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Google GCP-DE Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Building and operationalizing data processing systems	30%	- Data pipeline implementation 1. Dataproc and Spark-based processing 2. Dataflow pipeline development - Data ingestion and transformation 1. Pub/Sub streaming ingestion 2. ETL/ELT workflows
Topic 2: Designing data processing systems	20%	- Data pipeline architecture design 1. Scalable data ingestion design 2. Batch vs streaming data processing selection - Storage and data modeling 1. Data warehouse design using BigQuery 2. Data lake architecture on Google Cloud Storage
Topic 3: Maintaining and optimizing data and ML solutions	20%	- Machine learning integration 1. Vertex AI integration for pipelines 2. BigQuery ML usage - Security and governance 1. IAM and access control 2. Data encryption and compliance
Topic 4: Operationalizing data and ML pipelines	30%	- Pipeline automation and orchestration 1. Scheduling and monitoring pipelines 2. Cloud Composer workflows - Monitoring and troubleshooting 1. Performance optimization and debugging 2. Logging and observability

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Google Data Engineer Sample Questions (Q16-Q21):

NEW QUESTION # 16
Which of the following statements is NOT true regarding Bigtable access roles?

A. To give a user access to only one table in a project, you must configure access through your application.
B. Using IAM roles, you cannot give a user access to only one table in a project, rather than all tables in a project.
C. To give a user access to only one table in a project, grant the user the Bigtable Editor role for that table.
D. You can configure access control only at the project level.

Answer: C

Explanation:
For Cloud Bigtable, you can configure access control at the project level. For example, you can grant the ability to:
Read from, but not write to, any table within the project.
Read from and write to any table within the project, but not manage instances. Read from and write to any table within the project, and manage instances. Reference: https://cloud.google.com/bigtable/docs/access-control

NEW QUESTION # 17
What are all of the BigQuery operations that Google charges for?

A. Queries and streaming inserts
B. Storage, queries, and exporting data
C. Storage, queries, and streaming inserts
D. Storage, queries, and loading data from a file

Answer: C

Explanation:
Google charges for storage, queries, and streaming inserts. Loading data from a file and exporting data are free operations.
Reference: https://cloud.google.com/bigquery/pricing

NEW QUESTION # 18
You are designing storage for two relational tables that are part of a 10-TB database on Google Cloud. You want to support transactions that scale horizontally. You also want to optimize data for range queries on nonkey columns. What should you do?

A. Add secondary indexes to support query patterns.
B. Use Cloud Spanner for storag
C. Use Cloud SQL for storag
D. Use Cloud SQL for storag
E. Use Cloud Dataflow to transform data to support query patterns.
F. Add secondary indexes to support query patterns.
G. Use Cloud Spanner for storag
H. Use Cloud Dataflow to transform data to support query patterns.

Answer: H

Explanation:
Reference: https://cloud.google.com/solutions/data-lifecycle-cloud-platform

NEW QUESTION # 19
All Google Cloud Bigtable client requests go through a front-end server they are sent to a Cloud Bigtable node.

A. once
B. after
C. only if
D. before

Answer: D

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 # 20
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 sequential numeric ID
B. A timestamp followed by a stock symbol
C. A non-sequential numeric ID
D. A stock symbol followed by a timestamp

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 # 21
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