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Google Associate-Data-Practitioner Exam Syllabus Topics:

Section	Objectives
Topic 1: Data Management and Governance	- Data governance and quality 1. Data quality monitoring and validation 2. Regulatory compliance and data privacy 3. Data cataloging and metadata management (e.g., Data Catalog) 4. Data lineage and provenance - Data storage and management 1. Data lifecycle management 2. Choosing appropriate data storage solutions (e.g., BigQuery, Cloud Storage, Firestore, Bigtable) 3. Data warehouse and data lake design
Topic 2: Machine Learning	- Machine learning fundamentals 1. Understanding ML concepts and workflows 2. Feature engineering and selection 3. Choosing appropriate ML models - Implementing ML solutions 1. AutoML and custom training 2. Training and evaluating ML models 3. Using Vertex AI for ML workflows 4. ML model deployment and serving
Topic 3: Data Preparation and Data Processing	- Ingesting data 1. Data pipeline orchestration (e.g., Cloud Composer, Workflows) 2. Streaming data ingestion (e.g., Pub/Sub, Dataflow) 3. Batch data ingestion (e.g., Cloud Storage, Transfer Service, Datastream) - Transforming data 1. Data cleansing and preprocessing 2. Using Apache Beam/Dataflow for data transformation 3. Data wrangling and enrichment 4. Using SQL for data transformation (e.g., BigQuery)
Topic 4: Data Analysis and Data Visualization	- Visualizing data 1. Sharing and collaboration of data insights 2. Data visualization best practices 3. Creating dashboards and reports (e.g., Looker, Looker Studio) - Analyzing data 1. Using Looker for data analysis 2. Using BigQuery for data analysis 3. Statistical analysis and data modeling 4. Performing data exploration and analysis

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Google Cloud Associate Data Practitioner Sample Questions (Q83-Q88):

NEW QUESTION # 83
You need to design a data pipeline that ingests data from CSV, Avro, and Parquet files into Cloud Storage. The data includes raw user input. You need to remove all malicious SQL injections before storing the data in BigQuery. Which data manipulation methodology should you choose?

A. ELT
B. ETL
C. ETLT
D. EL

Answer: B

Explanation:
The ETL (Extract, Transform, Load) methodology is the best approach for this scenario because it allows you to extract data from the files, transform it by applying the necessary data cleansing (including removing malicious SQL injections), and then load the sanitized data into BigQuery. By transforming the data before loading it into BigQuery, you ensure that only clean and safe data is stored, which is critical for security and data quality.

NEW QUESTION # 84
You have created a LookML model and dashboard that shows daily sales metrics for five regional managers to use. You want to ensure that the regional managers can only see sales metrics specific to their region. You need an easy-to-implement solution. What should you do?

A. Create asales_regionuser attribute, and assign each manager's region as the value of their user attribute.
Add anaccess_filterExplore filter on theregion_namedimension by using thesales_regionuser attribute.
B. Create separate Looker instances for each regional manager. Copy the LookML model and dashboard to each instance. Provision viewer access to the corresponding manager.
C. Create five different Explores with thesql_always_filterExplore filter applied on theregion_namedimension. Set eachregion_namevalue to the corresponding region for each manager.
D. Create separate Looker dashboards for each regional manager. Set the default dashboard filter to the corresponding region for each manager.

Answer: A

Explanation:
Using asales_region user attributeis the best solution because it allows you to dynamically filter data based on each manager's assigned region. By adding anaccess_filterExplore filter on theregion_namedimension that references thesales_regionuser attribute, each manager sees only the sales metrics specific to their region. This approach is easy to implement, scalable, and avoids duplicating dashboards or Explores, making it both efficient and maintainable.

NEW QUESTION # 85
Your organization is conducting analysis on regional sales metrics. Data from each regional sales team is stored as separate tables in BigQuery and updated monthly. You need to create a solution that identifies the top three regions with the highest monthly sales for the next three months. You want the solution to automatically provide up-to-date results. What should you do?

A. Create a BigQuery materialized view that performs a cross join across all of the regional sales tables.Use the row_number() window function to query the new materialized view.
B. Create a BigQuery materialized view that performs a union across all of the regional sales tables. Use the rank() window function to query the new materialized view.
C. Create a BigQuery table that performs a union across all of the regional sales tables. Use the row_number() window function to query the new table.
D. Create a BigQuery table that performs a cross join across all of the regional sales tables. Use the rank() window function to query the new table.

Answer: B

Explanation:
Comprehensive and Detailed in Depth Explanation:
Why C is correct:Materialized views in BigQuery are precomputed views that periodically cache the results of a query. This ensures up-to-date results automatically.
A UNION is the correct operation to combine the data from multiple regional sales tables.
RANK() function is correct to rank the sales regions. ROW_NUMBER() would create a unique number for each row, even if sales amount is the same, this is not the desired function.
Why other options are incorrect:A and B: Standard tables do not provide automatic updates.
D: A CROSS JOIN would produce a Cartesian product, which is not appropriate for combining regional sales data.
Cross join is used when you want every combination of rows from tables, not a aggregation of data.

NEW QUESTION # 86
You need to create a weekly aggregated sales report based on a large volume of dat a. You want to use Python to design an efficient process for generating this report. What should you do?

A. Create a Cloud Data Fusion and Wrangler flow. Schedule the flow to run once a week.
B. Create a Colab Enterprise notebook and use the bigframes.pandas library. Schedule the notebook to execute once a week.
C. Create a Dataflow directed acyclic graph (DAG) coded in Python. Use Cloud Scheduler to schedule the code to run once a week.
D. Create a Cloud Run function that uses NumPy. Use Cloud Scheduler to schedule the function to run once a week.

Answer: C

Explanation:
Using Dataflow with a Python-coded Directed Acyclic Graph (DAG) is the most efficient solution for generating a weekly aggregated sales report based on a large volume of data. Dataflow is optimized for large-scale data processing and can handle aggregation efficiently. Python allows you to customize the pipeline logic, and Cloud Scheduler enables you to automate the process to run weekly. This approach ensures scalability, efficiency, and the ability to process large datasets in a cost-effective manner.

NEW QUESTION # 87
Your organization has decided to migrate their existing enterprise data warehouse to BigQuery. The existing data pipeline tools already support connectors to BigQuery. You need to identify a data migration approach that optimizes migration speed. What should you do?

A. Create a temporary file system to facilitate data transfer from the existing environment to Cloud Storage. Use Storage Transfer Service to migrate the data into BigQuery.
B. Use the BigQuery Data Transfer Service to recreate the data pipeline and migrate the data into BigQuery.
C. Use the Cloud Data Fusion web interface to build data pipelines. Create a directed acyclic graph (DAG) that facilitates pipeline orchestration.
D. Use the existing data pipeline tool's BigQuery connector to reconfigure the data mapping.

Answer: D

Explanation:
Since your existing data pipeline tools already support connectors to BigQuery, the most efficient approach is touse the existing data pipeline tool's BigQuery connectorto reconfigure the data mapping. This leverages your current tools, reducing migration complexity and setup time, while optimizing migration speed. By reconfiguring the data mapping within the existing pipeline, you can seamlessly direct the data into BigQuery without needing additional services or intermediary steps.

NEW QUESTION # 88
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