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Pass Guaranteed Trustable Google - Associate-Data-Practitioner - Reliable Google Cloud Associate Data Practitioner Braindumps Pdf

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Google Associate-Data-Practitioner Exam Syllabus Topics:

Topic
Details

Topic 1

Data Preparation and Ingestion: This section of the exam measures the skills of Google Cloud Engineers and covers the preparation and processing of data. Candidates will differentiate between various data manipulation methodologies such as ETL, ELT, and ETLT. They will choose appropriate data transfer tools, assess data quality, and conduct data cleaning using tools like Cloud Data Fusion and BigQuery. A key skill measured is effectively assessing data quality before ingestion.

Topic 2

Data Analysis and Presentation: This domain assesses the competencies of Data Analysts in identifying data trends, patterns, and insights using BigQuery and Jupyter notebooks. Candidates will define and execute SQL queries to generate reports and analyze data for business questions.| Data Pipeline Orchestration: This section targets Data Analysts and focuses on designing and implementing simple data pipelines. Candidates will select appropriate data transformation tools based on business needs and evaluate use cases for ELT versus ETL.

Topic 3

Data Management: This domain measures the skills of Google Database Administrators in configuring access control and governance. Candidates will establish principles of least privilege access using Identity and Access Management (IAM) and compare methods of access control for Cloud Storage. They will also configure lifecycle management rules to manage data retention effectively. A critical skill measured is ensuring proper access control to sensitive data within Google Cloud services

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Google Cloud Associate Data Practitioner Sample Questions (Q96-Q101):

NEW QUESTION # 96
Your organization's business analysts require near real-time access to streaming data. However, they are reporting that their dashboard queries are loading slowly. After investigating BigQuery query performance, you discover the slow dashboard queries perform several joins and aggregations.
You need to improve the dashboard loading time and ensure that the dashboard data is as up-to-date as possible. What should you do?

A. Disable BiqQuery query result caching.
B. Modify the schema to use parameterized data types.
C. Create a scheduled query to calculate and store intermediate results.
D. Create materialized views.

Answer: D

Explanation:
Creatingmaterialized viewsis the best solution to improve dashboard loading time while ensuring that the data is as up-to-date as possible. Materialized views precompute and cache the results of complex joins and aggregations, significantly reducing query execution time for dashboards. They also automatically update as the underlying data changes, ensuring near real-time access to fresh data. This approach optimizes query performance and provides an efficient and scalable solution for streaming data dashboards.
For near real-time dashboards with slow queries involving joins and aggregations, Google recommends materialized views in BigQuery to precompute and refresh results automatically.
* Option A: Scheduled queries compute intermediate results periodically, but they aren't near real-time and require manual scheduling, adding latency.
* Option B: Parameterized data types don't address query performance for joins/aggregations; they're for query flexibility, not optimization.
* Option C: Disabling caching worsens performance by forcing full recomputation each time, contrary to the goal.

NEW QUESTION # 97
You are working on a data pipeline that will validate and clean incoming data before loading it into BigQuery for real-time analysis. You want to ensure that the data validation and cleaning is performed efficiently and can handle high volumes of dat a. What should you do?

A. Write custom scripts in Python to validate and clean the data outside of Google Cloud. Load the cleaned data into BigQuery.
B. Load the raw data into BigQuery using Cloud Storage as a staging area, and use SQL queries in BigQuery to validate and clean the data.
C. Use Dataflow to create a streaming pipeline that includes validation and transformation steps.
D. Use Cloud Run functions to trigger data validation and cleaning routines when new data arrives in Cloud Storage.

Answer: C

Explanation:
Using Dataflow to create a streaming pipeline that includes validation and transformation steps is the most efficient and scalable approach for real-time analysis. Dataflow is optimized for high-volume data processing and allows you to apply validation and cleaning logic as the data flows through the pipeline. This ensures that only clean, validated data is loaded into BigQuery, supporting real-time analysis while handling high data volumes effectively.

NEW QUESTION # 98
You need to create a data pipeline that streams event information from applications in multiple Google Cloud regions into BigQuery for near real-time analysis. The data requires transformation before loading. You want to create the pipeline using a visual interface. What should you do?

A. Push event information to a Pub/Sub topic. Create a BigQuery subscription in Pub/Sub.
B. Push event information to a Pub/Sub topic. Create a Dataflow job using the Dataflow job builder.
C. Push event information to a Pub/Sub topic. Create a Cloud Run function to subscribe to the Pub/Sub topic, apply transformations, and insert the data into BigQuery.
D. Push event information to Cloud Storage, and create an external table in BigQuery. Create a BigQuery scheduled job that executes once each day to apply transformations.

Answer: B

Explanation:
Pushing event information to a Pub/Sub topic and then creating a Dataflow job using the Dataflow job builder is the most suitable solution. The Dataflow job builder provides a visual interface to design pipelines, allowing you to define transformations and load data into BigQuery. This approach is ideal for streaming data pipelines that require near real-time transformations and analysis. It ensures scalability across multiple regions and integrates seamlessly with Pub/Sub for event ingestion and BigQuery for analysis.

NEW QUESTION # 99
You have a Cloud SQL for PostgreSQL database that stores sensitive historical financial data. You need to ensure that the data is uncorrupted and recoverable in the event that the primary region is destroyed. The data is valuable, so you need to prioritize recovery point objective (RPO) over recovery time objective (RTO). You want to recommend a solution that minimizes latency for primary read and write operations. What should you do?

A. Configure the Cloud SQL for PostgreSQL instance for regional availability (HA) with synchronous replication to a secondary instance in a different zone.
B. Configure the Cloud SQL for PostgreSQL instance for regional availability (HA) with asynchronous replication to a secondary instance in a different region.
C. Configure the Cloud SQL for PostgreSQL instance for multi-region backup locations.
D. Configure the Cloud SQL for PostgreSQL instance for regional availability (HA). Back up the Cloud SQL for PostgreSQL database hourly to a Cloud Storage bucket in a different region.

Answer: C

Explanation:
Comprehensive and Detailed In-Depth Explanation:
The priorities are data integrity, recoverability after a regional disaster, low RPO (minimal data loss), and low latency for primary operations. Let's analyze:
* Option A: Multi-region backups store point-in-time snapshots in a separate region. With automated backups and transaction logs, RPO can be near-zero (e.g., minutes), and recovery is possible post- disaster. Primary operations remain in one zone, minimizing latency.
* Option B: Regional HA (failover to another zone) with hourly cross-region backups protects against zone failures, but hourly backups yield an RPO of up to 1 hour-too high for valuable data. Manual backup management adds overhead.
* Option C: Synchronous replication to another zone ensures zero RPO within a region but doesn't protect against regional loss. Latency increases slightly due to sync writes across zones.

NEW QUESTION # 100
Your company uses Looker to visualize and analyze sales dat
a. You need to create a dashboard that displays sales metrics, such as sales by region, product category, and time period. Each metric relies on its own set of attributes distributed across several tables. You need to provide users the ability to filter the data by specific sales representatives and view individual transactions. You want to follow the Google-recommended approach. What should you do?

A. Use Looker's custom visualization capabilities to create a single visualization that displays all the sales metrics with filtering and drill-down functionality.
B. Use BigQuery to create multiple materialized views, each focusing on a specific sales metric. Build the dashboard using these views.
C. Create a single Explore with all sales metrics. Build the dashboard using this Explore.
D. Create multiple Explores, each focusing on each sales metric. Link the Explores together in a dashboard using drill-down functionality.

Answer: C

Explanation:
Creating a single Explore with all the sales metrics is the Google-recommended approach. This Explore should be designed to include all relevant attributes and dimensions, enabling users to analyze sales data by region, product category, time period, and other filters like sales representatives. With a well-structured Explore, you can efficiently build a dashboard that supports filtering and drill-down functionality. This approach simplifies maintenance, provides a consistent data model, and ensures users have the flexibility to interact with and analyze the data seamlessly within a unified framework.

NEW QUESTION # 101
......

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