A

NEW QUESTION # 54
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 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
B. Create a Stackdriver Monitoring dashboard based on the BigQuery metric slots/allocated_for_project
C. Create a Stackdriver Monitoring dashboard based on the BigQuery metric query/scanned_bytes
D. 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

Answer: A

NEW QUESTION # 55
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. Create a cron schedule in Cloud Dataprep.
C. Export the Cloud Dataprep job as a Cloud Dataflow template, and incorporate it into a Cloud Composer job.
D. Export the recipe as a Cloud Dataprep template, and create a job in Cloud Scheduler.

Answer: D

NEW QUESTION # 56
You have historical data covering the last three years in BigQuery and a data pipeline that delivers new data to BigQuery daily. You have noticed that when the Data Science team runs a query filtered on a date column and limited to 30-90 days of data, the query scans the entire table. You also noticed that your bill is increasing more quickly than you expected. You want to resolve the issue as cost-effectively as possible while maintaining the ability to conduct SQL queries. What should you do?

A. Modify your pipeline to maintain the last 30-90 days of data in one table and the longer history in a different table to minimize full table scans over the entire history.
B. Re-create the tables using DD
C. Recommend that the Data Science team export the table to a CSV file on Cloud Storage and use Cloud Datalab to explore the data by reading the files directly.
D. Partition the tables by a column containing a TIMESTAMP or DATE Type.
E. Recommend that the Data Science team use wildcards on the table name suffixes to select the data they need.
F. Write an Apache Beam pipeline that creates a BigQuery table per data

Answer: C

NEW QUESTION # 57
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 non-sequential numeric ID
B. A sequential numeric ID
C. A stock symbol followed by a timestamp
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 # 58
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