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Snowflake DEA-C01 Exam Syllabus Topics:

Section	Objectives
Performance Optimization	- Warehouse optimization 1. Scaling strategies 2. Concurrency management - Query performance tuning 1. Clustering and micro-partitions 2. Caching mechanisms
Security, Governance, and Data Sharing	- Data governance 1. Data masking policies 2. Secure data sharing concepts - Access control 1. Role-based access control (RBAC) 2. Object-level permissions
Data Pipelines and Transformation	- ETL/ELT design patterns in Snowflake 1. Staging and transformation layers 2. Data orchestration concepts - Streams and Tasks 1. Change Data Capture (CDC) with Streams 2. Automating pipelines with Tasks
Data Loading and Integration	- Bulk data loading 1. COPY INTO command usage 2. File format handling - Snowpipe and continuous ingestion 1. Event-driven loading 2. Automated ingestion workflows
Semi-Structured and Advanced Data Handling	- Advanced transformation techniques 1. Complex transformations in SQL 2. Flattening nested data - Semi-structured data processing 1. JSON, Avro, Parquet handling 2. VARIANT data type usage
Data Engineering Fundamentals on Snowflake	- Snowflake architecture for data engineering 1. Virtual warehouses and compute scaling 2. Storage and compute separation - Data ingestion concepts 1. Continuous ingestion concepts 2. Batch loading strategies

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Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q193-Q198):

NEW QUESTION # 193
A company plans to use Amazon Kinesis Data Firehose to store data in Amazon S3. The source data consists of 2 MB .csv files. The company must convert the .csv files to JSON format. The company must store the files in Apache Parquet format.
Which solution will meet these requirements with the LEAST development effort?

A. Use Kinesis Data Firehose to convert the .csv files to JSON. Use an AWS Lambda function to store the files in Parquet format.
B. Use Kinesis Data Firehose to convert the .csv files to JSON and to store the files in Parquet format.
C. Use Kinesis Data Firehose to invoke an AWS Lambda function that transforms the .csv files to JSON and stores the files in Parquet format.
D. Use Kinesis Data Firehose to invoke an AWS Lambda function that transforms the .csv files to JSON. Use Kinesis Data Firehose to store the files in Parquet format.

Answer: B

Explanation:
By using the built-in transformation and format conversion features of Kinesis Data Firehose, you achieve the desired result with minimal custom development, thereby meeting the requirements efficiently and cost-effectively.

NEW QUESTION # 194
A gaming company uses Amazon Kinesis Data Streams to collect clickstream data. The company uses Amazon Data Firehose delivery streams to store the data in JSON format in Amazon S3.
Data scientists at the company use Amazon Athena to query the most recent data to obtain business insights.
The company wants to reduce Athena costs but does not want to recreate the data pipeline.
Which solution will meet these requirements with the LEAST management effort?

A. Create an Apache Spark job that combines JSON files and converts the JSON files to Apache Parquet files. Launch an Amazon EMR ephemeral cluster every day to run the Spark job to create new Parquet files in a different S3 location. Use the ALTER TABLE SET LOCATION statement to reflect the new S3 location on the existing Athena table.
B. Integrate an AWS Lambda function with Firehose to convert source records to Apache Parquet and write them to Amazon S3. In parallel, run an AWS Glue extract, transform, and load (ETL) job to combine the JSON files and convert the JSON files to large Parquet files. Create a custom S3 object YYYYMMDD prefix. Use the ALTER TABLE ADD PARTITION statement to reflect the partition on the existing Athena table.
C. Create a Kinesis data stream as a delivery destination for Firehose. Use Amazon Managed Service for Apache Flink (previously known as Amazon Kinesis Data Analytics) to run Apache Flink on the Kinesis data stream. Use Flink to aggregate the data and save the data to Amazon S3 in Apache Parquet format with a custom S3 object YYYYMMDD prefix. Use the ALTER TABLE ADD PARTITION statement to reflect the partition on the existing Athena table.
D. Change the Firehose output format to Apache Parquet. Provide a custom S3 object YYYYMMDD prefix expression and specify a large buffer size.
For the existing data, create an AWS Glue extract, transform, and load (ETL) job. Configure the ETL job to combine small JSON files, convert the JSON files to large Parquet files, and add the YYYYMMDD prefix. Use the ALTER TABLE ADD PARTITION statement to reflect the partition on the existing Athena table.

Answer: D

Explanation:
Changing the Firehose output format to Apache Parquet reduces Amazon Athena query costs because Parquet is a columnar storage format, which is much more efficient for queries compared to row-based formats like JSON. Parquet helps reduce the amount of data scanned by Athena, thus lowering costs.
Configuring Firehose to use a large buffer size ensures fewer, larger files, which also improves query performance in Athena.
For the existing JSON data, using an AWS Glue ETL job to convert the files into Parquet and apply the necessary partitioning (e.g., YYYYMMDD) will help optimize future queries without needing to recreate the data pipeline. Using the ALTER TABLE ADD PARTITION statement in Athena will allow the existing table to reflect the new partitions.
This solution minimizes management effort by reconfiguring Amazon Kinesis Data Firehose and using AWS Glue for handling existing data, without requiring significant changes to the existing pipeline.
Running an Apache Spark job on Amazon EMR requires more operational management, as you would need to launch and manage ephemeral EMR clusters daily, which increases complexity.
Amazon Managed Service for Apache Flink introduces additional components (Kinesis Data Streams and Flink), which adds complexity and management overhead compared to using the existing Firehose and simply changing its output format.
Using Lambda for real-time transformation and combining that with a Glue ETL job introduces unnecessary complexity. Firehose already supports direct output to Parquet format, making the Lambda function redundant.

NEW QUESTION # 195
A company stores data from an application in an Amazon DynamoDB table that operates in provisioned capacity mode. The workloads of the application have predictable throughput load on a regular schedule. Every Monday, there is an immediate increase in activity early in the morning.
The application has very low usage during weekends.
The company must ensure that the application performs consistently during peak usage times.
Which solution will meet these requirements in the MOST cost-effective way?

A. Use AWS Application Auto Scaling to schedule higher provisioned capacity for peak usage times.
Schedule lower capacity during off-peak times.
B. Divide the table into two tables. Provision each table with half of the provisioned capacity of the original table. Spread queries evenly across both tables.
C. Increase the provisioned capacity to the maximum capacity that is currently present during peak load times.
D. Change the capacity mode from provisioned to on-demand. Configure the table to scale up and scale down based on the load on the table.

Answer: A

NEW QUESTION # 196
A data engineer develops an AWS Glue Apache Spark ETL job to perform transformations on a dataset. When the data engineer runs the job, the job returns an error that reads, "No space left on device." The data engineer needs to identify the source of the error and provide a solution.
Which combinations of steps will meet this requirement MOST cost-effectively? (Choose two.)

A. Enable the --write-shuffie-files-to-s3 job parameter. Use the salting technique.
B. Scale out the workers vertically to address data skewness.
C. Use error logs in Amazon CloudWatch to monitor data skew.
D. Use the Spark UI and AWS Glue metrics to monitor data skew in the Spark executors.
E. Scale out the number of workers horizontally to address data skewness.

Answer: A,D

Explanation:
Use the Spark UI and AWS Glueexposed Spark metrics to pinpoint where partitions are disproportionately large (data skew) and where spill files are filling executor disk.
Enable the --write-shuffle-files-to-s3 job parameter so shuffle spills go to S3 instead of running out of local disk, and apply a salting technique on skewed keys to spread data more evenly across partitions.

NEW QUESTION # 197
You as Data engineer might want to consider disabling auto-suspend for a warehouse if?

A. You require the warehouse to be available with no delay or lag time.
B. You have a heavy, steady workload for the warehouse.
C. You require the warehouse to be available with delay.
D. You have a low, fluctuating workload for the warehouse.

Answer: A,B

Explanation:
Explanation
Automating Warehouse Suspension
Data Engineer might want to consider disabling auto-suspend for a warehouse if:
He/She have a heavy, steady workload for the warehouse.
He/She require the warehouse to be available with no delay or lag time. Warehouse provisioning is generally very fast (e.g. 1 or 2 seconds); however, depending on the size of the warehouse and the availability of compute resources to provision, it can take longer.
If he/she chose to disable auto-suspend, He/she must carefully consider the costs associated with running a warehouse continually, even when the warehouse is not processing queries. The costs can be significant, especially for larger warehouses (X-Large, 2X-Large, etc.).
To disable auto-suspend, Engineer must explicitly select Never in the web interface, or specify 0 or NULL in SQL.

NEW QUESTION # 198
......

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