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Google Associate-Data-Practitioner Ausbildungsressourcen - Associate-Data-Practitioner Prüfung

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Google Associate-Data-Practitioner Prüfungsplan:

Thema
Einzelheiten

Thema 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.

Thema 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.

Thema 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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Associate-Data-Practitioner Prüfung, Associate-Data-Practitioner Fragen Beantworten

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Google Cloud Associate Data Practitioner Associate-Data-Practitioner Prüfungsfragen mit Lösungen (Q70-Q75):

70. Frage
You are working with a small dataset in Cloud Storage that needs to be transformed and loaded into BigQuery for analysis. The transformation involves simple filtering and aggregation operations. You want to use the most efficient and cost-effective data manipulation approach. What should you do?

A. Create a Cloud Data Fusion instance and visually design an ETL pipeline that reads data from Cloud Storage, transforms it using built-in transformations, and loads the results into BigQuery.
B. Use Dataflow to perform the ETL process that reads the data from Cloud Storage, transforms it using Apache Beam, and writes the results to BigQuery.
C. Use BigQuery's SQL capabilities to load the data from Cloud Storage, transform it, and store the results in a new BigQuery table.
D. Use Dataproc to create an Apache Hadoop cluster, perform the ETL process using Apache Spark, and load the results into BigQuery.

Antwort: C

Begründung:
Comprehensive and Detailed In-Depth Explanation:
For a small dataset with simple transformations (filtering, aggregation), Google recommends leveraging BigQuery's native SQL capabilities to minimize cost and complexity.
* Option A: Dataproc with Spark is overkill for a small dataset, incurring cluster management costs and setup time.
* Option B: BigQuery can load data directly from Cloud Storage (e.g., CSV, JSON) and perform transformations using SQL in a serverless manner, avoiding additional service costs. This is the most efficient and cost-effective approach.
* Option C: Cloud Data Fusion is suited for complex ETL but adds overhead (instance setup, UI design) unnecessary for simple tasks.

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

Antwort: A

Begründung:
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.

72. Frage
You need to design a data pipeline to process large volumes of raw server log data stored in Cloud Storage.
The data needs to be cleaned, transformed, and aggregated before being loaded into BigQuery for analysis.
The transformation involves complex data manipulation using Spark scripts that your team developed. You need to implement a solution that leverages your team's existing skillset, processes data at scale, and minimizes cost. What should you do?

A. Use Dataform to define the transformations in SQLX.
B. Use Cloud Data Fusion to visually design and manage the pipeline.
C. Use Dataproc to run the transformations on a cluster.
D. Use Dataflow with a custom template for the transformation logic.

Antwort: C

Begründung:
Comprehensive and Detailed In-Depth Explanation:
The pipeline must handle large-scale log processing with existing Spark scripts, prioritizing skillset reuse, scalability, and cost. Let's break it down:
* Option A: Dataflow uses Apache Beam, not Spark, requiring script rewrites (losing skillset leverage).
Custom templates scale well but increase development cost and effort.
* Option B: Cloud Data Fusion is a visual ETL tool, not Spark-based. It doesn't reuse existing scripts, requiring redesign, and is less cost-efficient for complex, code-driven transformations.
* Option C: Dataform uses SQLX for BigQuery ELT, not Spark. It's unsuitable for pre-load transformations of raw logs and doesn't leverage Spark skills.

73. Frage
Your company wants to implement a data transformation (ETL) pipeline for their BigQuery data warehouse.
You need to identify a managed transformation solution that allows users to develop with SQL and JavaScript, has version control, allows for modular code, and has data quality checks. What should you do?

A. Use Dataproc to create an Apache Spark cluster and implement the transformations by using PySpark SQL.
B. Use Dataform to define the transformations in SQLX.
C. Create BigQuery scheduled queries to define the transformations in SQL.
D. Create a Cloud Composer environment, and orchestrate the transformations by using the BigQueryinsertJob operator.

Antwort: B

Begründung:
Comprehensive and Detailed in Depth Explanation:
Why C is correct:Dataform is a managed data transformation service that allows you to define data pipelines using SQL and JavaScript.
It provides version control, modular code development, and data quality checks.
Why other options are incorrect:A: Cloud Composer is an orchestration tool, not a data transformation tool.
B: Scheduled queries are not suitable for complex ETL pipelines.
D: Dataproc requires setting up a Spark cluster and writing code, which is more complex than using Dataform.

74. Frage
You are a Looker analyst. You need to add a new field to your Looker report that generates SQL that will run against your company's database. You do not have the Develop permission. What should you do?

A. Create a calculated field using the Add a field option in Looker Studio, and add it to your report.
B. Create a custom field from the field picker in Looker, and add it to your report.
C. Create a new field in the LookML layer, refresh your report, and select your new field from the field picker.
D. Create a table calculation from the field picker in Looker, and add it to your report.

Antwort: B

Begründung:
Creating a custom field from the field picker in Looker allows you to add new fields to your report without requiring the Develop permission. Custom fields are created directly in the Looker UI, enabling you to define calculations or transformations that generate SQL for the database query. This approach is user-friendly and does not require access to the LookML layer, making it the appropriate choice for your situation.

75. Frage
......

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