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Google Associate-Data-Practitioner Exam Syllabus Topics:

Topic
Details

Topic 1

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

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 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.

Google Cloud Associate Data Practitioner Sample Questions (Q105-Q110):

NEW QUESTION # 105
You need to create a new data pipeline. You want a serverless solution that meets the following requirements:
* Data is streamed from Pub/Sub and is processed in real-time.
* Data is transformed before being stored.
* Data is stored in a location that will allow it to be analyzed with SQL using Looker.

Which Google Cloud services should you recommend for the pipeline?

A. 1. BigQuery
2. Analytics Hub
B. 1. Dataproc Serverless
2. Bigtable
C. 1. Cloud Composer
2. Cloud SQL for MySQL
D. 1. Dataflow
2. BigQuery

Answer: D

Explanation:
To build a serverless data pipeline that processes data in real-time from Pub/Sub, transforms it, and stores it for SQL-based analysis using Looker, the best solution is to use Dataflow and BigQuery. Dataflow is a fully managed service for real-time data processing and transformation, while BigQuery is a serverless data warehouse that supports SQL-based querying and integrates seamlessly with Looker for data analysis and visualization. This combination meets the requirements for real-time streaming, transformation, and efficient storage for analytical queries.

NEW QUESTION # 106
Your company's ecommerce website collects product reviews from customers. The reviews are loaded as CSV files daily to a Cloud Storage bucket. The reviews are in multiple languages and need to be translated to Spanish. You need to configure a pipeline that is serverless, efficient, and requires minimal maintenance.
What should you do?

A. Use a Dataflow templates pipeline to translate the reviews using the Cloud Translation API. Set BigQuery as the sink.
B. Load the data into BigQuery using a Cloud Run function. Create a BigQuery remote function that invokes the Cloud Translation API. Use a scheduled query to translate new reviews.
C. Load the data into BigQuery using a Cloud Run function. Use the BigQuery ML create model statement to train a translation model. Use the model to translate the product reviews within BigQuery.
D. Load the data into BigQuery using Dataproc. Use Apache Spark to translate the reviews by invoking the Cloud Translation API. Set BigQuery as the sink.U

Answer: B

Explanation:
Loading the data into BigQuery using aCloud Run functionand creating aBigQuery remote functionthat invokes theCloud Translation APIis a serverless and efficient approach. With this setup, you can use ascheduled queryin BigQuery to invoke the remote function and translate new product reviews on a regular basis. This solution requires minimal maintenance, as BigQuery handles storage and querying, and the Cloud Translation API provides accurate translations without the need for custom ML model development.

NEW QUESTION # 107
You created a customer support application that sends several forms of data to Google Cloud. Your application is sending:
1. Audio files from phone interactions with support agents that will be accessed during trainings.
2. CSV files of users' personally identifiable information (Pll) that will be analyzed with SQL.
3. A large volume of small document files that will power other applications.
You need to select the appropriate tool for each data type given the required use case, while following Google-recommended practices. Which should you choose?

A. 1. Filestore
2. Bigtable
3. BigQuery
B. 1. Filestore
2. Cloud SQL for PostgreSQL
3. Datastore
C. 1. Cloud Storage
2. CloudSQL for PostgreSQL
3. Bigtable
D. 1. Cloud Storage
2. BigQuery
3. Firestore

Answer: D

Explanation:
Audio files from phone interactions: Use Cloud Storage. Cloud Storage is ideal for storing large binary objects like audio files, offering scalability and easy accessibility for training purposes.
CSV files of users' personally identifiable information (PII): Use BigQuery. BigQuery is a serverless data warehouse optimized for analyzing structured data, such as CSV files, using SQL. It ensures compliance with PII handling through access controls and data encryption.
A large volume of small document files: Use Firestore. Firestore is a scalable NoSQL database designed for applications requiring fast, real-time interactions and structured document storage, making it suitable for powering other applications.

NEW QUESTION # 108
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. ETL
B. ELT
C. EL
D. ETLT

Answer: A

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 # 109
You manage an ecommerce website that has a diverse range of products. You need to forecast future product demand accurately to ensure that your company has sufficient inventory to meet customer needs and avoid stockouts. Your company's historical sales data is stored in a BigQuery table. You need to create a scalable solution that takes into account the seasonality and historical data to predict product demand. What should you do?

A. Use Colab Enterprise to create a Jupyter notebook. Use the historical sales data to train a custom prediction model in Python.
B. Use the historical sales data to train and create a BigQuery ML time series model. Use the ML.
FORECAST function call to output the predictions into a new BigQuery table.
C. Use the historical sales data to train and create a BigQuery ML linear regression model. Use the ML.
PREDICT function call to output the predictions into a new BigQuery table.
D. Use the historical sales data to train and create a BigQuery ML logistic regression model. Use the ML.PREDICT function call to output the predictions into a new BigQuery table.

Answer: B

Explanation:
Comprehensive and Detailed In-Depth Explanation:
Forecasting product demand with seasonality requires a time series model, and BigQuery ML offers a scalable, serverless solution. Let's analyze:
* Option A: BigQuery ML's time series models (e.g., ARIMA_PLUS) are designed for forecasting with seasonality and trends. The ML.FORECAST function generates predictions based on historical data, storing them in a table. This is scalable (no infrastructure) and integrates natively with BigQuery, ideal for ecommerce demand prediction.
* Option B: Colab Enterprise with a custom Python model (e.g., Prophet) is flexible but requires coding, maintenance, and potentially exporting data, reducing scalability compared to BigQuery ML's in-place processing.
* Option C: Linear regression predicts continuous values but doesn't handle seasonality or time series patterns effectively, making it unsuitable for demand forecasting.

NEW QUESTION # 110
......

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